Major Outcomes With Personalized Dialysate TEMPerature: Pragmatic, Registry-Based, Cluster Randomized Controlled Trial
Phase: NA
Conditions: Kidney Diseases, End-Stage Kidney Disease, Hemodialysis
Sponsor: London Health Sciences Centre Research Institute OR Lawson Research Institute of St. Joseph's

 
 
  
 
 
 
Title: Major Outcomes With Personalized Dialysate TEMPerature: Pragmatic, Registry-
Based, Cluster Randomized Controlled Trial
Protocol Trial Registration: NCT02628366
Date: September 6, 2022

Table of Contents 
 
 
* The numbers below refer to the page numbers in the pdf document. 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
Original protocol ................................................................................................................... page 4
Final protocol ...................................................................................................................... page 33
Table of protocol changes ................................................................................................... page 97
Original statistical analysis plan  ........................................................................................ page 106
Correction to the statistical analysis plan  ........................................................................ page 126
Original substudy protocol .............................................................................................. page 127

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
 
 
 
 
 
MyTEMP 
 
Major important outcomes with personalized dialysate TEMPerature 
 
 
 
 
 
Principal Investigators 
Dr. Amit Garg, MD, PhD, FRCPC, FACP 
Director of the ICES Kidney Dialysis Transplantation Program 
London Health Sciences Centre 
800 Commissioners Road East, ELL-215 
London, ON N6A 5W9 
Phone: 519-685-8500 Ext 58502 
 
Dr. Christopher McIntyre MBBS, DM 
Director of the Lilibeth Caberto Kidney Clinical Research Unit  
London Health Sciences Centre 
800 Commissioners Road East, ELL-101 
London, ON  N6A 5W9 
Phone: 519-685-8500 Ext 58502 
 
Study Manager 
Ahmed Al-Jaishi, MSc, PhD (c) 
Epidemiologist 
London Health Sciences Centre 
800 Commissioners Road East, ELL-111 
London, ON N6A 5W9 
Phone: 519-685-8500 Ext 53573 
 

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
 
Trial Summary 
Title 
Major cardiovascular and other patient-important outcomes with 
personalized dialYsate TEMPerature (MyTEMP): A registry-based cluster 
randomized controlled trial 
Version & Date 
Version 1.0 completed on 08-Oct-2015 by Ahmed Al-Jaishi 
Motto 
“MY dialysis, MY temperature” 
Project Office and 
Contact 
Ahmed Al-Jaishi, Epidemiologist  
 
Kidney Clinical Research Unit, Room ELL-111 
London Health Sciences Centre 
800 Commissioners Road East 
London, ON  N6A 5W9 
Phone: 519-685-8500 Ext 53573 
Study Size 
75+ Hemodialysis Centres  (~ 7500 patients) 
Study Design 
Cluster randomized controlled trial 
Research Question  
Do centres randomized to provide temperature-reduced personalized hemodialysis 
have a different rate for the composite outcome of all-cause mortality and major 
cardiovascular events compared with centres that provide standard-temperature 
hemodialysis? 
Hypothesis 
There will be at least a 15% relative rate reduction in the composite outcome of all-
cause mortality and major cardiovascular events among centres that provide 
temperature-reduced personalized hemodialysis compared with centres that 
provide standard-temperature hemodialysis. 
Inclusion Criteria 
1) The medical director of the dialysis centre must provide informed consent and 
be willing adopt temperature-reduced personalized hemodialysis as their center 
protocol (if randomized to the intervention) or stay with a standard 36.5°C 
hemodialysis temperature protocol during the course of the trial (if randomized 
to the control group); and  
 
2) The centre must care for a minimum of 15 patients being treated with 
conventional in-centre hemodialysis. 
 
Study Intervention 
& Control 
Dialysis centres randomized to the intervention will provide temperature-reduced 
personalized hemodialysis. A nurse will set the temperature of the dialysate to 0.5-
0.9 °C below each patient’s body temperature (measured just before starting the 
dialysis treatment). The remaining dialysis centres (the control group) will provide 
usual care, which is standard dialysis using a fixed dialysate temperature of 36.5°C 
(the current practice for over 90% of patients in Ontario centres). 
Primary Outcome 
A composite of all-cause mortality or hospitalization for ischemic stroke, 
myocardial infarction, or coronary revascularization (coronary artery bypass 
grafting or percutaneous coronary intervention).  
Follow-up 
Centres will be followed for a period of two years after the intervention start date 
(01-April-2017 to 31-March-2019). 

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
 
Table of Contents 
Protocol Summary ........................................................................................................................................ 5 
1.0 
Background ....................................................................................................................................... 7 
2.0 
Rationale ........................................................................................................................................... 8 
2.1 
How does temperature-reduced hemodialysis prevent ischemic injury? ...................................... 8 
2.2 
Pilot Trial ...................................................................................................................................... 8 
2.3 
Meta-Analysis ............................................................................................................................... 8 
2.4 
The need to determine whether temperature-reduced personalized hemodialysis prevents major 
adverse cardiovascular events ................................................................................................................... 9 
3.0 
Plan ................................................................................................................................................... 9 
4.0 
Methods............................................................................................................................................. 9 
4.1 
Primary Research Question ........................................................................................................... 9 
4.2 
Hypothesis..................................................................................................................................... 9 
4.3 
Trial Design ................................................................................................................................ 10 
4.4 
Justification of Study Design: ..................................................................................................... 10 
4.5 
Inclusion/Exclusion Criteria ....................................................................................................... 10 
4.6 
Participating Dialysis Centres: .................................................................................................... 11 
4.7 
Intervention and Control ............................................................................................................. 11 
4.8 
Data Collection and Follow-Up .................................................................................................. 12 
4.8.1 
Additional Data Collection ................................................................................................. 12 
4.8.1.1 
Measuring Patient’s Temperature ....................................................................................... 12 
4.8.1.2 
Measure Blood Pressure ...................................................................................................... 12 
4.9 
Outcomes .................................................................................................................................... 12 
4.9.1 
Primary Outcome ................................................................................................................ 12 
4.9.2 
Secondary Outcomes........................................................................................................... 13 
4.9.3 
Economic Outcomes ........................................................................................................... 13 
4.10 
Randomization ............................................................................................................................ 14 
4.11 
Blinding....................................................................................................................................... 14 
4.12 
Statistical Analysis .................................................................................................................. 15 
4.12.1 
Analysis of Primary Outcome ............................................................................................. 15 

MY TEMP Trial Protocol 
 
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4.12.2 
Economic Analysis ............................................................................................................. 15 
4.12.3 
Subgroup Analysis .............................................................................................................. 15 
4.13 
Loss to follow-up ........................................................................................................................ 16 
4.13.1 
Patients Switching Centres .................................................................................................. 16 
4.13.2 
Inpatient Hemodialysis Sessions ......................................................................................... 16 
4.14 
Missing Data ............................................................................................................................... 16 
4.15 
Sample Size ................................................................................................................................. 16 
4.16 
Summary of Trial Design and Method ....................................................................................... 17 
5.0 
Ethical Standards ............................................................................................................................ 17 
5.1 
Ethical Considerations ................................................................................................................. 17 
5.2 
Access to Data ............................................................................................................................. 17 
5.3 
Data Destruction ......................................................................................................................... 18 
5.4 
Protecting Privacy ....................................................................................................................... 18 
6.0 
Study Timeline and Milestones ....................................................................................................... 19 
7.0 
References ....................................................................................................................................... 20 
8.0 
Appendices ...................................................................................................................................... 25 
Appendix 1: Patient Temperature and setting of the dialysate temperature for the intervention group. 
Centres that have dialysis machines able to change by increments of 0.1°C ...................................... 25 
Appendix 2: Patient Temperature and setting of the dialysate temperature for the intervention group. 
Centres that have dialysis machines able to change by increments of 0.5°C ...................................... 26 
Appendix 3: Common data sources used for population-based studies .............................................. 27 
Appendix 4: Primary outcome codes and performance measures ...................................................... 28 
Appendix 5: Full details of economic analysis ................................................................................... 29 
 
 
 

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Version Date: 01-Feb-2017 
 
 
Protocol Summary 
Background: For patients with kidney failure (23,000 in Canada, >2 million worldwide), 
hemodialysis provides a life-saving treatment option; however, mortality remains extremely 
high—30 times higher than age-matched controls from general population. Cardiovascular 
disease is the leading cause of morbidity and death. Standard therapies such as statins and anti-
platelet drugs, which are effective for treating cardiovascular disease in the general population, 
have been largely ineffective in hemodialysis patients. We need to re-think our strategies to 
prevent cardiovascular events in this vulnerable population.  
In this trial, we will test if temperature-reduced personalized hemodialysis reduces the composite 
rate of death and hospitalizations for major cardiovascular events. Preliminary results are 
promising: in a pilot trial, we showed that lowering the dialysate temperature to 0.5 C below a 
patient’s core body temperature (temperature-reduced personalized hemodialysis) effectively 
reduced progressive brain and cardiac injury. However, this was a small trial (n = 73 patients) 
and no study to date has investigated long-term effects of using temperature-reduced 
personalized hemodialysis. 
Research Question: Do centres randomized to provide temperature-reduced personalized 
hemodialysis have a different rate for the composite outcome of all-cause mortality and major 
cardiovascular events* compared with centres that provide standard-temperature hemodialysis? 
*Hospital encounters for ischemic stroke, myocardial infarction, or receipt of coronary 
revascularization (CABG/PCI). 
Hypothesis: There will be at least a 15% relative rate reduction in the composite outcome of 
all-cause mortality and major cardiovascular events among centres that provide temperature-
reduced personalized hemodialysis compared with centres that provide standard-temperature 
hemodialysis. 
Methods: We will conduct a registry-based, cluster randomized controlled trial. All 
hemodialysis centres (clusters) in Ontario that care for more than 15 patients are eligible to 
participate, and all renal programs overseeing these centres have already agreed to do so. We 
strategically designed this study to be inclusive of all facilities (including community hospitals 
that are normally excluded from trials) and patients treated within these facilities. Participating 
centres will be given resources and strategies to adhere to the allocated temperature therapy 
during the trial.  
Baseline characteristics and follow-up for outcomes will be ascertained through healthcare 
administrative databases housed at the Institute for Clinical Evaluative Sciences (ICES), 
supplemented with primary data abstracted from patient dialysis run sheets. 
Choice of Study Design:  Hemodialysis patients receive all their treatments at the same centre, 
making this population ideal for a cluster-level intervention, where all patients in the cluster 
receive the randomly assigned therapy. There are two reasons why we chose this design: 
1. Enhanced uptake of MyTEMP within each dialysis centre. From an administrative 
perspective, interventions such as MyTEMP are naturally applied at the level of the 
dialysis centre, where trained nurses follow a standard protocol for all patients under 
their care (e.g. for centres randomized to the intervention, nurses will measure each 

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
patient’s temperature and set the dialysate temperature to 0.5 °C below the patient’s body 
temperature). 
2. Minimized cross-group contamination. One limitation of patient-level randomization is 
the potential for cross-group contamination. For example, the same nurse cares for 
different patients in a dialysis centre and if a nurse observes that a patient with 
temperature-reduced personalized dialysis has fewer intra-dialytic hypotensive episodes, 
they may decide to apply this intervention to a patient randomized to the control group, 
negating the randomization and contaminating the control group. Cross-group 
contamination and treatment-effect dilution are less likely to occur in this setting with 
centre-level randomization. 
Feasibility/Scalability: Temperature-reduced personalized hemodialysis is safe, well tolerated, 
and simple—requiring no additional resources to apply in hemodialysis centres. In the pilot 
trial (n=73), we found 100% adherence to the intervention achieving the target 1°C separation 
in mean dialysate temperature between the treatment and control group.  
Public Health Impact: This simple and safe intervention warrants broad testing given its potential 
to reduce cardiovascular diseases, costs, and save lives in a vulnerable segment of the population  
Each year, more than 2,000 hemodialysis patients in Ontario die or are admitted to hospital for 
a major cardiovascular event. If this intervention is effective, even a modest 15% relative-risk 
reduction would translate into 300 fewer deaths or cardiovascular events each year (in Ontario 
alone!). 
Health System Economic Impact: Dialysis patients have the highest per-person healthcare costs 
compared with the 16 of the most common chronic diseases. The total healthcare cost for dialysis 
patients exceeds $1.6 billion per year in Ontario, with ~80% of costs attributed to 
hospitalizations. A conservative estimate is that our intervention could save the Ontario 
healthcare system up to $1.5 million each year simply by reducing cardiovascular-related 
hospitalizations.  
 
 
 

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
1.0 
Background 
Cardiovascular disease in hemodialysis patients: For those with kidney failure (~2 million 
worldwide and 23,000 in Canada), hemodialysis provides a life-saving treatment option; 
however a tragic 20-40% of patients die within one year of starting dialysis, and 
cardiovascular disease is the leading cause of death.1–3 Therapies such as statins (which help 
prevent major adverse cardiovascular events in many segments of the population) have proven 
largely ineffective in patients with advanced kidney disease.4 This led the renal community to 
consider other causes of cardiovascular disease that are unique to patients receiving long-term 
dialysis.  
Dr. Chris McIntyre (co-PI) has conducted a series of pivotal studies showing that the 
hemodialysis procedure itself directly damages the heart and brain through repeated episodes 
of hypotension and subclinical ischemia.5–9 This paradigm-shifting concept took the 
nephrology community by surprise, but is now broadly accepted. We and others are altering 
various aspects of the hemodialysis procedure to determine which paramaters best prevent 
hemodialysis-induced ischemic injury. In small randomized controlled trials, we have shown 
that reducing the temperature of a hemodialysis treatment may prevent intra-dialytic 
hypotension and mitigate injury to the heart and brain (details provided in this protocol).7,9–11  
We now propose to conduct a large-scale evaluation to determine whether temperature-
reduced personalized hemodialysis (compared to standard-temperature dialysis) reduces the 
risk of major cardiovascular events (myocardial infarction, stroke, coronary revascularization) 
and death. We propose an efficient Ontario-wide cluster-randomized registry trial that will 
involve 75+ hemodialysis centres (~7500 hemodialysis patients). The name of this trial is 
MyTEMP. 
How does hemodialysis cause ischemic injury? 
When fluid is removed from the body during 
hemodialysis (~1-3 L per session), blood 
pressure often drops by 20-30 mmHg.12,13 These 
intra-dialytic hypotensive episodes occur in up to 
50% of dialysis sessions. Not only are these 
extremely unpleasant for patients, but 
physiologically, these episodes cause repeated 
ischemic injury to the heart and brain.13–15 Using 
magnetic resonance imaging, Dr. McIntyre 
demonstrated that hemodialysis causes direct 
cardiac injury, myocardial stunning, and 
reversible left ventricular regional wall motion 
defects.7,9 Over time, the cumulative effect of 
these subclinical ischemic insults can lead to 
myocardial infarction, stroke, and even 
death.6,12,16   
 
In a cohort of 1409 hemodialysis patients, a greater frequency of intra-dialytic hypotension was 
associated with an incrementally greater risk of death in both unadjusted and adjusted analyses 
Figure 1: Association between intra-dialytic hypotension and 
mortality. Analyses were adjusted for demographics, clinical 
factors, and medications. Error bars indicate 95% CI.13 

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
(Figure 1).13 Patients who had the lowest nadir blood pressure during dialysis also had the 
highest risk of death.  
 
One strategy to maintain stable blood pressure and tissue oxygenation during hemodialysis is to use 
temperature-reduced personalized hemodialysis. 
2.0 
Rationale 
2.1 
How does temperature-reduced hemodialysis prevent ischemic injury? 
Approximately 120 L of dialysate is used during a hemodialysis treatment. This dialysate 
circulates through the dialysis machine at a set temperature, where a semi-permeable membrane 
separates the dialysate from a patient’s blood which is also circulating through the machine. A 
higher dialysate temperature means a patient will have a higher core body temperature during 
their dialysis treatment.  
A nurse sets the dialysis temperature at the beginning of each hemodialysis treatment (and it 
remains constant during that treatment). The current practice in Ontario is to set the temperature 
of the dialysate to 36.5ºC.17 However, this practice is largely based on clinical tradition, not 
evidence. There is now strong evidence that a cooler dialysate can improve patients’ 
hemodynamic response to dialysis and reduce the frequency of intra-dialytic hypotension.18–25  
Human body temperature is maintained within a narrow range, where 0.3°C to 0.8°C separates 
the thresholds for skin vasodilation and shivering.17 Exposure to a warm dialysate warms a 
patient’s core temperature, which causes the skin to vasodilate, and this may increase the risk of 
hypotension from decreased peripheral vascular resistance. However, a cooler dialysate increases 
peripheral vascular resistance, improves cardiac output, and alters the level of vasoactive 
peptides, all which decrease the risk of intra-dialytic hypotension.18–25 
2.2 
Pilot Trial 
In a small parallel-group randomized controlled trial (73 patients), we examined the effect of 
lowering the dialysate temperature to 0.5°C below each patient’s body temperature, which was 
measured at the beginning of their dialysis treatment (temperature-reduced personalized 
dialysis).9 For example, a patient who started dialysis with a body temperature of 36.2°C had 
their dialysis machine temperature set to 35.7°C, while another patient who started dialysis with 
a body temperature of 35.9°C had their dialysis temperature set to 35.4°C. Compared with 
standard-temperature dialysis, patients who received temperature-reduced personalized dialysis 
had improved dialysis tolerability and improved blood pressure stability; fewer patients stopped 
their treatment session prematurely and adherence to the intervention was 100% with no adverse 
events (no patients complained about feeling uncomfortably cold on dialysis). Most importantly, 
this intervention protected patients from progressive brain and cardiac injury as observed on 
magnetic resonance imaging over one year.7,9  
2.3 
Meta-Analysis 
Following our pilot trial, we updated our systematic review and identified 26 randomized 
controlled trials (484 patients in total) that compared temperature-reduced and standard-
temperature hemodialysis. This meta-analysis was led by Dr. Mustafa (co-investigator) and is 
registered at prospero.com (CRD42011001104). The manuscript is published in Clinical Journal 

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Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
of American Society of Nephrology.26 Compared with standard-temperature dialysis (36-38.5°C), 
temperature-reduced hemodialysis (34-35.5°C) lowered the rate of intra-dialytic hypotension by 
70% (95% CI: 49-89%) and increased the intra-dialytic mean arterial pressure by 12 mmHg 
(95% CI: 8 to 16 mmHg).   
2.4 
The need to determine whether temperature-reduced personalized hemodialysis 
prevents major adverse cardiovascular events 
Evidence from small clinical trials (6-128 patients) consistently shows that temperature-reduced 
personalized hemodialysis is a simple and safe intervention that effectively reduces intra-dialytic 
hypotension and protects the heart and brain from hemodialysis-induced ischemic injury. As 
well, one observational study showed that patients who received cooler dialysis had a reduced 
risk of death and cardiovascular events.27  However, to influence practice, we need at least one 
large, high-quality multi-centre randomized controlled trial to determine whether this 
intervention reduces the risk major cardiovascular events and death. While this is the next logical 
trial to do, current trials of temperature-reduced hemodialysis registered on clinicaltrials.gov 
have sample sizes of 100 patients or less. Thus, we propose to efficiently conduct a large 
Ontario-wide cluster-randomized registry trial to determine if temperature-reduced 
personalized hemodialysis compared to standard-temperature hemodialysis reduces the rate of 
death and major cardiovascular events.  
3.0 
Plan 
We are planning a large trial to determine if using temperature-reduced personalized 
hemodialysis reduces rates of major cardiovascular events and death.  
In this intervention (MyTEMP), the dialysate temperature will be set to 0.5-0.9 C below each 
patient’s body temperature, where the body temperature is measured just prior to starting a 
hemodialysis treatment. We are using this personalized approach because it adjusts for individual 
and diurnal variations. This differs from a fixed-colder temperature approach, where all patients, 
for example, receive a dialysis temperature of 35.5°C irrespective of their body temperature at 
the beginning of a dialysis treatment. In our pilot trial, a personalized-temperature approach 
achieved all the benefits of cooler dialysis without any patient concerns about feeling cold (no 
patient stopped their dialysis session early). This intervention is easily applied on standard 
dialysis machines, including all machines in Ontario.  
4.0 
Methods 
4.1 
Primary Research Question 
Do centres randomized to provide temperature-reduced personalized hemodialysis have a 
different rate for the composite outcome of all-cause mortality and major cardiovascular events 
compared with centres that provide standard-temperature hemodialysis? 
4.2 
Hypothesis 
There will be at least a 15% relative rate reduction in the composite outcome of all-cause 
mortality and major cardiovascular events among centres that provide temperature-reduced 
personalized hemodialysis compared with centres that provide standard-temperature 
hemodialysis. 

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
4.3 
Trial Design 
The MyTEMP trial is an Ontario-wide registry-based, cluster randomized controlled trial. 
Registry: routinely collected healthcare data that are housed at ICES. 
Cluster: the unit of randomization and analysis (dialysis centres). 
 
Using a cluster-design, we will randomize hemodialysis centres (rather than individual patients), 
to ensure that all patients in each centre are studied, including subgroups of patients who are 
normally excluded from trials.  
4.4 
Justification of Study Design:  
Hemodialysis patients receive all their treatments at the same centre, making this population 
ideal for a cluster-level intervention, where all patients in the cluster receive the randomly 
assigned therapy. There are two reasons we chose this design:  
1. Enhanced uptake of MyTEMP intervention within each dialysis centre: From an 
administrative perspective, interventions such as MyTEMP are naturally applied at the 
level of the dialysis centre, where trained nurses follow a standard protocol for all 
patients under their care (e.g. for centres randomized to the intervention, nurses will 
measure each patient’s temperature and set the dialysate temperature to 0.5-0.9 °C below 
the patient’s body temperature). 
2. Minimize cross-group contamination: One limitation of patient-level randomization is the 
potential for cross-group contamination. For example, the same nurse cares for different 
patients in a dialysis centre and if the nurse observed that a patient with temperature-
reduced personalized dialysis had fewer intra-dialytic hypotensive episodes, they may 
decide to apply this intervention to a patient randomized to the control group, negating 
the randomization and contaminating the control group. Cross-group contamination and 
treatment-effect dilution are less likely to occur in cluster-level interventions. 
 
4.5 
Inclusion/Exclusion Criteria 
This pragmatic trial has only two inclusion criteria:  
 
1) The medical director of the dialysis centre must be willing to adopt temperature-reduced 
personalized hemodialysis as the routine protocol for their centre (if the centre is randomized to 
the intervention) or stay with the standard 36.5°C hemodialysis temperature protocol during the 
course of the trial (if the centre is randomized to the control group); and  
 
2) The centre must care for a minimum of 15 patients being treated with conventional in-centre 
hemodialysis.  
 
These broad inclusion criteria mean our study will include many types of patients who are 
normally excluded from clinical trials. For example, we analyzed historic data from the same 
ICES healthcare databases that will used for this trial. In our preliminary analysis of 7,446 
hemodialysis patients in Ontario who would comprise our trial population, our study would 
include 4,989 patients with multiple comorbidities, and 673 patients living in rural or remote 
locations. The Ontario data below, while not exhaustive, provides a simple overview of patient 
diversity and high expected levels of clinically significant co-morbidities among patients that 
will be included in our trial.  

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Version Date: 01-Feb-2017 
 
 
 
    Patients living in rural or remote locations 
673 (9%) 
    Women 
3019 (42%) 
    Lowest income quintile 
2049 (28%) 
    Patients older than 65 years 
4468 (60%) 
    Patients older than 80 years 
1580 (21%) 
    Patients with multiple (>4) comorbidities 
4989 (67%) 
       Coronary artery disease 
4617 (62%) 
       Congestive heart failure 
3797 (51%) 
       Diabetes 
4728 (63%) 
       Dementia 
1117 (15%) 
 
As approved by Research Ethics Review Board at Western University, medical directors will 
provide approval for the participation of their dialysis centre, and the standard protocol used in 
the hemodialysis center for the trial will be the randomly allocated dialysis temperature treatment 
(see the Ethic Section 5.0 of this protocol for further details). In routine care, any given patient or 
their nephrologist have the option of pursuing a dialysis temperature other than the routine 
temperature protocol for that centre. The same will be true during the course of this trial; we 
expect such protocol deviations from the randomly allocated temperature will be uncommon. By 
including patients from a variety of medical, ethnic, geographic, and socioeconomic 
backgrounds, the results of our trial should be broadly generalizable. 
4.6 
Participating Dialysis Centres:  
Ontario has 97 hemodialysis centres that care for ~7800 patients. We have already recruited 80+ 
dialysis centres that meet the eligibility requirements for this trial (~7,000 dialysis patients). 
Thirteen centres were excluded because they care for fewer than 15 patients (less than 225 
patients in Ontario) or the centre director did not agree to randomization. 
4.7 
Intervention and Control  
Dialysis centres randomized to the intervention will provide temperature-reduced personalized 
hemodialysis. A nurse will set the temperature of the dialysate to 0.5-0.9°C below each 
patient’s body temperature measured just before starting the dialysis treatment (Appendix 1). 
We are aware that some dialysis machines (e.g. Fresenius 5008) are only able to modify 
dialysate temperature by 0.5°C increments. For centres with those machines, the temperature 
should be lowered by a minimum of 0.5°C and a maximum of 0.9°C (Appendix 2). The 
remaining dialysis centres (the control group) will provide usual care, which is standard dialysis 
using a fixed dialysate temperature of 36.5°C (the current practice for over 98% of patients in 
Ontario centres).  
 
All participating centres have agreed to adhere to their randomly allocated temperature therapy 
for two years after randomization. We expect that this intervention will be easily deployed. 
Nonetheless, to maximize intervention uptake, we performed semi-structured interviews of 18 
health professionals (nephrologists, nurses, and technicians) and 3 dialysis patients to identify 
any behaviours that may hinder or influence intervention uptake. We used principles of theory-
based knowledge translation, and respondents were purposely sampled from a wide range of 
practice settings across Ontario.28–30 The results from these interviews informed our final 
implementation strategy for this trial, which is provided to the centre PI for the renal program. 

MY TEMP Trial Protocol 
 
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Version Date: 01-Feb-2017 
 
 
Dr. Jeremy Grimshaw (Canada Research Chair, Health Knowledge Transfer and Uptake) at the 
Ottawa Provincial Knowledge Translation and Exchange Network helped develop and guide the 
implementation plan for our intervention (both during and after the trial).   
4.8 
Data Collection and Follow-Up 
All data, including baseline characteristics, study outcomes, other patient-selected outcomes, and 
economic data will be obtained from data sources housed at the Institute for Clinical Evaluative 
Sciences (ICES).  Common data sources used at ICES are described in Appendix 3. 
4.8.1 Additional Data Collection  
We will ask each dialysis centre (both intervention and control centres) to send additional data 
from 15 random patients (no patient identifiers will be requested) including pre-dialysis patient 
temperatures, pre-dialysis systolic and diastolic blood pressure, the temperature of the dialysate, 
and the nadir systolic with its accompanying diastolic blood pressure. To ascertain baseline 
information, we will request this data be sent monthly starting as early as October 2015 until 
March 2017.  
 
Beginning April 2017, we will ask each centre to send weekly reports for the first month of the 
trial, bi-weekly for the second month, and monthly thereafter. All of these data elements are 
available in the patient's dialysis run sheet as part of routine care. These data will allow us to 
assess adherence to the treatment allocation, and assess differences in average dialysis 
temperature and blood pressure between the two groups of dialysis centres.  
 
4.8.1.1 
Measuring Patient’s Temperature 
In Canada and abroad, all patients have their vital signs (including temperature) measured before 
initiating a hemodialysis session. In this pragmatic trial, participating centres will continue with 
their usual method of temperature measurement (e.g. oral or tympanic). 
4.8.1.2 
Measure Blood Pressure 
Blood pressure measurements should be completed before starting dialysis and while the patient 
is sitting down using standard centre equipment. For instances where the patient has multiple 
measurements before dialysis, please record the first blood pressure reading.  
Centres can enter their monthly data using the following link: 
https://mytemp.lawsonresearch.ca/account/login.aspx 
 
User Name: 
Password: 
4.9 
Outcomes 
4.9.1 Primary Outcome  
Composite outcome of all-cause mortality or hospitalization for ischemic stroke, myocardial 
infarction, or coronary revascularization (coronary artery bypass grafting or percutaneous 
coronary intervention).  
 

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Version Date: 01-Feb-2017 
 
 
Datasets housed at ICES contain vital statistics and reasons for hospital admission for all 
Ontarians. Hospital encounters for cardiovascular events will be ascertained using ICD-10 codes 
recorded in CIHI-DAD. These codes have high accuracy (Appendix 4); demonstrating high 
sensitivity and specificity when compared to manual chart review. We will also use the 
registered persons database (RPDB) to ascertain all-cause mortality (recorded with 99% 
accuracy).31 
 
We can reliably track the major outcomes in the MyTEMP trial (as we have done in other 
published studies) with validated algorithms applied to the large healthcare databases held at 
ICES.31–34   
4.9.2 Secondary Outcomes 
 
We will estimate the relative rate reduction for each component of the primary composite 
outcome.  
 
Other Important Outcomes 
 
In consultation with patient representatives, we discussed several patient-important outcomes 
that: 1) may be responsive to our intervention, and 2) can be reliably assessed using the 
administrative data sources. Patient representatives prioritized four other outcomes for this trial:  
 
Amputation: Patients on HD, especially those with diabetes, have a high incident rate of 
amputation with 3% of patients having a lower or upper limb amputation each year. This high 
rate of amputations is associated with cardiovascular risk factors and likely linked to vascular 
injury caused by HD-induced ischemia, which complicates pre-existing arterial disease and 
diabetes related injury. We will compare the amputation rate for the two groups for all-
amputations (excluding digit amputation) and separately for upper and lower limb amputations.  
 
Major fall and Fractures: Many patients on dialysis are frail and prone to falling, which may 
also predispose them to suffer a fracture. Bone fractures are an important outcome and can result 
in morbidity, high economic, costs, and mortality. The three-year incidence of falls requiring a 
hospitalization ranges from 3 to 12% for patients on dialysis, with elderly females being at the 
highest risk.35 Fractures (hip, forearm, pelvis, or proximal humerus) are also common occurring 
in nearly 5% of patients each year.35  
 
Intra-dialytic hypotension might increase the rate and severity of falls after a HD session leading 
to additional fractures requiring hospitalizations. We will estimate the rate of falls and fractures 
for both groups. 
 
The ability to live independently: Temperature-reduced personalized HD may help patients 
maintain health, cognition and mobility through direct neuro-protection.  
 
Intensity use of blood pressure medications: This outcome will be restricted to patients covered 
by the Ontario Drug Benefit (ODB) Program; this program covers most of the cost of 4,400 
prescription drug products, some nutrition products and some diabetic testing agents. Individuals 
eligible for ODB include [i] all patients 65 years and older; [ii] patients living long-term care or a 

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home for special care; [iii] patients enrolled in the Home Care Program; [iv] patients registered 
in the Trillium drug plan; and [v] patients on social assistance through Ontario Works or the 
Ontario Disability Support Program.  
 
We will include the following blood pressure prescription medication classes:  
• Diuretics;  
• renin-angiotensin system blockers (angiotensin-converting enzyme inhibitors and 
angiotensin receptor blockers);  
• β-blockers (selective, nonselective, and α-β-blocker agents);  
• calcium channel blockers (nondihydropyridines and dihydropyridines);  
• centrally acting antiadrenergic agents; and  
• other (i.e., peripheral acting antiadrenergic agents or vasodilators). 
 
We will calculate the daily intensity of blood pressure medication by dividing the total defined 
daily dose (DDD) by the number of days under observation for each patient. Each daily dose will 
be converted to a standardized daily dose based on the corresponding DDD proposed by the 
World Health Organization International Working Group for Drug Statistics Methodology.36 The 
intensity will be classified in three categories: Low (0 to <0.2 DDD), moderate (0.2-2.5 DDD), 
or high (>2.5 DDD).37 We will estimate the intensity of BP medication for ODB eligible patients 
for both groups. 
4.9.3 Economic Outcomes  
ICES contains information on all costs borne by the Ontario health care system. We will 
compare our two groups on the average healthcare cost per patient per month alive (to account 
for differences in mortality), which will include the hospitalization costs for any major 
cardiovascular events. 
4.10 
Randomization  
On a single date, we will randomize all participating HD centres to the intervention or control 
group in a 1:1 ratio using computer-generated randomization. We are using the method of 
covariate-constrained randomization, where we will run a large number of random allocations 
from all possible allocations. We will select random allocations where a set of important 
covariates are well balanced — a scheme was considered to have good balance if the between-
group standardized differences on the constrained variables were within 10% caliper. This 
standardized difference translates to group means/proportions for each trial arm having less than 
~8% non-overlap in the two populations. At this stage, we will select one overall randomization 
scheme from the list of all "good" allocation schemes.38 This method ensures the two groups are 
comparable at baseline on important measured prognostic factors and has been shown to produce 
un-biased results.39,40 It should be noted, because of the way administrative data are captured, the 
two groups will be compared on baseline prognostic factors for prevalent HD patients in the two-
years prior to trial start (i.e. April 01, 2017). 
4.11 
Blinding 
Because of the nature of MyTEMP trial, it is not feasible to blind the patients, nurses, or 
nephrologists who care for these patients to the intervention assignment. However, outcome 
adjudication (e.g. myocardial infarction) at/within hospital admission will be diagnosed by 
physicians who are blinded to the MyTEMP intervention.  

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4.12 
Statistical Analysis 
4.12.1 Analysis of Primary Outcome 
The statistical analysis is being supervised by Dr. Allan Donner, a Professor of Biostatistics at 
Western University and a world authority on the statistics of cluster-randomized trials. Trial 
centres will be analyzed according to their random allocation (i.e. intention to treat). We will 
assume a closed cohort, where only patients that are on dialysis on April 31st, 2017 will be 
included in our analyses (we will assume an open cohort in additional analyses). We will fit a 
Poisson regression model to the individual (i.e. patient) level data, including potential 
confounders, but without the intervention variable. We will calculate the residual (the difference 
between the observed and fitted values using the adjusted model without intervention status) for 
each patient. The individual-level residual includes the effects of random variation and 
explanatory factors not included in the model, one of which is the intervention.41  
 
The analysis is unaffected by whether the data are analyzed as one observation per individual or 
as one summary observation per combination of covariates.41 Therefore, we will aggregate the 
individual-level residuals, rather than the raw incident rate, for each cluster using inverse 
probability weighting using centre’s sample size. Using the calculated aggregate cluster 
residuals, we will fit an ordinary Poisson model including an indicator variable for the 
intervention. All analyses will be conducted using SAS version 9.4 (SAS Institute Inc.).  
4.12.2 Economic Analysis  
There is no incremental cost for administering temperature-reduced personalized hemodialysis 
compared with standard-temperature hemodialysis. While there is an initial one-time education 
cost for the nursing staff in the intervention group (nurses will require ~15 minutes of training to 
deliver personalized dialysis temperature for all subsequent hemodialysis treatments), the 
educational package for the MyTEMP intervention will be bundled into standard education 
processes already available to nurses at each participating centre. Most programs have clinical 
educators that update dialysis nurses on new policies and procedures. This education is often 
efficiently delivered to groups of nurses at one time.  
 
If our intervention is successful, a reduced annual rate of cardiovascular-related hospitalizations 
may result in cost savings to the healthcare system. If (as anticipated) costs are the same or lower 
in the intervention group (and outcomes better) relative to the control group (i.e. dominant 
strategy), a cost-effectiveness analysis will not be necessary. 
 
We are planning to conduct a cost-analysis from a healthcare system perspective. We will use 
data from ICES on total (and hospitalization only) resource consumption to estimate the average 
healthcare cost per patient per month alive (to address differences in mortality) accounting for 
within-centre clustering. Full details of the economic analysis are provided in Appendix 5. The 
economic analysis will be supervised by Dr. Walter Wodchis, Associate Professor of Health 
Economics and an authority on healthcare costing studies using ICES datasets. 
4.12.3 Subgroup Analysis 
We have not pre-specified any subgroup analyses. As subgroup analyses often have inadequate 
statistical power for modest interactions, we will simply perform exploratory subgroup analyses 
in this trial looking at rates of our composite outcomes for: 1) incident (starting dialysis <1 

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month) and prevalent patients (on dialysis for longer than one month); and 2) Previous History of 
cardiovascular disease. 
4.13 
Loss to follow-up  
We expect that less than 7% of patients during the follow-up period will transfer to home 
peritoneal dialysis or receive a kidney transplant. In the primary analysis, patients will be 
followed after these transitions; however, in additional analyses we will censor the observation 
period at the time of these transitions. 
 
Using healthcare administrative databases, we expect little loss to follow-up (< 0.2% per year 
who emigrates from Ontario).  
4.13.1 Patients Switching Centres 
We expect that a proportion of patients will switch centres. Some patients will likely switch from 
a centre on the MyTEMP intervention to a centre in the control group (or vice versa). For these 
instances, the patient temperature prescription should follow the new centre’s protocol. That is, if 
a patient switches to a centre in the control group, their dialysate temperature prescription should 
change to a fixed temperature of 36.5 °C. 
4.13.2 Inpatient Hemodialysis Sessions 
Patients hospitalized will use the hosting hospital’s standard dialysate temperature protocol whilst 
in hospital. In most cases, physicians will likely deviate back to standard temperature of 36.5 °C. 
4.14 
Missing Data 
Using healthcare administrative databases, we will have near-complete data for all study 
variables.  We will use recommended methods to investigate missing data, including a model-
based multiple imputation technique.42 We will also conduct sensitivity analyses to investigate 
whether conclusions are sensitive to assumptions about the missing-data mechanism. 
4.15 
Sample Size 
Based on 2013 ICES data, the rate of our primary composite outcome was 0.30 events per 
person-year with an average dialysis centre size of 90 patients (range 16 to 325). To detect a 10% 
relative-rate reduction between our intervention and control group with 80% power (two-tailed 
alpha 0.05 and coefficient of variation of 0.07) over a follow-up of 2 years, we require 35 
dialysis centres per group. However, based on the strong advice of our methods experts, we have 
recruited all eligible 80+ dialysis centres in Ontario for three reasons: 1) to minimize the chance 
baseline imbalance between groups (which could bias our trial results), 2) to improve the 
generalizability of the results, and 3) to increase the statistical power for analyzing components 
of the primary outcome. The Table below shows the required number of clusters needed per 
group for various scenarios. 
 
  
 
Incidence rate of the primary composite outcome 
(per person-year) 
 
 
0.20 
0.25 
0.30 
0.35 
10% 
49 
41 
35 
31 

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Minimum detectable 
relative-rate 
reduction  
15% 
21 
18 
15 
14 
20% 
12 
10 
9 
8 
Assumptions (as observed in 2013 ICES data): Mean cluster size=90 patients; Coefficient of variation=0.07; 
α=0.05; β=0.2 (power=0.8). Based on these assumptions, we have a sufficient number of clusters to detect a 
statistically significant difference for all of the above scenarios, except the one shaded cell. The coefficient of 
variation is defined as the ratio of the standard deviation to the mean – it shows the extent of variability in relation to 
the mean of the population. 
4.16 
Summary of Trial Design and Method 
We will conduct a trial of 80+ dialysis centres (~7000 patients) across Ontario. We will 
randomize half of the centres to use temperature-reduced personalized hemodialysis (0.5 ºC 
below the patient’s body temperature). The other centres will use fluid at the standard 
temperature. We will follow all patients for at least 2 years. At the end of the trial, we will 
compare rate of the primary outcomes between centres randomized to the intervention and 
control using Poisson regression. 
5.0 
Ethical Standards 
5.1 
Ethical Considerations 
In 2001, Canada's three federal research agencies, the Canadian Institutes for Health Research 
(CIHR), the Natural Sciences and Engineering Research Council (NSERC), and the Social 
Sciences and Humanities Research Council (SSHRC), jointly developed the Tri-Council Policy 
Statement (TCPS). Endorsed by the Government of Canada, the mandate was to promote the 
ethical conduct of research involving human participants.  
 
Cluster randomized trials have unique ethical challenges. Consent to enter this study can be 
theoretically obtained at multiple levels (e.g. patient, physician, or HD medical director). 
MyTEMP met the necessary criteria for altered patient consent as outlined in the Tri-Council 
Policy Statement for the following reasons: (i) the research poses a clear benefit to society and 
unlikely to adversely affect patient welfare; (ii) the intervention was considered to be of minimal 
risk to patients (similar to a quality-control measure that could be implemented by a dialysis 
centre director); (iii) an informed consent model is impossible and impracticable given our 
research design and resources; and (iv) there is a plan to provide a debriefing which also offers 
patients the possibility of refusing the intervention.43  
 
The medical directors of the dialysis centres (see inclusion criteria) will provide the overall 
approval for their HD centre(s) to participate in the MyTEMP trial.  We also have approval to 
obtain de-identified information on all patients in each cluster through administrative data sets 
held at the Institute for Clinical and Evaluative Sciences (ICES), which has special status with 
the privacy commissioner of Ontario. 
5.2 
Access to Data 
All baseline and outcome data will be retrieved from routinely collected administrative 
healthcare data housed at ICES. ICES is a prescribed entity for the purposes of section 45 
Ontario's Personal Health Information Privacy Act (PHIPA). This means that health information 
custodians — like physicians, hospitals or long-term care homes — are permitted to disclose 

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personal health information about their patients to ICES without consent. This information is for 
statistical analysis in order to evaluate and monitor aspects of the health system. ICES may also 
use personal health information under the authority of PHIPA’s section 44 for approved research 
projects. Data custodians outside the health sector may disclose personal information to ICES for 
specified use under the authority of FIPPA or other data-governing statutes. This project has 
already been approved by the ICES Privacy Office to access to the entire provincial repository of 
75 linked databases with information on all hemodialysis patients in Ontario (~7,500 patients). 
ICES data provides the flexibility to link individual records across a large breadth of data. 
5.3 
Data Destruction 
Any data generated by ICES as part of the proposed studies, will be destroyed within 8 years of 
study completion (expected date of study completion is March 31st, 2019, expected date of 
destruction is March 31st, 2027). Study data specified for destruction will be destroyed according 
to ICES standard operating procedures, which include document shredding, electronic erasure, 
and physical destruction of electronic media. These policies are also in line with Western 
University and Lawson Health Research Institute’s policies. 
5.4 
Protecting Privacy 
We will follow all rules and policies implemented by ICES to protecting the information 
collected. To achieve this, ICES implements the privacy policies and practices required by the 
Ontario’s Information and Privacy Commissioner  under section 45 of PHIPA. These include 
implementation of a range of physical and logical controls to control access to information, like 
use of secure zones within ICES facilities, complex passwords and encryption. In addition, ICES 
has adopted the following key principles to protect information: 
1. ICES limits the information it collects to what is necessary and lawful. 
2. ICES restricts access to information within ICES by role. 
3. ICES administers access to information on a project-by-project basis. Scientists must apply 
for and justify each element of information. 
4. ICES prohibits identification of individuals and uses techniques like coding and de-
identification to prevent it. Direct personal identifiers, including names and health card 
numbers and other identifying numbers, are removed and replaced by a confidential code 
promptly after it is collected. 
5. ICES requires all employees and scientists to be trained in the privacy policies and 
procedures relevant to their role, and agree to uphold them. 
Monthly additional data (blood pressures, patient temperature, and dialysate temperature) 
collected from centres will be entered into a secure and password protected website hosted by 
Lawson Health Research Institute Web- and database-server. No patient identifiers will be 
collected and we will ask the centre to send a random group of patients each time. 
 
 

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6.0 
Study Timeline and Milestones 
We will be able to complete this project within 3 years. Year 3 will be used to support activities 
including finalization of the dataset (accounting for the time it takes information to be compiled 
at ICES), completion of the analysis, presentation and publication of the results and 
implementation of the knowledge dissemination plan. The Table below summarizes study 
progress and key milestones.  
Year 1*   
(April 2017 – Mar 2018) 
Year 2 
(April 2018 – March 
2019) 
Year 3 
(April 2019 – March 2020) 
- 
Finalize, register, and 
publish the trial protocol 
- 
Onboard and randomize 
all dialysis centres to the 
intervention/control 
group. 
- Monitor dialysis 
centres monthly for 
uptake and adherence 
to the intervention  
- Publish manuscript on 
the implementation 
strategy used maximize 
adherence to cold 
dialysis temperature 
across 40+ 
hemodialysis centres 
- 
Complete follow-up  
 
- 
Complete data linkages and 
cleaning 
- 
Conduct the analysis 
- 
Present and publish the 
results 
- 
Present results to key 
knowledge users including 
the Ontario Renal Network 
- 
Develop and implement 
knowledge dissemination 
plan 
*We will start collecting monthly preliminary data starting October 2015 or when we have 
Institutional and Ethics Approval. This data will include pre-dialysis core temperature, setting of 
dialysate temperature, pre-dialysis systolic BP, pre-dialysis diastolic BP, nadir systolic BP during 
dialysis session for 15 random patients.  
 
 

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Lee DS, Verocai F, Husain M, Al Khdair D, Wang X, Freeman M IR. Cardiovascular 
outcomes are predicted by exercise-stress myocardial perfusion imaging: Impact on death, 
myocardial infarction, and coronary revascularization procedures. Am Hear J. 
2011;161(5):900-907. 
 
48.  
Wodchis WP, Bushmeneva K, Nikitovic M, McKillop I. Guidelines on Person-Level 
Costing Using Administrative Databases in Ontario. Vol 1. Toronto, Ontario, Canada; 
2013. 
 
49.  
Manning WG, Mullahy J. Estimating log models: to transform or not to transform? Heal 
Econ. 2001;20(4):461-494. 
 
50.  
Hausman JA. Specification Tests in Econometrics. Econometrica. 1978;46(6):1251-1271. 

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
Appendices  
 
Appendix 1: Patient Temperature and setting of the dialysate temperature for the intervention 
group. Centres that have dialysis machines able to change by increments of 0.1°C 
 
Patient Temperature** (°C) 
Dialysate Temperature (°C) 
37.5 and greater 
36.5 (or standard centre protocol) 
37.4 36.9 
37.3 36.8 
37.2 36.7 
37.1 36.6 
37 36.5 
36.9 36.4 
36.8 36.3 
36.7 36.2 
36.6 36.1 
36.5 36 
36.4 35.9 
36.3 35.8 
36.2 35.7 
36.1 35.6 
36 35.5 
35.9 35.5 
35.8 35.5 
35.7 35.5 
35.6 35.5 
35.5 and less 
35.5 (or standard centre protocol) 
 
When to measure patient temperature: before starting the dialysis session using your standard 
thermometer. 
If temperature out of ordinary (e.g. patient consuming cool/warm beverage or just came from the 
cold outside in the winter), then: please start the patient on a reasonable dialysate temperature 
and re-check the body temperature in a few minutes. 

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
Appendix 2: Patient Temperature and setting of the dialysate temperature for the intervention 
group. Centres that have dialysis machines able to change by increments of 0.5°C 
 
Patient Temperature** (°C) 
Dialysate Temperature (°C) 
37.5 and greater 
36.5 (or standard centre protocol) 
37.4 36.5 
37.3 36.5 
37.2 36.5 
37.1 36.5 
37 36.5 
36.9 36 
36.8 36 
36.7 36 
36.6 36 
36.5 36 
36.4 35.5 
36.3 35.5 
36.2 35.5 
36.1 35.5 
36 35.5 
35.9 35.5 
35.8 35.5 
35.7 35.5 
35.6 35.5 
35.5 and less 
35.5 (or standard centre protocol) 
 
 
When to measure patient temperature: before starting the dialysis session using your standard 
thermometer. 
If temperature out of ordinary (e.g. patient consuming cool/warm beverage or just came from the 
cold outside in the winter), then: please start the patient on a reasonable dialysate temperature 
and re-check the body temperature in a few minutes. 
 
 

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
Appendix 3: Common data sources used for population-based studies  
    Database (Source) 
Description 
Key Data Variables 
Health Services 
Discharge Abstract Database  
(CIHI) 
Hospital discharge abstracts for acute, chronic and 
rehabilitative care (1988 onward) 
Diagnoses; Procedures; 
Comorbidities; Length of 
Stay  
National Ambulatory Care 
Reporting System (CIHI) 
Emergency department visits, same day surgery, 
outpatient clinics (e.g., dialysis, cancer clinics) (2002  
onward) 
Reason for visit; Triage 
level; Interventions; Mode 
of arrival 
Ontario Drug Benefit 
Database (MOHLTC) 
Claims for prescribed drugs covered by the Ontario Drug 
Formulary for adults aged 65+ and those receiving social 
assistance (1990 onward) 
Drug ID number; Drug 
quantity; Cost  
Ontario Health Insurance 
Plan (MOHLTC) 
Reimbursement claims made by fee-for-service 
physicians and community based labs (1991 onward) 
Service provided; 
Diagnosis codes ; Fee paid; 
Physician specialty 
Registry 
 
 
Ontario Renal Reporting 
System (ORRS) 
Collects and records the incidence, prevalence, treatment 
changes, and outcomes of all chronic dialysis and solid 
organ transplant patients in Ontario (2010 onward)  
Hemodialysis start; 
vascular access use; 
nephrology referral; 
comorbid and baseline 
conditions; centre 
characteristics  
Population and Demographics 
Registered Persons Database  
(MOHLTC) 
Basic demographic information about anyone who has 
received an Ontario HCN. (1990 onward) 
Date of birth; Date of 
death; Sex; Geographic 
information  
Care Providers 
ICES Physicians Database  
This data set contains yearly information about all 
physicians in Ontario (1992 onward) 
Annual demographics; 
Specialization; Workload  
Laboratory Datasets 
Ontario Gamma Dynacare  
Outpatient laboratory values for all Gamma Dynacare 
laboratories in Ontario (2002 onward, > 59 million tests) 
 
Creatinine levels, lipid 
panels, urine protein 
Cerner Data stream 
Laboratory values from an electronic medical record 
operating in 12 hospitals in Southwestern Ontario (2000 
onward, > 2 million tests).  
Creatinine levels. 
Outpatient, emergency 
room and inpatient values. 
Ontario Laboratories 
Information System (pending 
linkage) 
OLIS is a cornerstone information system that connects 
hospitals, community laboratories, public health 
laboratories and practitioners to facilitate the secure 
electronic exchange of laboratory test orders and results. 
ICES has signed and currently executing a Data Sharing 
Agreement to link Ontario-wide laboratory results to the 
Ontario-wide data holdings housed at ICES. 
Creatinine levels, lipid 
panels, urine protein 
Outpatient, emergency 
room and inpatient values. 
MOHTC: Ministry of Health and Long-term Care, CIHI – Canadian Institutes for Health Information 
 

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
Appendix 4: Primary outcome codes and performance measures 
Outcome 
Algorithm 
Position of 
code 
Performance 
Death 
Registered Person’s 
Database 
N/A 
Accuracy=99% 31 
Hospital admission 
with Ischemic Stroke 
ICD-10:  
I63 (excl. I63.6), I64, 
H341 
Primary 
Diagnosis 
PPV= 85% 44,45  
Hospital admission 
with Myocardial 
infarction 
ICD-10:  
I21, I22 
Primary 
Diagnosis 
Sn= 89%, PPV= 87% 46  
Coronary 
revascularization 
(includes CABG/PCI): 
 
CCI:  
1IJ50, 1IJ57, 1IJ76, 
1IJ54GQAZ 
N/A 
Sn= 99-100%, PPV= 
97-100% 46 ** 
Abbreviations: ICD = International Classification of Disease; OHIP = Ontario Health Insurance Plan; 
Sn=Sensitivity; PPV= Positive Predictive Value. 
** Juurlink et al.46 only validated the codes 1IJ50 and 1IJ76. However, previous studies have reported the use of 
additional codes, which likely increases the sensitivity of capturing coronary revascularizations.47 
 
 
 

MY TEMP Trial Protocol 
 
Drs. Garg & McIntyre 
Version Date: 01-Feb-2017 
 
 
Appendix 5: Full details of economic analysis 
Cost Measurement  
Nominal costs for each encounter with the health system are ascribed using algorithms that have 
been implemented at ICES, based on methods for costing using administrative data.48 Costs for 
each encounter where an encounter-specific payment is made (e.g. for prescriptions, fee for 
service physician visits, assistive devices claims) use that payment information. Costs for acute, 
rehabilitation, complex continuing care and mental health hospital encounters use the appropriate 
resource intensity weight for that particular care setting (e.g. resource intensity weight for acute 
care) and the setting-specific weighted cost derived based on Ontario spending.48 Costs for long-
term care are measured as a fixed per diem based on prevailing government payment rates. 
Capitation payments for primary care physicians are calculated based on the payment rate and 
the particular model of primary care for each patient’s physician in each month of the study 
period. Team-based payments for family health teams and physician bonus payments for pay-
for-performance are not ascribed to individual patients and are not included in the analysis. 
Emergency department and oncologist physicians receive substantial alternative payments that 
are not visit-related and the algorithms also ascribed these payments, generally on an average 
per-patient approach. 
Analysis 
We will compare two-year costs associated with standard and personalized treatments. Costs are 
a useful summative measure of intensity of health system use. Costs are modeled using a 
generalized linear model (e.g. linear model of log-costs etc.) or two part model if necessary 
depending on the structure of the observed cost distributions. The dependent variable in the cost 
analysis will be average cost per month alive in order to account for differences in survival 
between the two population groups. The independent variable will be the treatment group 
assignment. Additional covariates will be added to the model if treatment and control groups are 
not balanced on measured clinical variables. The appropriate model for the cost estimation will 
be selected following Manning and Mullahy.49 Clustering at the hospital level will be addressed 
by including a random-effect for within-centre clustering (or fixed effect if covariates are 
included and random effects are identified to be inconsistent [biased] based on a Heckman 
specification test).50 
The results will also be used to produce estimates of the incremental cost per year alive as an 
estimate of cost-effectiveness. Bootstrap analysis will be used to calculate standard errors and 
95% confidence intervals for the difference in average costs.  The bias corrected and accelerated 
(BCa) method will be used to obtain confidence intervals for the estimates of cost per year alive. 
Given a hypothesis that the intervention will provide improved life expectancy with lower costs, 
we anticipate this estimate will be negative indicating a strictly dominant strategy.  
 

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Journal canadien de la santé et de la maladie rénale
https://doi.org/10.1177/2054358119887988
Canadian Journal of Kidney Health 
and Disease 
Volume 7: 1 –18
© The Author(s) 2020
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/2054358119887988
journals.sagepub.com/home/cjk
Study Protocol-Clinical Research
887988 CJKXXX10.1177/2054358119887988Canadian Journal of Kidney Health and DiseaseAl-Jaishi et al
research-article20202020
Major Outcomes With Personalized 
Dialysate TEMPerature (MyTEMP): 
Rationale and Design of a Pragmatic, 
Registry-Based, Cluster Randomized 
Controlled Trial
Ahmed A. Al-Jaishi1,2,3
, Christopher W. McIntyre1,4,  
Jessica M. Sontrop1,4, Stephanie N. Dixon1,2,4, Sierra Anderson1, 
Amit Bagga5, Derek Benjamin6, David Berry7, Peter G. Blake1,4, 
Laura Chambers1, Patricia C. K. Chan8, Nicole Delbrouck9,  
P. J. Devereaux3, Luis F. Ferreira-Divino1, Richard Goluch10, 
Laura Gregor11, Jeremy M. Grimshaw12,13, Garth Hanson14, 
Eduard Iliescu15, Arsh K. Jain1,2,4, Charmaine E. Lok16,  
Reem A. Mustafa3,17, Bharat Nathoo18, Gihad E. Nesrallah19, 
Matthew J. Oliver20,21, Sanjay Pandeya22, Malvinder S. Parmar23, 
David Perkins24, Justin Presseau12,13, Eli Rabin25,  
Joanna Sasal26, Tanya Shulman27, Manish M. Sood2,12,13
, 
Andrew Steele28, Paul Tam29, Daniel Tascona30,  
Davinder Wadehra31, Ron Wald2,21,32
, Michael Walsh3,33,  
Paul Watson34, Walter Wodchis2,21, Phillip Zager35,  
Merrick Zwarenstein2,4, and Amit X. Garg1,2,3,4
 On behalf of 
the MyTEMP Investigators
Abstract
Background: Small randomized trials demonstrated that a lower compared with higher dialysate temperature reduced the 
average drop in intradialytic blood pressure. Some observational studies demonstrated that a lower compared with higher 
dialysate temperature was associated with a lower risk of all-cause mortality and cardiovascular mortality. There is now the 
need for a large randomized trial that compares the effect of a low vs high dialysate temperature on major cardiovascular 
outcomes.
Objective: The purpose of this study is to test the effect of outpatient hemodialysis centers randomized to (1) a personalized 
temperature-reduced dialysate protocol or (2) a standard-temperature dialysate protocol for 4 years on cardiovascular-
related death and hospitalizations.
Design: The design of the study is a pragmatic, registry-based, open-label, cluster randomized controlled trial.
Setting: Hemodialysis centers in Ontario, Canada, were randomized on February 1, 2017, for a trial start date of April 3, 
2017, and end date of March 31, 2021.
Participants: In total, 84 hemodialysis centers will care for approximately 15 500 patients and provide over 4 million dialysis 
sessions over a 4-year follow-up.
Intervention: Hemodialysis centers were randomized (1:1) to provide (1) a personalized temperature-reduced dialysate 
protocol or (2) a standard-temperature dialysate protocol of 36.5°C. For the personalized protocol, nurses set the dialysate 
temperature between 0.5°C and 0.9°C below the patient’s predialysis body temperature for each dialysis session, to a 
minimum dialysate temperature of 35.5°C.

2 
Canadian Journal of Kidney Health and Disease
Primary outcome: A composite of cardiovascular-related death or major cardiovascular-related hospitalization (a 
hospital admission with myocardial infarction, congestive heart failure, or ischemic stroke) captured in Ontario health care 
administrative databases.
Planned primary analysis: The primary analysis will follow an intent-to-treat approach. The hazard ratio of time-to-first 
event will be estimated from a Cox model. Within-center correlation will be considered using a robust sandwich estimator. 
Observation time will be censored on the trial end date or when patients die from a noncardiovascular event.
Trial Registration: www.clinicaltrials.gov; identifier: NCT02628366.
Abrégé 
Contexte: De petits essais à répartition aléatoire ont montré que l’utilisation d’un dialysat à basse température réduisait 
le risque d’hypotension intra-dialytique. De même, certaines études observationnelles ont démontré qu’un dialysat à basse 
température était associé à un plus faible risque de mortalité toute cause ou d’origine cardiovasculaire. Le temps est venu 
de procéder à un vaste essai à répartition aléatoire comparant les effets d’un dialysat à basse température et à température 
standard sur les principaux résultats cardiovasculaires.
Objectif: Répartir aléatoirement des centres d’hémodialyse ambulatoire pour qu’ils suivent pendant quatre ans (i) un 
protocole personnalisé de dialysat à basse température ou (ii) un protocole de dialysat à température standard, et tester 
l’effet sur les hospitalisations et la mortalité attribuables à des événements cardiovasculaires.
Type d’étude: Un essai clinique à répartition aléatoire en grappes.
1London Health Sciences Centre, ON, Canada
2ICES, ON, Canada
3McMaster University, Hamilton, ON, Canada
4Western University, London, ON, Canada
5Windsor Regional Hospital, ON, Canada
6Royal Victoria Hospital, Barrie, ON, Canada
7Sault Area Hospital, Sault Ste. Marie, ON, Canada
8Michael Garron Hospital, Toronto, ON, Canada
9Renfrew Victoria Hospital, ON, Canada
10Health Sciences North, Sudbury, ON, Canada
11Grand River Hospital, Kitchener, ON, Canada
12Ottawa Hospital Research Institute, ON, Canada
13University of Ottawa, ON, Canada
14Peterborough Regional Health Centre, ON, Canada
15Kingston Health Sciences Centre, ON, Canada
16University Health Network, Toronto, ON, Canada
17University of Kansas Medical Center, Kansas City, USA
18Mackenzie Health, Richmond Hill, ON, Canada
19Humber River Regional Hospital, Toronto, ON, Canada
20Sunnybrook Health Sciences Centre, Toronto, ON, Canada
21University of Toronto, ON, Canada
22Halton Healthcare, Oakville, ON, Canada
23Timmins and District Hospital, ON, Canada
24Trillium Health Partners, Mississauga, ON, Canada
25Niagara Health System, St. Catharines, ON, Canada
26St. Joseph’s Health Centre, Toronto, ON, Canada
27North Bay Regional Health Centre, ON, Canada
28Lakeridge Health Corporation, Oshawa, ON, Canada
29Scarborough Health Network, ON, Canada
30Orillia Soldiers’ Memorial Hospital, ON, Canada
31William Osler Health Centre, Brampton, ON, Canada
32St. Michael’s Hospital, Toronto, ON, Canada
33St. Joseph’s Healthcare, Hamilton, ON, Canada
34Thunder Bay Regional Health Sciences Centre, ON, Canada
35Dialysis Clinic Inc, Nashville, TN, USA
Corresponding Author:
Amit X. Garg, London Health Sciences Centre and Western University, Westminster Tower, 800 Commissioner’s Road East, ELL-215, London, ON, 
Canada N6A 5W9. 
Email: amit.garg@lhsc.on.ca

Al-Jaishi et al 
3
Cadre: Le 1er février 2017, des centres d’hémodialyse de l’Ontario (Canada) ont été répartis aléatoirement en vue d’un 
essai qui a débuté le 3 avril 2017 et qui se poursuivra jusqu’au 31 mars 2021.
Participants: Quatre-vingt-quatre centres d’hémodialyse qui prendront en charge environ 15 500 patients pendant les 
quatre ans de suivi.
Intervention: Les centres d’hémodialyse ont été répartis aléatoirement (1:1) pour offrir (i) un protocole personnalisé 
de dialysat à température réduite ou (ii) un protocole de dialysat à 36,5°C. Pour le protocole personnalisé, les infirmières 
règlent la température du dialysat entre 0,5 et 0,9°C sous la température corporelle du patient mesurée avant la dialyse, 
jusqu’à une température minimale de 35,5°C.
Principaux résultats: Un ensemble d’hospitalisations attribuables à un événement cardiovasculaire majeur (accident 
ischémique cérébral non fatal, infarctus du myocarde ou insuffisance cardiaque congestive) et de décès d’origine cardiovasculaire 
consignés dans les bases de données de santé de l’Ontario.
Principale analyse envisagée: L’analyse primaire adoptera une approche fondée sur l’intention de traiter. Un modèle de 
Cox servira à estimer le rapport de risque du temps écoulé jusqu’au premier événement. La corrélation intra-centre sera 
prise en compte à l’aide d’un estimateur sandwich robuste. Le temps d’observation sera censuré à la date de fin de l’essai ou 
au moment d’un décès non lié à un événement cardiovasculaire.
Keywords
cluster randomized controlled trial, pragmatic trial, dialysis, dialysis solutions, personalized dialysate temperature, cardiovascular 
events, mortality
Received September 9, 2019. Accepted for publication September 23, 2019.
What was known before
••
The results of small randomized clinical trials suggest 
that a cooler dialysate temperature (≤35.5°C) com-
pared with a standard dialysate temperature (≥36.0°C) 
lessens the drop in systolic blood pressure during 
hemodialysis treatments.
••
In some observational studies, using a cooler dialysate 
temperature (≤35.5°C) vs a standard dialysate tem-
perature (≥36.0°C) was associated with a lower rate 
of all-cause and cardiovascular-related death in adults 
receiving in-center hemodialysis.
What this adds
••
The Major Outcomes with Personalized Dialysate 
TEMPerature (MyTEMP) trial will generate robust 
information on whether providing a personalized dial-
ysate temperature (set 0.5°C below a patient’s body 
temperature measured prior to each dialysis session) 
vs usual dialysate temperature (36.5°C) as a hemodi-
alysis center policy for 4 years alters the risk of car-
diovascular-related death or cardiovascular-related 
hospitalization.
Background
Maintenance hemodialysis provides a life-saving treatment 
for patients with end-stage kidney disease (approximately 3 
million worldwide and 23 000 in Canada); however, 20% to 
40% of patients die within 1 year of starting dialysis, which 
is often due to cardiovascular-related causes.1-5 Evidence 
from magnetic resonance imaging showed hemodialysis 
itself can injure the heart, brain, and other vital organs 
through repeated episodes of intradialytic hypotension and 
subclinical ischemia.6-13 During a hemodialysis session, 
blood pressure often drops by 20 mm Hg or more, and this 
can lead to coronary hypoperfusion and myocardial stun-
ning,14,15 which is associated with left ventricular dysfunc-
tion.8,10,16-18 This results in the heart losing some of its ability 
to compensate for the reduced blood volume that occurs dur-
ing dialysis, and this may lead to further hypotensive events 
and related ischemic organ damage (more detail in 
Supplemental Appendix 1—Section 1.1). In observational 
research, a greater frequency of intradialytic hypotension 
was associated with an incrementally greater risk of death, 
and patients with the lowest nadir blood pressure during dial-
ysis had the highest risk of death.13
Reducing the dialysate temperature is one strategy to help 
stabilize blood pressure during hemodialysis. The measures 
used to describe blood pressure differences between cooler 
dialysate temperature (≤35.5°C) vs a standard dialysate 
temperature (≥36.0°C) in prior individual-level randomized 
controlled trials (RCTs) have not been consistent; with some 
reporting mean intradialytic systolic blood pressure, nadir 
systolic blood pressure, and predialysis and postdialysis 
blood pressure. Nevertheless, these trials reported that cooler 
compared with standard dialysate temperature led to (1) 
higher intradialytic nadir systolic blood pressure readings, 
(2) a smaller drop in postdialysis blood pressure from predi-
alysis blood pressure, and (3) a smaller drop in nadir intra-
dialytic blood pressure from predialysis blood pressure 
(more detail in Supplemental Appendix 1—Section 1.2 and 
eTable 1).15,19-28

4 
Canadian Journal of Kidney Health and Disease
A cooler dialysate may also improve peripheral vascular 
resistance, improve cardiac function, and alter the level of 
vasoactive peptides—all of which may stabilize intradialytic 
blood pressure.24,29-35 Compared with a dialysate temperature 
of 37°C (standard dialysate temperature in the United 
Kingdom, where the trial took place), a cooler personalized 
dialysate temperature (ie, 0.5°C below the patient’s predialy-
sis body temperature) showed less injury to both the brain 
and the heart over a 12-month period, as observed on mag-
netic resonance imaging (more detail in Supplemental 
Appendix 1—Section 1.2).8,10
In a meta-analysis of 26 randomized clinical trials (total 
484 patients), a cooler dialysate temperature (ie, 34°C-35.5°C 
vs the control, where the control in different regions ranged 
from 36°C to 38.5°C) reduced the rate of intradialytic hypo-
tension by 70% (95% confidence interval: 49%-89%), sig-
nificantly increased the intradialytic mean arterial pressure 
by 12 mm Hg (95% confidence interval: 8-16 mm Hg).36 
Several trials reported a smaller drop in average intradialytic 
nadir systolic blood pressure and postdialysis systolic blood 
pressure compared with predialysis systolic blood pres-
sure.19,21,22,36 Dialysis adequacy (measured using Kt/V) was 
not statistically different between patients treated with cooler 
vs standard dialysate temperature.36 Most trials enrolled 
fewer than 30 patients and only 3 trials followed patients for 
longer than 6 sessions; mortality and major adverse events 
were not evaluated.23,24,37 In observational studies, the use of 
a cooler dialysate has been associated with a reduced risk of 
cardiovascular mortality38,39 and all-cause mortality in 
some,38 but not all studies39,40 (more detail in Supplemental 
Appendix 1—Section 1.3).
Currently, the dialysate temperature used in most centers 
in Canada and the United States ranges from 36.5°C to 
36.7°C (97.7°F-98.1°F) (more detail in Supplemental 
Appendix 1—Section 1.4).41 This practice comes largely 
from clinical tradition rather than empirical evidence (with 
the historic rationale being that the dialysate temperature 
should be similar to the average body temperature). While a 
cooler dialysate shows promise for stabilizing intradialytic 
blood pressure and improving patient outcomes, current tri-
als investigating this question (registered on clinicaltrials.
gov) plan to enroll fewer than 150 patients and will therefore 
lack statistical power to test the effect of this intervention on 
many important outcomes. To inform clinical practice, evi-
dence from a large, pragmatic, high-quality, multicenter ran-
domized clinical trial is needed.36,42,43
A Pragmatic Cluster Randomized Clinical Trial of 
Dialysate Temperature
This protocol describes the design and statistical analysis plan 
for a cluster randomized clinical trial that will test the effect 
of randomizing hemodialysis centers to provide a personal-
ized reduced-temperature dialysate protocol vs a standard-
temperature protocol (ie, 36.5°C) for 4 years on the rate of 
cardiovascular-related death or hospitalization in outpatients 
receiving maintenance hemodialysis. The personalized dialy-
sate temperature-reduced approach proposed in this trial 
accounts for individual, diurnal, and seasonal variations in 
body temperature. In contrast, a nonpersonalized protocol of 
dialysate temperature might be fixed at a specific temperature 
(eg, 35.5°C) for all patients, irrespective of their body tem-
perature. In a clinical trial of 73 patients, a personalized 
approach achieved the hemodynamic benefits of cooler 
hemodialysis without any major patient concerns about feel-
ing cold (no patient stopped their hemodialysis session 
early).8,10 More details on how the dialysate temperature is set 
and maintained during hemodialysis and patient effects are 
provided in Supplemental Appendix 1—Sections 1.5 and 1.6.
Objective
The purpose of this study is to test the effect of randomizing 
hemodialysis centers to provide (1) a personalized tempera-
ture-reduced dialysate protocol of 0.5°C to 0.9°C below the 
patient’s predialysis body temperature measured before each 
dialysis session, to a minimum dialysate temperature of 
35.5°C, vs (2) a standard-temperature dialysate protocol of 
36.5°C, for a period of 4 years, on a composite outcome of 
cardiovascular-related death or hospitalization for major car-
diovascular events in outpatients receiving maintenance 
hemodialysis.
Methods
Study Design and Overview
The Major outcomes with personalized dialysate TEMP-
erature (MyTEMP) is a pragmatic, 2-arm, parallel-group, 
registry-based, open-label, cluster RCT. The trial started on 
April 3, 2017, and enrolled 84 (of the 97) hemodialysis cen-
ters in Ontario, Canada, at that time. This province-wide trial 
is embedded into routine care with center-wide implementa-
tion of the intervention delivered by dialysis unit personnel 
rather than research staff (Supplemental Appendix 1—
Section 1.7). Patient characteristics and outcomes will be 
largely obtained from administrative health care databases. 
This pragmatic design allows broad inclusion of dialysis 
centers and a large representative sample of patients that 
should yield highly generalizable findings (Figure 1).44,45
Hemodialysis centers were randomized (1:1) to provide 
(1) a personalized temperature-reduced dialysate protocol 
(see “Intervention” section) or (2) a standard dialysate tem-
perature of 36.5°C, which reflects usual practice at Ontario 
hemodialysis centers. Randomization with concealed alloca-
tion was conducted centrally on February 1, 2017, and cen-
ters were notified of their group allocation by the study team 
2 months before the intervention start date. The primary out-
come is a composite of cardiovascular-related death or hos-
pital admission with myocardial infarction, congestive heart 

Al-Jaishi et al 
5
failure, or ischemic stroke. Follow-up for study outcomes 
will continue until March 31, 2021.
Choice of study design. In this trial, the unit of randomization 
is the cluster (ie, the hemodialysis center) and the unit of 
analysis is the patient (for the primary and most secondary 
outcomes). For the secondary outcome of mean drop in sys-
tolic blood pressure (see “Secondary Outcomes” section), 
the unit of randomization and the unit of analysis is the clus-
ter because we sample a subset of hemodialysis sessions 
each month to represent the entire cluster (see “Data Collec-
tion” section). We chose a cluster randomized design to 
enhance intervention uptake and adherence (logistical con-
venience) and to minimize cross-group contamination. 
Hemodialysis patients typically receive all their treatments at 
the same center, making this population suitable for cluster-
level interventions. Delivery of the MyTEMP intervention in 
this cluster trial follows what occurs in routine care, where 
all nurses in each center are trained to follow the same dialy-
sis protocol or policy for patients under their care.
Eligibility Criteria
This trial had 2 inclusion criteria at the level of the hemodi-
alysis center:
1. 
The hemodialysis center must have cared for a mini-
mum of 15 outpatients being treated with mainte-
nance in-center hemodialysis on January 1, 2017.
2. 
The medical director of the hemodialysis center (who 
acted as the center’s gatekeeper) must have been 
willing for their center to adopt the randomly allo-
cated dialysate temperature protocol for the duration 
of the trial.
Hemodialysis medical centers and patients. On February 1, 
2017 (the randomization date), Ontario had 97 hemodialysis 
centers that were overseen by 26 medical directors. Nine 
centers (less than a total of 135 patients) cared for fewer than 
15 patients, and 4 centers (less than a total of 120 patients) 
were not included at the request of the medical director. 
Thus, 84 hemodialysis centers (caring for approximately 
7500 hemodialysis patients at the randomization date) met 
the trial’s eligibility criteria. Figure 2 shows the geographical 
locations of all participating centers.
At the time of the analysis, we will restrict the study 
cohort to outpatients who received in-center maintenance 
hemodialysis at a participating study center between April 3, 
2017, and March 31, 2021. To minimize the inclusion of 
patients who leave the study or switch centers soon after 
starting in-center hemodialysis, we will restrict the cohort to 
Figure 1. Precis-2 wheel highlighting the pragmatism of MyTEMP trial for 9 domains.
Note. Small reductions in pragmatism relate to (1) monthly data collection from centers to assess intervention adherence and (2) contact with centers 
that had less than 80% adherence. MyTEMP = Major Outcomes with Personalized Dialysate TEMPerature.

6 
Canadian Journal of Kidney Health and Disease
patients who received treatment at the same participating 
study center for at least 90 days before their cohort entry date 
(the index date), after which the patient’s observation time 
will begin (termed the 90-day rule). This added restriction 
would exclude (1) patients who quickly recover renal func-
tion (eg, patients with acute kidney injury), (2) early sched-
uled transfers to home dialysis or those receiving kidney 
transplants, and (3) those with arranged dialysis treatments 
away from home (transient dialysis). In our analysis of his-
toric data, approximately 40% of patients were excluded 
from the cohort as a result of the 90-day rule (in the 90 days 
observation period prior to cohort entry, patients may have 
died, recovered their renal function, switched to home dialy-
sis, received a kidney transplant, or emigrated out of the 
province).
Intervention
Hemodialysis centers were randomly allocated (as described 
above) to provide a personalized temperature-reduced dialy-
sate protocol or a standard-temperature dialysate protocol. 
On April 3, 2017, 62 participating centers utilized hemodi-
alysis machines that were able to modify the dialysate tem-
perature by steps of 0.1°C, and the remaining 22 centers 
were able to modify the dialysate temperature by steps of 
0.5°C. The predialysis body temperature was measured by a 
nurse as done in usual care before each dialysis session; 41 
centers used tympanic, 33 used oral, 6 used a combination of 
tympanic and oral, and 4 used forehead thermometers.
For the personalized protocol, a nurse sets the dialysate 
temperature between 0.5°C and 0.9°C below each patient’s 
predialysis body temperature, to a minimum dialysate tem-
perature of 35.5°C (Supplemental Appendices 2 and 3). For 
machines that can only lower the dialysate temperature by 
steps of 0.5°C, nurses were asked to lower the temperature to 
the next increment, to a maximum of 0.9°C below the 
patient’s temperature. For example, if a patient’s body tem-
perature is 36.7°C, then the dialysate temperature is set to 
36.0°C (Supplemental Appendix 3). The set dialysate tem-
perature remains fixed for the duration of the dialysis ses-
sion. For the intervention arm, the lowest recommended 
setting is 35.5°C and the highest is 36.5°C.
Protocol adherence. Participating centers were asked to apply 
the randomly allocated temperature protocol for all patients 
and hemodialysis sessions. If necessary, individual patients, 
in consultation with their nephrologist, may opt to use a dif-
ferent dialysate temperature. In this pragmatic trial, our goal 
Figure 2. Map of Ontario, Canada, depicting participating centers across the province.
Note. Each black dot represents one of the 84 participating hemodialysis centers that in total care for approximately 7500 patients at any time. During the 
4-year trial period, these 84 centers will care for approximately 15 550 patients and will provide over 4 million hemodialysis sessions.

Al-Jaishi et al 
7
is to have at least 80% adherence to the center’s allocated 
protocol at any given time. This level of adherence is 
expected to achieve a target between-group difference in the 
median dialysate temperature of approximately 0.5°C (ie, the 
median dialysate temperature is expected to be 36.5°C for 
centers in the control arm and 36.0°C for centers in the inter-
vention arm). This estimate is based on a preliminary analy-
sis of 12 012 hemodialysis sessions during the 6-month 
period before the trial start date, where patients had a median 
predialysis body temperature of 36.3°C (25th, 75th percen-
tiles: 35.9°C, 36.6°C). As described in the “Data Collection” 
section, we will monitor center adherence by randomly sam-
pling hemodialysis sessions from each center in both groups 
during the trial follow-up. We have shown that this sampling 
accurately reflects overall center adherence (see Supplemen-
tal Appendix 4). If center adherence drops below 80%, we 
contact the local site investigator, and where applicable, a 
nurse educator, charge nurse, or hemodialysis program man-
ager, to explore reasons for low adherence and discuss pos-
sible solutions (Table 1). We will also examine the proportion 
of time patients spend in their index center’s initial allocation 
(ie, if their initial center was allocated to the intervention 
arm, what proportion of follow-up time did patients spend in 
a center allocated to the intervention arm).
Implementation Strategy
We used a framework of behavioral change (the Theoretical 
Domains Framework) to assess and address potential barri-
ers to intervention implementation before the trial started. 
The results from this work are detailed elsewhere.48 Briefly, 
through semistructured interviews with physicians and 
nurses, we identified some potential barriers that we were 
able to address before the trial started. These included 
aligning the intervention protocol with local policies and 
procedures, addressing concerns about thermometer accu-
racy, patient comfort, and beliefs about the potential impact 
of the intervention on patients.48 This information was 
incorporated into the trial’s educational and training mate-
rials, which were delivered by study staff, nurse educators, 
or charge nurses to the other dialysis nurses. Training ses-
sions included opportunities to discuss and address other 
additional concerns or barriers to maximize intervention 
uptake and adherence.
Ethical Considerations
This trial was designed and is being conducted in accordance 
with the second edition of the Tri-Council Policy Statement 
(TCPS-2).49 The Health Sciences Research Ethics Board at 
Western University centrally approved the research ethics 
application for Ontario through the Streamlined Research 
Ethics Review System managed by Clinical Trials Ontario 
(CTO), an independent not-for-profit organization estab-
lished with support from the Government of Ontario. Ethics 
approval for this trial was given on behalf of 13 institutions 
(overseeing 45 hemodialysis centers at the time) participat-
ing in CTO’s streamlined ethics review process. The remain-
ing institutions received ethics approval from their local 
research ethics boards. The medical directors of the dialysis 
centers (see “Eligibility Criteria” section) acted as the cen-
ter’s gatekeeper and provided overall approval for their 
hemodialysis center(s) to participate and be randomized. We 
also received approval to obtain de-identified baseline and 
follow-up information on all patients in each participating 
dialysis center through administrative data sets held at ICES 
(previously known as the Institute for Clinical Evaluative 
Sciences), which has special status with the privacy commis-
sioner of Ontario (see “Data Collection” section).
The trial received research ethics approval with a waiver 
of written patient consent for enrollment, receiving the allo-
cated temperature protocol, and data collection; the criteria 
for this waiver are detailed in Supplemental Appendix 5. All 
patients receiving hemodialysis at a participating center were 
notified about the trial and of their right to opt out of their 
center’s allocated treatment protocol; however, given our 
data sources (see “Data Collection” section), it is not possi-
ble for patients to opt out of data collection or data analysis 
(where encoded information on all patients receiving hemo-
dialysis at each center is analyzed and aggregated without 
knowledge of whether a specific patient adhered to the ran-
domly allocated treatment). Participating centers were pro-
vided with an information letter to give to patients; the letter 
described the center’s allocated temperature protocol and 
patients’ right to dialyze at a different temperature should 
they, or their treating physician choose (see “Protocol 
Adherence” section). As well, posters describing the trial 
were placed in a highly accessible area (eg, the patient wait-
ing area, near the scale where all patients are weighed before 
each treatment).
Presentations to Patient and Family Advisory 
Councils
We presented MyTEMP trial details to several Renal 
Patient and Family Advisory Councils across Ontario and 
sought feedback and advice on the trial, the intervention, 
and on what patient-important outcomes should be consid-
ered.50 These discussions influenced how the trial was 
communicated to patients (including in the patient infor-
mation letter) and we are now designing an independent 
substudy that will assess patient-reported symptoms (eg, 
itching, tiredness, time to recovery after treatment) in a 
subset of centers (details of this substudy are not included 
in this protocol).
Data Collection
Data on patient characteristics and study outcomes will be 
obtained through administrative data sources housed at 

8 
Canadian Journal of Kidney Health and Disease
Table 1. Potential Techniques to Address Low Adherence at a Center Depending on the Allocated Group.
Potential reason for low compliance
How the issue may be addressed
Control arm
 Patients are hypotensive and may require 
cooler dialysate temperature
When patients are at high risk of intradialytic hypotension, and the treating 
physician wishes to lower the dialysate temperature, we ask their treating 
physician to consider lowering the dialysate temperature at increments of 0.5°C 
rather than prescribing a set temperature below 36°C. This recommendation 
aligns with guidelines from the Canadian Society of Nephrology and other 
organizations46,47
 Nurses forget to use the prescribed 
dialysate protocol
Nurse educator or charge nurse is asked to highlight the importance of 
following the prescribed dialysate temperature during their regular rounds 
and educational sessions. Specific nurses not following the prescribed dialysate 
temperature protocol are approached separately for retraining/education
Intervention arm
 Nurses forget to use the prescribed 
dialysate protocol
See “Nurses forget to use the prescribed dialysate protocol” above
 Nurses set a warmer temperature for 
patients who are hypertensive
In centers when this occurs, we ask the lead site investigator to speak directly 
with those nurses regarding the potential impact of raising the dialysate 
temperature beyond the patient’s body temperature. We suggest avoiding 
externally/actively warming patients by increasing the dialysate temperature 
beyond the patient’s body temperature. During the hemodialysis session, core 
temperature increases, which may lead to peripheral vasodilation counteracting 
the normal vascular response to a decline in blood volume. Increasing the 
dialysate temperature may exacerbate that process and lead to a sudden and 
significant drop in blood pressure. Also, increasing the dialysate temperature 
may increase the core body temperature resulting in reduced tissue 
oxygenation
 Patients are unable to tolerate the 
MyTEMP intervention protocol
Whenever patients decline the intervention due to cold symptoms, we ask 
nurses to follow the protocol below
Accommodate patients as per usual care and suggest any of the following, if 
available, at the unit:
• Suggest that patients bring a blanket to their hemodialysis session
• Suggest that patients bring or wear additional layers to their hemodialysis 
session
• Offer a warm blanket to keep the patient comfortable
If the patient continues to feel uncomfortable and unable to tolerate the 
prescribed dialysate temperature, we suggest physicians and/or nurses increase 
the dialysate temperature to 36°C to a maximum of 36.5°C
 Patients decline the MyTEMP intervention 
protocol
We ask the treating physician to discuss with their patients the potential benefits 
of personalized dialysate temperature. Physicians explain that personalized 
dialysate temperature is the new center protocol because current evidence 
suggests it may be beneficial for patients. Previous research shows it reduces 
the frequency in drops in blood pressure and reduces the feeling of fatigue from 
these drops in blood pressure. As an added benefit, we think by following this 
new way of setting the machine temperature, our patients may be less likely to 
experience events like heart attacks and strokes
When messaging to patients, rather than saying “we are cooling the dialysate 
temperature,” please consider messaging the intervention as “personalizing the 
machine temperature to your [the patient’s] body temperature”
If the patient is willing, the physician/nurse can ask the patient to try personalized 
dialysate temperature for at least 3 sessions to see how they feel during and 
after the hemodialysis session. Patients were assured they can still use a warm 
blanket or bring additional layers if they feel cold symptoms during their session
If a patient wishes to use a different dialysate temperature after these discussions, 
the treating physician will not adhere to the MyTEMP protocol and prescribe 
a different temperature moving forward. If the treating physician is prescribing 
a different temperature, we ask them to consider a dialysate temperature of 
36°C rather than 36.5°C
Note. MyTEMP = Major Outcomes with Personalized Dialysate TEMPerature.

Al-Jaishi et al 
9
ICES. ICES is a prescribed entity for the purposes of Section 
45 Ontario’s Personal Health Information Privacy Act, which 
means that health information custodians, including physi-
cians, hospitals, or long-term care homes, are permitted to 
disclose personal health information about their patients to 
ICES without patient consent. Secure access to these data is 
governed by policies and procedures that are approved by the 
Information and Privacy Commissioner of Ontario. These 
data sets will be linked using unique encoded identifiers and 
analyzed at ICES. More information about the databases and 
variables that will be used in this study is provided in 
Supplemental Appendices 6 and 7.
The following data, collected as part of routine care, will 
be obtained from the hemodialysis run sheet as part of 
patients’ medical record: the patient’s predialysis body tem-
perature (measured as described in the “Intervention” sec-
tion), the patient’s predialysis systolic and diastolic blood 
pressure (typically measured while seated), the patient’s 
nadir systolic with accompanying diastolic blood pressure 
during the hemodialysis session, and the prescribed dialysate 
temperature. Baseline data on these variables will be obtained 
from a random sample of 15 hemodialysis sessions from 
each center during the 2-month period before the interven-
tion start date. After the intervention start date, these data 
will be collected from a random sample of 15 hemodialysis 
sessions weekly for the first month, biweekly for the second 
month, and monthly thereafter. Data will be collected on 
either the last Friday or Saturday of the data collection 
period. During the 4-year study follow-up, we expect to sam-
ple approximately 65 500 of 4.2 million hemodialysis ses-
sions (Table 2).
Primary Outcome
The primary outcome is a composite of cardiovascular-
related death or major cardiovascular-related hospitalization 
(a hospital admission with myocardial infarction, congestive 
heart failure, or ischemic stroke; the coding algorithm is pro-
vided in Supplemental Appendix 8). Data on cause of death 
will primarily be obtained from the Office of the Registrar 
General Deaths (ORGD) database, which records the cause 
of death for all deaths in Ontario; however, the release of 
these data is lagging by 2 years. Thus, at the time of the anal-
ysis (anticipated in Spring 2023), to avoid delays in the pub-
lication of results, ORGD cause-of-death data will be 
available for all deaths that occur between April 3, 2017, and 
December 31, 2020 (which is 88% of the follow-up period). 
Deaths that occur in the last 3 months of follow-up will be 
captured using the Registered Persons Database,51 and cause 
of death will be defined as cardiovascular-related if the 
patient dies in hospital (or the emergency department) with a 
cardiovascular event as the main diagnosis on the discharge 
summary. For the subset of deaths that occur outside of hos-
pital in the last 3 months of follow-up, cause of death will be 
unknown; using historic data, approximately 33% of cardio-
vascular-related deaths were missed because they occurred 
outside of hospital. Hospital encounters for cardiovascular 
events will be ascertained using the 10th version of the 
International Classification of Diseases (ICD-10) codes. 
These codes have high accuracy (Supplemental Appendix 8), 
demonstrating high sensitivity and specificity in the general 
population against adjudication of medical charts as the ref-
erence standard.51-55
Table 2. Expected Number of Prevalent Patients at Any Specific Time and the Expected Total Number of Patients and Hemodialysis 
Sessions Over the 4-Year Follow-Up.
Personalized reduced dialysis 
temperature 0.5°C
predialysis core body temperature
Fixed dialysis temperature 
of 36.5°C
Number of hemodialysis centers
42
42
Expected number of prevalent hemodialysis patients per center
Median (25th, 75th percentiles)
Average: 103
Median: 81 (32, 130)
Average: 89
Median: 56 (30, 132)
Expected number of patients per center over the 4-year follow-
upa
Median (25th, 75th percentiles)
Average: 189
Median: 136 (60, 262)
Average: 174
Median: 100 (49, 253)
Expected total number of patients over the 4-year follow-up
7750
7750
Expected number of sessions over 4-year follow-up periodb
2 184 000
2 184 000
Expected number of sampled hemodialysis sessions over 4-year 
follow-up periodc
32 760
32 760
aIncludes both prevalent patients who were on dialysis as of April 3, 2017, and new patients who start hemodialysis over the 4-year follow-up.
bUsing historic data, we estimate there will be approximately 31 314 patient-years of follow-up (over a 4-year period). We also assume there will be 
at least 3500 patients dialyzing at any one point in time per group. Assuming 3 hemodialysis sessions/week regimen, there will be approximately 156 
hemodialysis sessions per patient-year [3 sessions/week × 52 weeks/year]. Thus, 3500 patients × 156 hemodialysis sessions per patient-year × 4 years of 
follow-up is equal to 2 184 000 sessions. (Note: These calculations assume that the number of prevalent patients remains constant overtime and is similar 
in both groups. The true hemodialysis sessions count will likely be higher because the number of patients on hemodialysis is increasing each year.)
cBased on 15 hemodialysis sessions randomly selected per month and 42 centers over a 48-month period. It should be noted, in April and May 2017, we 
collected data weekly and biweekly, respectively.

10 
Canadian Journal of Kidney Health and Disease
Justification for using a primary composite outcome. The pri-
mary composite outcome will provide an overall measure of 
the intervention’s impact on cardiovascular-related morbid-
ity and mortality. The outcome components are each expected 
to respond similarly to the intervention (ie, be reduced by a 
similar magnitude) and have a similar rate of occurrence, and 
each is clinically important—appreciating that while death is 
far worse than a cardiovascular-related hospitalization—
avoiding the latter is also important to patients. A detailed 
justification for each component is provided in Supplemental 
Appendix 9.
Secondary Outcomes
The key mechanism through which a personalized dialy-
sate temperature may be beneficial is through preventing 
drops in intradialytic systolic blood pressure. As a key 
secondary outcome, we will examine the between-group 
mean difference in the drop in intradialytic systolic blood 
pressure. A blood pressure drop is defined as the predialy-
sis systolic blood pressure minus the intradialytic nadir 
systolic blood pressure, where the greater the number (in 
the positive direction), the larger the drop (see “Data 
Collection” section).
Most definitions of intradialytic hypotension are defined 
by a specified drop (eg, ≥20 mm Hg) in systolic blood pres-
sure. In this trial, we have limited statistical power to detect 
clinically important between-group differences in the pro-
portion of patients who experience intradialytic hypotension. 
Thus, the outcome of intradialytic hypotension will only be 
considered in additional post hoc analyses (Supplemental 
Appendix 10).
The following secondary outcomes will also be exam-
ined: a composite of all-cause death or cardiovascular-related 
hospitalization, all-cause death, and components of the pri-
mary composite outcome examined separately.
Other Important Outcomes
We will examine additional patient-important outcomes that 
(1) may be responsive to the intervention based on prior lit-
erature or biologic rationale and (2) can be reliably assessed 
using our administrative data sources. These outcomes 
include a composite of emergency department visits and all-
cause hospitalization (also each examined separately), a hos-
pital encounter with major lower limb amputation, and a 
hospital encounter with a major fall or fracture.
Randomization
Sequence generation, allocation concealment, and implementa-
tion. Centers were randomly allocated to the intervention or 
control arm (1:1) using covariate-constrained randomization 
(detailed below). The allocation scheme was computer-gen-
erated at a central location (ICES Western, London, Ontario, 
Canada) on February 1, 2017, using SAS version 9.4 (SAS 
Institute Inc, Cary, North Carolina) and concealed from 
study investigators and centers. The study team notified each 
hemodialysis center of their assigned allocation approxi-
mately 2 months before the intervention start date (the 
2-month lead time was chosen to give centers enough time to 
update their standard operating procedures and to conduct 
nurse training sessions on delivering the allocated tempera-
ture protocol).
Covariate-constrained randomization. We performed the 
covariate-constrained randomization in the following series 
of steps (this method has been shown to produce interven-
tion groups that are well balanced on measured baseline 
characteristics).56-58 We first generated a large number of 
randomized allocation schemes from 1.68 × 1024 possible 
schemes, and selected those with good balance on a set of 
patient-level baseline characteristics—a scheme was con-
sidered to have good balance if the between-group stan-
dardized differences on the constrained variables were 
within 10% caliper.59 This caliper size was chosen because 
it was expected to result in the trial arms having over 90% 
overlap on the distributions of the measured baseline char-
acteristics. We then randomly selected one randomization 
scheme from the set with good balance. Given that ICES 
data sources lag by approximately 6 to 12 months, the base-
line data used in the covariate-constrained randomization 
were based on patient and center records from April 1, 
2016. New patients will initiate hemodialysis during the 
trial follow-up period, and these patients are also counted in 
the primary analysis. Therefore, at the final analytic stage, 
we will conduct analyses to confirm that the groups are 
similarly balanced on baseline characteristics at their trial 
entry date (April 3, 2017, for patients receiving dialysis at 
the beginning of the trial or, for new patients, on the date 
they started outpatient hemodialysis).
Blinding. The nature of the intervention makes it infeasible 
to blind patients, nurses, or nephrologists to the treatment 
assignment; however, the primary outcome will be recorded 
by medical coders who are unaware of the trial or the cen-
ter’s treatment assignment. In Ontario, medical coders 
review the medical charts of all patients with health care 
encounters, and code all diagnoses and procedures using 
ICD-10 coding system; this information is then entered into 
the Canadian Institute for Health Information Discharge 
Abstract Database. Medical coders only consider physi-
cian-recorded diagnoses in the patient’s medical chart when 
assigning the codes, and it is highly unlikely that physi-
cians’ recorded diagnoses will be influenced by knowledge 
of the trial. Moreover, most patients admitted to hospital 
with major cardiovascular complications are admitted 
under a most responsible physician who is not their primary 
nephrologist.

Al-Jaishi et al 
11
Statistical Power
Power calculations for the primary composite outcome of this 
trial are based on a comparison of hazard rates (accounting 
for clustering).60 To inform these calculations, we conducted 
a historical analysis of 15 233 patients who received mainte-
nance hemodialysis at 84 Ontario centers in the 4-year period 
before the trial start date. Each center cared for a median of 
122 patients (range, 17-736) and contributed a median fol-
low-up of 258 person-years (range, 35-1524). During this 
period, the hazard rate for the composite outcome of cardio-
vascular-related death or hospitalization was 0.095 events per 
person-year (termed the baseline hazard). We used a coeffi-
cient of variation (the ratio of the between-cluster variance to 
the average baseline hazard rate) of 0.216, and a cluster har-
monic mean of the total person-time follow-up of 163 person-
years.60,61 Based on these data, our trial will have 80% power 
to detect a hazard rate reduction of at least 20% (correspond-
ing to a hazard ratio of 0.80; 2-sided α = 0.04; 0.04 chosen to 
control the family-wise error rate, see “Statistical Significance” 
section). Supplemental Appendix 11a shows the statistical 
power achieved for various hazard rate reductions, coeffi-
cients of variation, and annual baseline hazard rates for the 
primary composite outcome. We also confirmed our power 
calculations through computer simulations that took into 
account other complex aspects of the study design, including 
variable cluster sizes, censoring, and different patient follow-
up times (these analyses are detailed in Supplemental 
Appendix 11b and confirmed the trial will have 81% power 
for a 20% hazard rate reduction [or a hazard ratio of 0.80] in 
the primary composite outcome [2-sided α = 0.04]).62-65
Assumptions for power calculations for the key mecha-
nistic secondary outcome are presented in Supplemental 
Appendix 11c. We have 84% power to detect a 4 mm Hg 
between-group difference in the mean drop in systolic blood 
pressure with a 2-tailed α = 0.01 (0.01 was chosen to control 
the family-wise error rate, see “Statistical Significance” sec-
tion), a standard deviation of 7.2, 6 repeated observations, 
constant conservative intraclass correlation coefficient (ICC) 
of 0.4, and an average drop across all sites and periods of 28 
mm Hg.
Statistical Analysis Plan
Cohort creation and observation time. The study cohort will 
include outpatients receiving maintenance hemodialysis at a 
study center at any time between April 3, 2017, and March 
31, 2021. The patient’s cohort entry date is their index date. 
Patients will be analyzed according to their initial center’s 
random allocation on their index date (ie, all outcome events 
will be attributed to that center [the index center] according 
to the intention-to-treat principle); for new patients who ini-
tiate dialysis during the trial follow-up, the index center is 
the dialysis center where they entered the cohort. Patients 
will be followed for study outcomes until death or the trial 
end date on March 31, 2021. Based on emigration rates in 
Ontario, we estimate that 0.5% of patients will permanently 
leave the province every year during follow-up66 and approx-
imately 85% of patients’ observation time will be spent 
receiving maintenance hemodialysis at their index center (or 
at another center with the same random treatment alloca-
tion). The treatment allocation does not follow patients who 
transfer to another hemodialysis center during follow-up; 
rather patients follow the new center’s dialysate temperature 
protocol.
Analysis of adherence to the allocated temperature proto-
col. Prior to the analysis of the primary outcome, we will 
assess adherence to the allocated temperature protocol for 
each month during follow-up and overall for each arm of the 
trial. The adherence at the center level will be weighted by 
center size. We will also report the proportion of time patients 
spend on their index center’s treatment allocation.
Analysis of the primary outcome. Our analyses will account 
for the design and covariate-constrained randomization. In 
the primary intention-to-treat analyses, we will also assess 
the effect of the intervention on the rate of the composite 
outcome of cardiovascular-related death or hospitalization 
using the multivariable generalized-estimating-equations 
extension for the Cox proportional hazard model, with an 
exchangeable covariance matrix, to account for the cluster-
ing of individuals within hemodialysis centers.67,68 Patients 
will be censored at end of study follow-up or earlier if they 
die due to a non-cardiovascular-related cause. Patients who 
recover renal function, switch to a hemodialysis center with 
the alternative or no temperature allocation, receive a kidney 
transplant, or switch to home dialysis will be followed for 
outcomes according to their initial random allocation (see 
“Additional Exploratory Analyses” section).
Analysis of secondary outcomes. Between-group mean differ-
ences in the drop of mean systolic blood pressure is the key 
secondary outcome, because it examines the mechanism 
through which a lower dialysate temperature is expected to 
improve outcomes. This outcome will be analyzed at the cen-
ter level using a repeated-measures random-effects linear 
mixed model. This model will provide an estimate (with 99% 
confidence intervals—see “Statistical Significance” section) 
of the absolute mean difference in the intradialytic drop in 
systolic blood pressure between the 2 groups.
For the analysis of the other secondary outcomes, the 
same approach described for the primary outcome will be 
used to analyze each component individually (eg, cardiovas-
cular-related death, hospital admission with myocardial 
infarction) and the other secondary time-to-event composite 
outcomes (ie, all-cause mortality or cardiovascular-related 
hospitalization). Noncardiac death (except for outcomes that 
include all-cause mortality) will be treated as censoring 
events in these analyses. Model fit will be assessed to ensure 

12 
Canadian Journal of Kidney Health and Disease
that all assumptions are met (eg, proportional hazards). If 
proportional hazard assumption is violated, we will explore 
using a time-stratified Cox model.
Bayesian analysis of the primary outcome. We will assess the 
robustness of the primary findings (based on the classical 
frequentist analytic approach) to various assumptions about 
the use of prior information from various sources. As a sup-
plement, we will conduct and report a Bayesian analysis 
based on existing guidelines.69 Our aim is to determine the 
probability that the intervention (1) has any effect on the pri-
mary outcome and (2) reduces the hazard rate of the primary 
outcome by at least 5%, 10%, 15%, 20%, and 30% given the 
observed data. We considered a minimum 5% hazard rate 
reduction (ie, hazard ratio = 0.95) in the primary composite 
outcome as clinically relevant as it would translate to an esti-
mated 150 lives saved or major cardiovascular-related hospi-
talizations prevented every year in Canada.
We will explore several prior distributions (Table 3) that 
can condition the posterior distribution and provide insight 
about the sensitivity of the primary results. We are using pri-
ors to reflect varying degrees of enthusiasm and skepticism 
for the benefit of personalized dialysate temperature before 
the start of the MyTEMP trial. See Supplemental Appendix 
12 for more details.
Analysis of other outcomes. For the analysis of emergency 
department visits and all-cause hospitalizations (number of 
visits and length of stay), the incident rate ratio (visits/events 
per person-year) will be estimated using either Poisson 
regression or a negative binomial regression model (depend-
ing on the level of dispersion) accounting for within-center 
clustering. For the analysis of hospital encounters with major 
lower limb amputation and hospital encounters with major 
falls or fractures, the hazard ratio for time to first event will 
be estimated from a Cox model as described above for the 
primary outcome.
Additional exploratory analyses. We will perform several 
exploratory analyses to assess the robustness of the primary 
analysis. These analyses may include treating some events as 
competing rather than censoring events in follow-up and 
repeated events per patient (for the primary analysis)—see 
Supplemental Appendix 13 for more details.
In the literature, the credibility of subgroup effects is gener-
ally low, even when claims about the treatment effect made by 
the researchers are strong.70 We will visually examine the 
point estimate of the hazard ratio for the primary outcome with 
its accompanying 95% confidence intervals across subgroups 
for consistency of the effect. Two prespecified subgroups of 
interest, where a personalized dialysis temperature may have a 
larger treatment effect, are (1) patients with a baseline prior 
hospitalization with myocardial infarction, ischemic stroke, or 
congestive heart failure, and (2) incident patients, defined as 
new patients starting in-center hemodialysis during follow-up 
(based on historical data, approximately 9000 patients will 
start hemodialysis at a study center during the 4-year trial 
period).
Economic Analysis
We are designing a cost-effectiveness analysis that will 
model the costs and health outcomes for implementing a per-
sonalized dialysate temperature compared with a usual dialy-
sate temperature. The primary outcome is the incremental 
cost-effectiveness ratio (ICER), where the costs will be con-
sidered from the perspective of a universal health care sys-
tem and health outcome will be life-years.
We will use a multilevel model to allow for the correlation 
between costs and outcomes while accounting for clustering.71 
The results will produce an estimate of the incremental cost 
per month alive with an accompanying 95% confidence inter-
vals. We will supplement our base case analysis with a proba-
bilistic sensitivity analysis to quantify the level of confidence 
in relation to uncertainty in the model inputs (ie, relative treat-
ment effects, transition probabilities, costs, and outcomes). 
Details of this substudy are not included in this protocol.
Statistical Significance
In keeping with recommended practice, our aim is to avoid 
type I errors due to multiple comparisons.72-75 We will use the 
parallel gatekeeping procedure76 to control the overall family-
wise error rate at 0.05. The first family of hypotheses includes 
both the primary and key secondary hypotheses (composite 
primary outcome and drop in intradialytic systolic blood pres-
sure), with weights of 0.8 and 0.2, respectively. If the interven-
tion improves at least 1 of the 2 outcomes in the first family of 
hypotheses, outcomes in a second family of hypotheses will be 
tested in the following order at a level of significance that 
maintains the overall error rate across all prior testing at .05: 
all-cause mortality or cardiovascular-related hospitalization, 
all-cause mortality, hospital admission with myocardial infarc-
tion, hospital admission with congestive heart failure, and hos-
pital admission with ischemic stroke. Any reported confidence 
intervals that maintain the family-wise error rate at .05 will be 
adjusted for the tested level of significance.
The reporting of treatment effects on outcomes including 
secondary outcomes examined after the family-wise error 
rate exceeds 0.05, additional outcomes, prespecified and post 
hoc subgroup analyses, and exploratory analyses will be lim-
ited to point estimates with 95% confidence intervals (with-
out P values), and we will indicate that these interval widths 
are not adjusted for multiple testing, so that inferences drawn 
from them may not be reproducible.72,74
Discussion
This protocol describes the design and statistical analysis 
plan for MyTEMP, a pragmatic cluster randomized clinical 

13
Table 3. Characteristics of Reference Prior Probability Distributions Representing Prior Beliefs About Primary Composite Endpoint Benefit.
Prior belief
Assumed 
HR
Assumed SD 
of log HR
Pretrial probability of treatment effect greater than or 
equal to a specified HR threshold
Rationale for specifying distribution characteristics
<1.00
<0.95
<0.90
<0.85
<0.80
<0.70
Uninformativea
1.0
10
50%
50%
50%
49%
49%
49%
All possible values for treatment effect for log HR are equally 
likely
Strongly enthusiastic
0.8
0.1
99%
96%
88%
73%
50%
9%
Based on historic data from our data sources, the standard 
deviation is generally less than 0.1, and published observation 
studies have shown the intervention can have less than an HR 
of 0.838,39
Moderately enthusiastic
0.8
0.135
95%
90%
81%
67%
50%
16%
Probability of observing a treatment effect greater than that 
assumed in MyTEMP trial design (HR = 0.8) is 50%; probability 
of no benefit is 5%
Moderately skeptical
0.9
0.125
80%
67%
50%
32%
17%
2%
Probability of observing a treatment effect greater than an HR of 
0.90 is 50%; probability of any benefit is 80%
Skeptical
1.0
0.135
50%
35%
22%
11%
5%
0%
Probability of observing a treatment effect greater than that 
assumed in MyTEMP trial design (HR = 0.8) is 5%; probability of 
any benefit or harm is equivalent
Strongly skeptical
1.0
0.07
50%
23%
7%
1%
0%
0%
Probability of observing a treatment effect greater than that 
assumed in MyTEMP trial design is <5%; probability of any 
benefit or harm is equivalent
Note. HR = hazard ratio; MyTEMP = Major Outcomes with Personalized dialysate TEMPerature trial.
aAn uninformative prior assigns an equal probability to all possibilities of treatment effects.

14 
Canadian Journal of Kidney Health and Disease
trial currently running at 84 hemodialysis centers in Ontario. 
The trial will test the effect of randomizing hemodialysis 
centers to provide a personalized temperature-reduced dialy-
sate protocol vs a standard-temperature dialysate protocol 
(ie, 36.5°C) for 4 years on the rate of cardiovascular-related 
death or hospitalization in outpatients receiving maintenance 
hemodialysis.
This province-wide pragmatic trial will include outpa-
tients receiving maintenance in-center hemodialysis in par-
ticipating centers. The study population will include groups 
of patients who are commonly excluded from clinical trials, 
such as high-risk patients with multiple comorbidities and 
those with cognitive impairment or disabilities. By including 
patients from a variety of medical, ethnic, geographic, and 
socioeconomic backgrounds, the results of this trial should 
be broadly generalizable.
The setting of hemodialysis is well suited for trials that 
employ cluster randomization of interventions implemented 
at the individual level; patients typically receive all treat-
ments at the same center (3 times or more per week), and all 
patients in a given center receive care using a uniform set of 
standard operating procedures. We used a behavioral change 
framework to systematically identify barriers and facilitators 
to implementing a personalize dialysate temperature in 
hemodialysis centers.48 The results of this study informed 
our intervention implementation strategy, which should 
improve fidelity to the intervention and the trial’s internal 
validity. This approach to implementation also lends itself 
well to wide-scale uptake of the intervention, should it prove 
beneficial in this trial.
This study has some limitations. First, even in a random-
ized trial of 84 hemodialysis units, imbalance on unmeasured 
patient-level and center-level prognostic factors is possible. 
To protect against this, we used covariate-constrained ran-
domization to randomly select an allocation scheme from a 
list of acceptable allocations to ensure the 2 trial arms were 
balanced on baseline variables.
Second, we will have limited statistical power to detect a 
risk reduction below 20%, yet a risk reduction of even 5% 
(ie, hazard ratio of 0.95) could be clinically meaningful. In 
the absence of any harm, even a small risk reduction on the 
primary outcome would likely convince dialysis providers 
worldwide to adopt a personalized temperature-reduced dial-
ysate protocol as the standard of care. To address this limita-
tion, we will conduct a prespecified Bayesian analysis to 
examine the probability that the intervention is effective 
under differing thresholds that could be clinically relevant, in 
keeping with advice that investigators should prespecify 
both frequentist and Bayesian analyses as part of their statis-
tical analysis plan for randomized clinical trials.73,77-79
Third, our administrative data sources lack information 
on patient symptoms (eg, postdialysis fatigue, restless legs, 
discomfort from being cold on dialysis, changes in cogni-
tion). To address this, we will conduct a substudy to assess 
patient-reported symptoms in a subset of centers (details of 
this substudy are currently under development). Key caveats 
are the reliability of such measures in a setting where patients 
are aware of the dialysate temperature they receive, and lim-
ited statistical power to detect the minimal clinically relevant 
effects with a subset of centers.
Fourth, our data sources lack information on patients who 
were and were not adherent to the randomly allocated tem-
perature protocol. The observed effect of the intervention 
could be attenuated if (1) there is a high level of nonadher-
ence in either the control or intervention arms, (2) a large 
proportion of patients transfer to centers that have a different 
treatment allocation than their index center (ie, treatment 
contamination), or (3) the level of nonadherence is associ-
ated with the risk of intradialytic hypotension (eg, patients at 
high risk of experiencing intradialytic hypotension in the 
control arm are prescribed a cooler dialysate temperature). 
We will monitor and report adherence to the allocated ther-
apy during the trial, with a target between-group difference 
in the delivered dialysate temperature of 0.5°C.
Fifth, the primary data source that will be used to identify 
patients receiving maintenance in-center hemodialysis was 
not developed for research or clinical purposes, but rather to 
assess the funding and business needs of the Ontario Renal 
Network, which oversees the delivery of chronic kidney dis-
ease services in the province. As such, there is a possibility of 
including patients who temporarily switch to in-center hemo-
dialysis or who are not on chronic hemodialysis. To overcome 
this issue, we are using the 90-day rule to focus the analysis 
on stable patients who are receiving chronic hemodialysis 
(see “Hemodialysis Medical Centers and Patients” section). 
The cardiovascular outcomes used in this trial are well coded 
in administrative data when compared with adjudicated out-
comes in clinical trials of the general population.51-55
Sixth, we are testing a strategy of adopting a personalized 
dialysate temperature protocol for all patients treated in a 
hemodialysis center. As such, we will not be able to comment 
on the treatment effect of personalized dialysate temperature 
in patients at high risk of intradialytic hypotension. An indi-
vidual patient RCT, with more restrictive eligibility criteria, 
would be a better design to address the latter objective.
Trial Oversight
An independent Data and Safety Monitoring Board (DSMB) 
has convened and continues to assess the progress of this 
trial and the safety data from published literature. After each 
meeting, the DSMB provides recommendations to the study 
team. The main responsibilities of the DSMB are listed in 
Supplemental Appendix 14.
Conclusion
Lowering the dialysate temperature between 0.5°C and 0.9°C 
below a patient’s predialysis temperature may stabilize intradia-
lytic blood pressure, reduce the risk of intradialytic hypotension, 

Al-Jaishi et al 
15
protect the heart and brain from cumulative subclinical ischemic 
injury, and improve cardiovascular outcomes. This Ontario-
wide clinical trial will determine the effect of randomizing 
hemodialysis centers to provide a personalized temperature-
reduced-dialysate protocol vs a standard-temperature dialysate 
protocol on the rate of cardiovascular-related death or hospital-
ization. The intervention will be embedded into routine clinical 
care and patient characteristics, and outcomes will be largely 
obtained from administrative health care databases. This prag-
matic design will allow broad inclusion of dialysis centers and a 
large, representative sample of patients that should yield highly 
generalizable results. If effective in reducing cardiovascular-
related death or hospitalization, a personalized dialysate tem-
perature can be scaled and delivered on all hemodialysis 
machine in Ontario (and worldwide) at no added cost.
Authors’ Note
The analyses, conclusions, opinions and statements expressed 
herein are solely those of the authors and do not reflect those of the 
funding or data sources; no endorsement is intended or should be 
inferred.
Acknowledgments
We thank all the participating research sites and the time and sup-
port from patients, local collaborators, nurse educators, hemodial-
ysis unit managers, nurses, clerical staff, and members of the data 
safety monitoring board. We thank Mr Sal Treesh for sharing his 
knowledge about the operation of hemodialysis machines. We are 
also grateful for the support of Mr Jordan Ward who assisted in 
trial site activation, day-to-day management of the trial for a por-
tion of the study follow-up, as well as researched information per-
taining to the functionality of dialysis machines. This study was 
supported by Institute for Clinical Evaluative Sciences (ICES), 
which is funded by an annual grant from the Ontario Ministry of 
Health and Long-Term Care (MOHLTC). Parts of this material are 
based on data and information compiled and provided by the 
Canadian Institute for Health Information (CIHI), Cancer Care 
Ontario, and MOHLTC, and Ontario Registrar General informa-
tion on deaths, the original source of which is ServiceOntario. The 
ICES Kidney, Dialysis, and Transplantation Program conducted 
this project. We thank members of the Data Safety Monitoring 
Board of this trial: Dr Lehana Thabane (Chair) and Dr John 
Eikelboom from McMaster University, and Dr Clara Bohm from 
the University of Manitoba.
Ethics Approval and Consent to Participate
The Health Sciences Research Ethics Board at Western University 
centrally approved the research ethics application for Ontario 
through the Streamlined Research Ethics Review System man-
aged by Clinical Trials Ontario (Application Number: CTO-
0736). The Research Ethics Board approved our application with 
alteration to the informed consent process as described in the 
“Ethical Considerations” section of this manuscript.
Consent for Publication
Consent for publication was obtained from all authors.
Availability of Data and Materials
The data sets from this study are held securely in coded form at 
Institute for Clinical Evaluative Sciences (ICES). While data shar-
ing agreements prohibit ICES from making the data publicly avail-
able, access may be granted to those who meet prespecified criteria 
for confidential access, available at www.ices.on.ca/DAS. The full 
dataset creation plan and underlying analytic code are available 
from the authors upon request, understanding that the programs 
may rely upon coding templates or macros that are unique to ICES.
Declaration of Conflicting Interests
The author(s) declared the following potential conflicts of interest 
with respect to the research, authorship, and/or publication of this 
article: Dr Zager is the Medical Director for Dialysis Clinic Inc, 
which provided partial funding for Major Outcomes with 
Personalized Dialysate TEMPerature (MyTEMP). Dr Wald has 
received unrestricted research support from Baxter Healthcare. The 
remaining authors declare they have no other relevant interests.
Funding
The author(s) disclosed receipt of the following financial support for 
the research, authorship, and/or publication of this article: We 
received funding for Major Outcomes with Personalized Dialysate 
TEMPerature (MyTEMP) from the Lawson Health Research 
Institute, the Ontario Renal Network, Dialysis Clinic Inc, the Heart 
and Stroke Foundation of Canada, and the Canadian Institutes of 
Health Research (CIHR). Funding is also provided by the Ontario 
Strategy for Patient-Oriented Research SUPPORT Unit, which is 
supported by the CIHR and the Province of Ontario. Mr Ahmed 
Al-Jaishi is supported by the Allied Health Doctoral Fellowship from 
the Kidney Foundation of Canada, Michael DeGroote Scholarship, 
and CIHR Doctoral Award. Dr Stephanie Dixon’s research is sup-
ported by a CIHR innovative Clinical Trials award. Dr Chris McIntyre 
is supported by the Dr Robert Lindsay Chair of Dialysis Research and 
Innovation. Dr P. J. Devereaux holds a Tier 1 Canada Research 
Chair. Dr Jeremy Grimshaw holds a Canada Research Chair in Health 
Knowledge Transfer and Uptake. Dr Amit Garg is supported by the 
Dr Adam Linton Chair in Kidney Health Analytics and a Clinician 
Investigator Award from the CIHR.
ORCID iDs
Ahmed A. Al-Jaishi 
 https://orcid.org/0000-0003-0376-2214
Manish M. Sood 
 https://orcid.org/0000-0002-9146-2344
Ron Wald 
 https://orcid.org/0000-0003-4411-8169
Amit X. Garg 
 https://orcid.org/0000-0003-3398-3114
Supplemental Material
Supplemental material for this article is available online.
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Appendices 
 
 

Appendix 1: Detailed Background and Rationale for the MyTEMP Trial 
Maintenance hemodialysis provides a life-saving treatment for persons whose kidneys have 
permanently failed (approximately 3 million worldwide and 23,000 in Canada).1,2 However, over 400,000 
individuals worldwide (2,500 persons in Canada) are admitted to hospital- or die from a major 
cardiovascular-related event each year.3–5  
In most hemodialysis centers, the default dialysate temperature setting is in the range of 36.5 °C to 37.0 
°C. Lowering the dialysate temperature below a patient’s core body temperature (such a value of 35 °C 
to 36 °C) is a promising intervention that has the potential to reduce the risk of cardiovascular-related 
mortality and major adverse cardiovascular events.6–8 Lowering the dialysate temperature stabilizes 
intradialytic blood pressure and decreases the risk of experiencing hypotensive events during 
hemodialysis treatments9 – experiencing frequent hypotensive events during hemodialysis is associated 
with a greater risk of all-cause mortality and cardiovascular events.10 
 
1.1 Physiology of intradialytic hypotension 
There is evidence showing hemodialysis itself injures the heart, brain, and other vital organs through 
repeated episodes of intradialytic hypotension and subclinical ischemia.10–17 Most intradialytic 
hypotensive events are attributed to the ultrafiltration that occurs during dialysis and an inadequate 
cardiovascular compensation to replace the loss in blood volume.18 When fluid is removed from the 
body during hemodialysis, systolic blood pressure often drops by an average of 20 mmHg to 30 mmHg 
and diastolic blood pressure drops by 7 mmHg to 10 mmHg.10,19 The normal physiological response to 
reductions in blood volume for healthy individuals is an increase of peripheral vascular resistance, an 
increase in the heart stroke volume, and/or a faster heart rate. Healthy individuals can tolerate up to a 
20% loss in circulating blood volume before they experience hypotension.20,21 However, many patients 

on hemodialysis are unable to mount the response seen in  healthy persons, and hypotension occurs 
with a smaller decline in blood volume.22 This inability to mount a normal response has been partly 
attributed to impairment in myocardial contractile reserve due to cardiomyopathy.23,24  Beyond 
ultrafiltration, there are multiple patient and dialysis-associated factors that contribute to intra-dialytic 
hypotension including poor sympathetic responsiveness,25 poor cardiac function,26,27 older age (possibly 
related to increasing comorbid conditions),28 medication use (e.g. use of anti-hypertensive agents),29 
body heating,30–32 release of vasodilator agents,33,34 and osmolar and electrolyte changes.35–38 
Large drops (greater than 20 mmHg) in blood pressure complicate up to 50% of hemodialysis sessions.22 
Intradialytic hypotension increases the risk of coronary hypoperfusion that can lead to myocardial 
stunning,39,40 which is associated with left ventricular dysfunction.13,15,41–43 When the left ventricle starts 
losing its ability to pump blood, the heart’s compensatory mechanisms further loses the ability to 
compensate for the loss in blood volume during ultrafiltration – possibly leading to further hypotensive 
events and the damage of vital organs. Over time, the cumulative effect of intra-dialytic hypotensive 
events – each time resulting in small ischemic insults – may lead to a higher risk of major adverse 
cardiovascular events and cardiovascular-related death.12,19,44  
 
1.2 Physiologic effects of reduced dialysate temperature 
One strategy to help stabilize blood pressure during hemodialysis is to reduce the temperature of the 
dialysate. A cooler dialysate temperature increases peripheral vascular resistance, improves cardiac 
function, and alters the level of vasoactive peptides — all which may stabilize blood pressure.30,32,45–50 10 
The measures used to described  blood pressure differences between cooler dialysate temperature 
(≤35.5 °C) vs. a standard dialysate temperature (≥36.0 °C) in prior individual level RCTs has not been 
consistent; with some reporting mean intra-dialytic systolic blood pressure, nadir intra-dialytic systolic 

blood pressure, and pre- and post-dialysis blood pressure. Nevertheless, these studies reported with a 
cooler compared to standard dialysate temperature there was a: (i) higher nadir systolic blood pressure; 
(ii) a smaller drop in post-dialysis from pre-dialysis blood pressure; and (3) a smaller drop in nadir intra-
dialytic from pre-dialysis blood pressure - (eTable 1).40,47,51–58 
Compared to a dialysate temperature of 37 °C, personalized dialysate temperature (0.5 °C below pre-
dialysis core body temperature) over a 12-month period reduced injury to both the brain and heart. In 
the brain, temperature-reduced hemodialysis protected patients against white matter changes as a 
result of less injuries to cerebral vascular beds.13 In the heart, temperature-reduced hemodialysis 
resulted in positive (but not statistically significant) changes in resting ejection fraction, however, there 
was a statistically significant reductions in both left ventricular mass and left ventricular end-diastolic 
volumes, and aortic distensibility was preserved.15 A cardio- and neuro-protective effect of cooler 
dialysate temperature may operate through several mechanisms beyond stabilizing blood pressure and 
reducing the risk of intra-dialytic hypotension. Other mechanisms may include: lowering cell 
metabolism, reducing the likelihood of experiencing calcium overload, reducing  inflammatory factors, 
and increasing anti-apoptotic factors.59–62 
 
1.3 Clinical effects of reduced dialysate temperature 
We conducted a systematic review and meta-analysis that identified 26 randomized controlled trials 
(total 484 patients) investigating the effect of cooler dialysate temperature compared to a standard 
temperature. Most of the trials enrolled less than 30 patients and only three trials followed patients for 
longer than six sessions.47,54,63 In this review, temperature-reduced hemodialysis (34-35.5 °C) compared 
to control (where in different jurisdictions ranged from 36 °C to 38.5 °C), reduced the rate of intra-
dialytic hypotension by 70% (95% CI: 49% to 89%). The intra-dialytic mean arterial pressure increased by 

an average of 12 mmHg (95% CI: 8 to 16 mmHg) for temperature-reduced hemodialysis compared to 
standard temperature hemodialysis, and several studies reported a smaller reduction in average intra-
dialytic nadir and post-dialysis systolic blood pressure compared with pre-dialysis blood pressure 
reading.9,51–53 The of risk adverse events was not statistically different compared with standard dialysate 
temperature. However, these results should be interpreted with caution as the methodological quality 
of the 26 trials was rated as low to very low using GRADE criteria (Grading of Recommendations 
Assessment, Development and Evaluation criteria).64,65  
Observational studies have reported inconsistent results with regards to the effect of temperature-
reduced hemodialysis on mortality in comparison to the control temperature. Hsu et al.66 found the use 
of cooler dialysate temperature (<35.5 °C) was associated with a 35% lower risk of cardiac mortality and 
25% lower risk of all-cause mortality compared to patients that used a dialysate temperature between 
35.5 and 37 °C. Similarly, data on 8807 patients from 232 hemodialysis facilities across 12 countries in 
the Dialysis Outcomes and Practice Patterns Study (DOPPS) Phase 4 (2009-2012) showed cool dialysate 
was associated with a 24% reduction in the risk of cardiovascular-related mortality (HR=0.76; 99% CI: 
58%-98%), but was not associated with an altered risk of all-cause hospitalization (HR=1.12; 99% CI 0.98-
1.27), all-cause mortality (HR=1.04; 99% CI 0.87-1.24), or major cardiovascular events (HR=0.94; 99% CI 
0.80-1.11).67 In a study comparing outcomes of cool dialysate at a temperature of 36 °C (n=313 patients) 
with matched-control patients with a dialysate temperature of 37 °C (n=1565), Gray et al.68 found no 
difference in the risk of hospitalization (incidence rate ratio [IRR]=1.10; 95% CI 0.94-1.29) or all-cause 
mortality (IRR=1.09; 95% CI 0.77-1.53).  
Some have suggested that a cooler dialysate temperature may reduce uremic toxin removal compared 
to a warmer dialysate temperature; however, this was not supported in our systematic review above 
when all prior studies were considered.9 As well, other studies investigating the effect of a cooler 
dialysate on urea removal found that urea-based dialysis adequacy is largely unaffected by dialysate 

temperature.9,57,69,70  However, others have suggested urea removal is not a good marker for toxin 
removal because of its small size and generally negligible inter-compartmental resistance.71 There is an 
ongoing clinical study of 14 patients that aims to compare toxin removal for patients on cool and warm 
dialysate for both small and large-sized toxins.71 Of note, if a cooler dialysis temperature enables a 
patient to receive more dialysis or more ultrafiltration than they would otherwise receive with a warmer 
dialysis temperature (e.g. dialysis treatments are stopped early for reasons of intra-dialytic hypotension 
or cramping) this would increase uremic toxin removal.   
 
1.4 What is the dialysate temperature used in current practice? 
Currently, the dialysate temperature used in most centres in Canada and the United States ranges from 
36.5 °C to 36.7 °C (97.7°F to 98.1°F). In preparation for the MyTEMP trial, we collected data on the 
prescribed dialysate temperature and patients' pre-dialysis body temperatures for 12,012 hemodialysis 
sessions across 68 unique hemodialysis centres in Ontario over a six-month period (September 2016 to 
March 2017). Results are reported as the median (25th, 75th percentile). We confirmed the delivered 
dialysis temperature during this period was fixed for each dialysis session except for 5 of the 68 
hemodialysis centres that used blood temperature monitoring. The prescribed dialysate temperature 
was 36.5 °C or 97.7 °F (36 °C [96.8 °F], 36.5 °C [97.7 °F]). The pre-dialysis body temperature was 36.3 °C 
or 97.3 °F (35.9 °C [96.6 °F], 36.6 °C [97.9 °F]) and 59% of hemodialysis sessions started with a pre-
dialysis body temperature (measured using oral or tympanic instruments) less than 36.5 °C (97.7 °F). The 
difference between the pre-dialysis body temperature and prescribed dialysate temperature was 0.0 °C 
(0.3 °C lower, 0.4 °C higher than body temperature).  
In the United States, it has been estimated that the average delivered dialysate temperature is 36.7 °C 
(98.1 ºF).72  The prescribed dialysate temperature of 36.5 °C (97.7 °F) used by most nephrologists comes 

from clinical tradition rather than empirical evidence; with the historic rationale that dialysate 
temperature should be similar to typical body temperature.  
 
1.5 How is the dialysate temperature set and maintained?  
There are several types of hemodialysis mechanisms that control the dialysate temperature. These 
methods include fixed, programmed, isothermic, thermoneutral, and negative energy hemodialysis 
prescriptions.6 The fixed method uses a single non-variable dialysate temperature that is prescribed 
throughout a patient’s hemodialysis session. The latter four methods use blood temperature monitoring 
to make constant adjustments to the dialysate temperature during hemodialysis in response to the 
measured body temperature. 
The fixed dialysate temperature prescription is currently the most common prescription method used in 
Ontario and likely worldwide. All hemodialysis machines have the mechanisms and software to achieve 
a fixed dialysis temperature, which makes this method of temperature control popular. To set a fixed 
dialysate temperature, a physician or nurse practitioner prescribes a specific temperature for a patient’s 
hemodialysis treatment, and a dialysis nurse programs the fixed temperature into the hemodialysis 
machine.  The nurse monitors the patient during the treatment, and some have the authority to alter 
the dialysate temperature during the treatment according to the patient’s symptoms (e.g. temperature 
may be adjusted as per patient’s condition). 
In Ontario, the most commonly used dialysis machines are the Fresenius 5008 and the Baxter Artis. 
Purified water enters the machine through an inlet valve at a temperature between 5 °C and 30 °C. 
Then, the purified water passes through a passive heat exchanger where the spent dialysate that passed 
through the dialyzer passively heats the incoming purified water entering the hemodialysis machine.  
The purified water is then further heated by a heating element at a power correlated to the fixed 

dialysate temperature.  The heated water is combined with bicarbonate and acid to form the base of the 
dialysate. 
A temperature sensor measures the dialysate temperature to determine if it is equivalent to the 
programmed dialysate temperature. The communication between the dialysate temperature sensor and 
the heating element is in a constant feedback loop throughout the hemodialysis session to maintain the 
programmed dialysate temperature. The temperature sensor in the above-mentioned machines 
measures the temperature of the water leaving the heater assembly and controls the heater to ensure 
that the: (a) temperature is within operating range; (b) maximum temperature deviation is within 
acceptable range; and (c) response time is within acceptable range. The Fresenius 5008 and Baxter Artis 
machines have different temperature circuit specification as shown in eTable 2. 
Continuous monitoring of the dialysate fluid temperature is monitored by the protection system 
throughout the treatment session (eFigure 1). If the dialysate temperature cannot be maintained within 
the allowable operating and accuracy range (as specified in eTable 2) due to a failure in the temperature 
circuit, for patient safety an alarm is activated to warn the nurse and the bypass function is activated for 
the patient’s blood to bypass the dialyzer.    
 
1.6 How does body temperature change in response to the dialysate temperature? 
In general, human body temperature is maintained within a narrow range. Several studies show that 
during conventional hemodialysis with the dialysate temperature set at 36.5 to 37 °C, temperature can 
increase by 0.1 to 0.9 °C at various parts of the body, including the arterial fistula line, oral cavity, and 
skin surface.73  In the skin, decreases in body temperature as small as 0.3 °C can alter vascular tone; 
whereas, reductions in skin temperature of 0.8 °C associates with symptoms of shivering.73 Using 
historic data from 4407 sessions, eTable 3 shows as the dialysate temperature becomes cooler, the 

post-hemodialysis body temperature decreases after accounting for pre-hemodialysis body 
temperature.  
 
Effects of temperature-reduced dialysis on patient symptoms. Some patients may experience feeling 
cold when using temperature-reduced hemodialysis.9,40 In MyTEMP, we are personalizing the dialysate 
temperature for each patient, rather than using a single fixed cool temperature for all patients. In turn, 
this may improve tolerability for more patients. In a previous study, most patients using fixed 
temperature-reduced hemodialysis of 35 °C reported positive views of their experience and wanted to 
continue using the cooler temperature after study completion.7,74 Patients also reported perceived 
benefits such as having more energy, better cognition, less post-hemodialysis fatigue, and a quicker time 
to recovery after their hemodialysis session.9,74–76  
 
1.7 The need for large multi-centre trials of temperature-reduced hemodialysis 
Many in the nephrology community have called for large-scale testing of temperature reduced 
dialysis.8,9,77 Current trials of temperature-reduced hemodialysis registered on clinicaltrials.gov have 
fewer than 150 patients and none of the prior or current studies investigate major outcomes when a 
hemodialysis facility changes its protocol from a standard hemodialysis dialysate temperature of ≥36.5 
°C to personalized temperature-reduced hemodialysis. To inform clinical practice change, we need 
evidence from at least one large, pragmatic, high-quality, multi-centre randomized controlled trial (that 
is generalizable to most hemodialysis centres) and has adequate statistical power to detect a meaningful 
change in the rates of major outcomes.  
 
 
 

eFigure 1: Purified water (light blue) enters the hemodialysis machine where it passes through a passive 
heat exchanger.  Spent dialysate (yellow) after leaving the dialyzer passively heats the purified water 
entering the hemodialysis machine (light red).  The purified water is further heated by a heating element 
at a power that will raise the fresh dialysate to the desired programmed temperature (red).  The heated 
water is combined with bicarbonate and acid to form the base of dialysate (green).  A temperature 
sensor is used to measure the dialysate temperature to determine if it is equivalent to the programmed 
dialysate temperature.  The temperature sensor will communicate with the heating element (by 
switching on or off) to achieve the programmed temperature. 
 

eTable 1: Summary of systolic blood pressure measures in previous randomized controlled trials 
Reference 
N 
Patients 
Dialysate temperature 
Blood Pressure Measures ¥ 
Cooler dialysate temperature 
Standard dialysate temperature 
Beerenhout 
2004 51 
12 
Fixed temperature 35.5 °C; 
duration: one session 
 
Vs 
 
Fixed temperature 36.0 °C; 
duration: one session 
Change in SBP: -6 ± 2 mmHg 
Change in SBP: -0.8 ± 22.7 mmHg 
Beerenhout 
2004 52 
12 
BTM (mean dialysate 
temperature 35.2 °C); 
duration: one session 
 
Vs 
 
Fixed temperature 37.5 °C; 
duration: one session 
SBP pre-dialysis: 146 ± 5 mmHg 
SBP post-dialysis: 140 ± 6 mmHg 
SBP pre-dialysis: 150 ± 5 mmHg 
SBP post-dialysis: 132 ± 4 mmHg 
Chesterton 
2009 53 
10 
Fixed temperature 35 °C; 
duration: one session 
 
Vs 
 
Fixed temperature 37 °C; 
duration: one session 
Percent change in SBP: 2.71% 
above baseline ± 0.97% 
Percent change SBP: 7.54% below 
baseline ± 1.92% 
Cruz 1999 
54 
19 
Fixed temperature 35.5 °C; 
duration: nine sessions 
SBP pre-dialysis: 132 ± 3.3 mmHg 
Nadir SBP: 103 ± 2.9 mmHg 
SBP pre-dialysis: 132.7 ± 3.4 mmHg 
Nadir SBP: 90.6 ± 2.5 mmHg 

 
Vs 
 
Fixed temperature 37 °C; 
duration: nine sessions 
SBP post-dialysis: 118 ± 3.5 mmHg 
SBP post-dialysis: 109.0 ± 2.1 mmHg 
Maggiore 
2002 47 
95 
BTM isothermic; duration: 
12 sessions on average 
 
Vs 
 
BTM thermoneutral; 
duration: 12 sessions on 
average 
Change in SBP between post- and 
pre-dialysis readings: -14 ± 17 
mmHg 
 
* Post-dialysis SBP was 14 mmHg 
below pre-dialysis SBP 
Change in SBP between post- and 
pre-dialysis readings: -21 ± 16 
mmHg 
 
* Post-dialysis SBP was 21 mmHg 
below pre-dialysis SBP 
Parker 
2007 55 
7 
Fixed temperature 35 °C; 
duration: one session 
 
Vs 
 
Fixed temperature 37 °C; 
duration: one session 
Intra-dialytic SBP: 137 ± 11.4 
mmHg 
Intra-dialytic SBP: 130.7 ± 11.4 
mmHg 
Selby 2006 
40 
10 
Fixed temperature 35 °C; 
duration: one session 
 
Vs 
 
Fixed temperature 37 °C; 
duration: one session 
Intra-dialytic SBP: 159 ± 14 mmHg 
Intra-dialytic SBP: 142 ± 17 mmHg 
 
 
 

van der 
Sande 1999 
56 
9 
Fixed temperature 35.5 °C; 
duration: one session 
 
Vs 
 
Fixed temperature 37 °C; 
duration: one session 
SBP pre-dialysis: 130 ± 22 mmHg 
Max ↓ in SBP: 21.8 ± 26.1 mmHg 
SBP post-dialysis: 132 ± 21 mmHg 
SBP pre-dialysis: 144 ± 26 mmHg 
Max ↓ in SBP: 43 ± 20.6 mmHg 
SBP post-dialysis: 117 ± 26 mmHg 
Kaufman 
1998 57 
17 
BTM isothermic; BTM 
cooling 0.5 °C below body 
temperature; duration: 1.5 
sessions on average 
 
Vs 
 
BTM thermoneutral; 
duration: 1.5  sessions on 
average 
SBP pre-dialysis: 159 ± 35 mmHg 
Nadir SBP: 113 ± 30 mmHg 
SBP post-dialysis: 127 ± 39 mmHg 
SBP pre-dialysis: 151 ± 27 mmHg 
Nadir SBP: 104 ± 27 mmHg 
SBP post-dialysis: 122 ± 28 mmHg 
Zitt 2008 58 
17 
Fixed temperature 35 °C; 
duration: not clear 
 
Vs 
 
Fixed temperature 37 °C; 
duration: not clear 
SBP pre-dialysis: 127 ± 6.4 mmHg 
SBP post-dialysis: 134 ± 3.9 mmHg 
SBP pre-dialysis: 126 ± 4.6 mmHg 
SBP post-dialysis: 127 ± 2.1 mmHg 
SBP=systolic blood pressure (mean ± standard deviation); Max ↓ in SBP: Maximum drop in intradialytic SBP (difference between pre-dialysis and nadir intra-
dialytic SBP); Intra-dialytic SBP: Mean intradialytic SBP during the hemodialysis session;  
¥ Information presented is Mean ± SD 

eTable 2: Default temperature circuit specification for the Fresenius 5008 and Baxter Artis hemodialysis 
machines.  
 
Machine 
Fresenius 5008 
Baxter Artis 
Dialysate temperature range 
+34 °C to +39 °C 
+35 °C to +39.5 °C 
Accuracy** 
+0.2 °C/-0.5 °C of the set value 
+0.5 °C/-1.8 °C of the set value 
Resolution++ 
0.5 °C 
0.5 °C (0.1 °C is possible) 
 
**Accuracy of the delivered dialysate temperature compared to the programmed dialysate temperature. 
++ The resolution (increments) at which the dialysate temperature can be programmed on the machine. 
 
 
 
 

eTable 3: Change in body temperature by different levels of dialysate temperature using historic data from 4407 sessions. Patient body 
temperatures were measured using tympanic thermometers. 
 
Dialysate  is: 
Dialysate 
Temperature 
Arrival 
Temperature 
(Pre-dialysis) 
Departure 
Temperature 
(Post-dialysis) 
Change in Body 
Temperature** 
At least 1 °C above body 
temperature 
37 (36.5, 37.5) 
35.8 (35.5, 35.9) 
36.3 (36.1, 36.5) 
0.7 (0.4, 1) 
0.5 to 0.99 °C above body 
temperature 
36.5 (36.5, 36.5) 
36 (36, 36) 
36.3 (36.1, 36.5) 
0.3 (0.1, 0.6) 
0.01 to 0.49 °C above body 
temperature 
36.5 (36.5, 36.5) 
36.3 (36.2, 36.4) 
36.4 (36.3, 36.6) 
0.2 (0, 0.4) 
Equal to body temperature 
36.5 (36.5, 36.5) 
36.5 (36.5, 36.5) 
36.5 (36.3, 36.7) 
0.1 (-0.1, 0.2) 
0.01 to 0.49 °C below body 
temperature 
36.5 (36, 36.5) 
36.6 (36.4, 36.7) 
36.6 (36.4, 36.7) 
0 (-0.2, 0.2) 
0.5 to 0.99 °C below body 
temperature 
36 (36, 36) 
36.6 (36.6, 36.8) 
36.5 (36.4, 36.7) 
-0.1 (-0.3, 0.1) 
At least 1 °C below 
temperature 
35.5 (35.5, 36) 
36.7 (36.6, 37) 
36.5 (36.4, 36.7) 
-0.2 (-0.5, 0) 
** Change in Body Temperature refers to the difference in each patients’ arrival from departure temperature. A positive number means the departure 
temperature greater (i.e., warmer) than the arrival temperature.  
Columns are presented as median (25th, 75th percentile). 
To convert from °C to °F, use the formula: (Temperature °C × 1.8) + 32;  

Appendix 2: Patient Temperature and setting of the dialysate temperature for the intervention group. 
Centres that have dialysis machines able to change by increments of 0.1°C 
 
Patient 
Temperature* (°C) 
Dialysate 
Temperature (°C) 
37.5 and greater 
36.5 (or standard 
centre protocol) 
37.4 
36.5 
37.3 
36.5 
37.2 
36.5 
37.1 
36.5 
37 
36.5 
36.9 
36.4 
36.8 
36.3 
36.7 
36.2 
36.6 
36.1 
36.5 
36 
36.4 
35.9 
36.3 
35.8 
36.2 
35.7 
36.1 
35.6 
36 and less 
35.5 (or standard 
centre protocol) 
 
When to measure patient temperature: before starting the dialysis session using your standard thermometer. 
If temperature out of ordinary (e.g. patient consuming cool/warm beverage or just came from the cold outside in 
the winter), then: please start the patient on a reasonable dialysate temperature and re-check the body 
temperature in a few minutes. 
 
 

Appendix 3: Patient Temperature and setting of the dialysate temperature for the intervention group. 
Centres that have hemodialysis machines able to change by increments of 0.5 °C 
 
Patient 
Temperature* (°C) 
Dialysate 
Temperature (°C) 
37.5 and greater 
36.5 (or standard 
centre protocol) 
37.4 
36.5 
37.3 
36.5 
37.2 
36.5 
37.1 
36.5 
37 
36.5 
36.9 
36 
36.8 
36 
36.7 
36 
36.6 
36 
36.5 
36 
36.4 
35.5 
36.3 
35.5 
36.2 
35.5 
36.1 
35.5  
36 and less 
35.5 (or standard 
centre protocol) 
 
When to measure patient temperature: before starting the dialysis session using your standard thermometer. 
If temperature out of ordinary (e.g. patient consuming cool/warm beverage or just came from the cold outside in 
the winter), then: please start the patient on a reasonable dialysate temperature and re-check the body 
temperature in a few minutes. 
 
 

Appendix 4: Sampling accuracy for overall centre adherence 
For eight centres, we had access to the full patient data on adherence to the allocated temperature 
protocol (5 centres in the control and 3 centres in the intervention arm). We sampled 15 patients 1000 
times from each centre and compared the sampled adherence to the true adherence for all patients 
within the respective centre. The sampled adherence was within 10% of the true adherence 
approximately 50% to 90% of the time.  The sampled adherence was within 20% of the true adherence 
over 80% of the time for all centres. We found as the true centre adherence increased towards 100%, so 
did the accuracy of our estimated sample adherence. 
 

Appendix 5: 
MyTEMP met the necessary criteria for alteration to the patient consent process as outlined in the TCPS-
2 Statement: (i) the research poses a clear benefit to society and was unlikely to adversely affect patient 
welfare; (ii) the intervention was considered to be of minimal risk to patients (similar to a quality-control 
measure that could be implemented by a dialysis centre director); (iii) an informed consent model is 
impossible and impracticable given our research design and resources (e.g. a source of bias if patients in 
a hemodialysis centre randomly allocated to personalized temperature are less likely to consent to trial 
participation [a change compared to their historic dialysate prescription] compared to patients in a 
hemodialysis centre randomly allocated to control arm [where there is no change from what they have 
historically received]); and (iv) there is a plan to provide a debriefing which also offers patients the 
possibility of refusing the intervention.78  
 
 

Appendix 6: Common data sources used for population-based studies  
 
Database (Source) 
Description 
Key Data Variables 
Health Services 
Discharge Abstract 
Database  (CIHI) 
Hospital discharge abstracts for acute, chronic 
and rehabilitative care (1988 onward) 
Diagnoses; Procedures; 
Comorbidities; Length 
of Stay  
National Ambulatory Care 
Reporting System (CIHI) 
ED visits, same day surgery, outpatient clinics 
(e.g., dialysis, cancer clinics) (2002 onward) 
Reason for visit; Triage 
level; Interventions; 
Mode of arrival 
Ontario Drug Benefit 
Database (MOHLTC) 
Claims for prescribed drugs covered by the 
Ontario Drug Formulary for adults aged 65+ and 
those receiving social assistance (1990 onward) 
Drug ID number; Drug 
quantity; Cost  
Ontario Health Insurance 
Plan (MOHLTC) 
Reimbursement claims made by fee-for-service 
physicians and community-based labs (1991 
onward) 
Service provided; 
Diagnosis codes; Fee 
paid; Physician 
specialty 
 
Registry 
 
Canadian Organ 
Replacement Register 
(CIHI) 
Collects and records the incidence, prevalence, 
treatment changes, and outcomes of all chronic 
dialysis and solid organ transplant patients in 
Canada. Data is collected by voluntary 
completion of survey forms for each patient at 
dialysis initiation and at yearly follow-up (2001 
onward) 
Hemodialysis start; 
vascular access use; 
nephrology referral; 
comorbid and baseline 
conditions 
Ontario Renal Reporting 
System 
Collects and records the incidence, prevalence, 
treatment changes, and outcomes of all chronic 
dialysis and solid organ transplant patients in 
Canada. Data is collected is mandated by the 
Ontario Renal Network for each patient at 
dialysis initiation and at yearly follow-up (2010 
onward) 
Hemodialysis start; 
vascular access use; 
nephrology referral; 
comorbid and baseline 
conditions 
Population and Demographics 
Registered Persons 
Database  (MOHLTC) 
Basic demographic information about all 
Ontarians that ever had an Ontario Health Card 
Number. (1990 onward) 
Date of birth; Date of 
death; Sex; Geographic 
information  

Office of the Registrar 
General- Deaths (ORGD) 
ORGD is an annual dataset containing 
information on all deaths registered in Ontario 
starting on January 1st, 1990. 
Information on cause 
Note: Information on 
cause of death lags 
other variables by ~2 
years. 
Care Providers 
ICES Physicians Database  
This data set contains yearly information about 
all physicians in Ontario (1992 onward) 
Annual demographics; 
Specialization; 
Workload  
Laboratory Datasets 
Ontario Laboratories 
Information System 
(pending linkage) 
OLIS is a cornerstone information system that 
connects hospitals, community laboratories, 
public health laboratories and practitioners to 
facilitate the secure electronic exchange of 
laboratory test orders and results. ICES has 
signed and currently executing a Data Sharing 
Agreement to link Ontario-wide laboratory 
results to the Ontario-wide data holdings 
housed at ICES. 
Creatinine levels, lipid 
panels, urine protein 
Outpatient, emergency 
room and inpatient 
values. 
MOHTC: Ministry of Health and Long-term Care, CIHI – Canadian Institutes for Health Information

Appendix 7: List of 78 variables baseline variables 
 
Medical history of the following 
 Databases 
 
Demographic 
   
Age 
 RPDB 
Sex 
 RPDB 
Race (includes information about aboriginals) 
 ORRS 
Rural living 
 RPDB 
Socioeconomic status 
 RPDB 
 Primary Cause of ESRD 
 
Diabetes 
 ORRS 
Drug Induced 
 ORRS 
GN/Autoimmune disease 
 ORRS 
Polycystic Kidney Disease 
 ORRS 
Renal Vascular Disease 
 ORRS 
Other 
 ORRS 
Comorbid Factors 
   
Arrhythmia 
 OHIP/CIHI-DAD 
Amputation  
 CIHI-DAD 
Alcoholism 
 CIHI-DAD 
Atrial Fibrillation/Flutter 
 CIHI-DAD 
CABG/PCI  
 ORRS / CIHI-DAD/OHIP 
Charlson Comorbidity Score 
 CIHI-DAD 
Coronary Artery Disease (with angina) 
 ORRS/ CIHI-DAD/OHIP 
Crash start with AKI 
 CIHI-DAD 
Dementia 
 ORRS /CIHI-DAD/OHIP  
Depression* 
 CIHI/ODB/OHIP 
Diabetes mellitus 
 ORRS/CIHI-DAD/OHIP 
Fracture  
 CIHI-DAD/OHIP 
Heart failure ++ 
 CIHI-DAD 
Hemorrhage 
 OHIP/CIHI-DAD 
Hypertension 
 ORRS/ CIHI-DAD/OHIP 
Hypotension 
 CIHI-DAD 
Ischemic Stroke ++ 
 CIHI-DAD 
Liver Disease 
 ORRS/ CIHI-DAD/OHIP 

Lung disease (COPD) 
 ORRS/ CIHI-DAD/OHIP 
Malignancy 
 ORRS /CIHI-DAD/OHIP 
Myocardial infarction ++ 
 ORRS/ CIHI-DAD 
Other serious illness that would shorten life 
expectancy less than 5 years 
 ORRS 
Peripheral vascular disease  
 ORRS/ CIHI-DAD/OHIP 
Smoking 
 ORRS 
Stroke/Transient ischemic attack 
 ORRS/CIHI-DAD 
Subarachnoid Hemorrhage 
 CIHI-DAD 
Syncope 
 CIHI-DAD  
Drugs (for 65+ years) 
   
ACE Inhibitors 
 ODB 
ARB 
 ODB 
Anti-depressants 
 ODB 
Anti-Psychotics 
 ODB 
Benzodiazepine 
 ODB 
Beta-Blockers 
 ODB 
 Healthcare Utilization 
   
Long term care facility utilization 
 ODB/OHIP/CCRS 
Number of nephrology consults in the last 12 months 
 OHIP 
Number of Family Doctor consults in the last 12 
months 
 OHIP 
Number of Hospitalizations in last 12 months 
 CIHII-DAD 
Number of Visits to Emergency Department in last 12 
months 
 NACRS 
Total Healthcare Costs in last 12 months 
 Various sources at ICES 79 
Lab Data (Last measured) 
 
 
Hemoglobin 
 ORRS/OLIS 
Urea 
 ORRS/OLIS 
eGFR 
 ORRS/OLIS 
Serum Albumin 
 ORRS/OLIS 
Procedures / Monitoring 
   
Carotid endarterectomy 
 OHIP 
Coronary angiogram 
 OHIP/CIHI-DAD 
Coronary revascularization 
 OHIP/CIHI-DAD 
Echocardiography 
 OHIP/CIHI-DAD 
Holter monitoring 
 OHIP/CIHI-DAD 

Other Variables 
   
Dialysate Temperature (baseline) 
 Case Report Forms** 
Pre-dialysis systolic blood Pressure (baseline) 
 Case Report Forms** 
Pre-dialysis diastolic blood pressure (baseline) 
 Case Report Forms** 
Mean intra-dialytic nadir systolic blood pressure 
(baseline) 
 Case Report Forms** 
Diastolic blood pressure accompanying the intra-
dialytic nadir systolic blood pressure (baseline) 
 Case Report Forms** 
Date of first nephrology visit  
 ORRS 
Height 
 ORRS 
Last measure weight 
 ORRS 
Body Mass Index (BMI) 
 ORRS 
History of Renal Transplant 
 ORRS /CIHI-DAD/OHIP 
First Dialysis Modality 
 
Peritoneal Dialysis 
 ORRS/CORR 
Hemodialysis 
 ORRS/CORR 
Had a late nephrology referral 
 ORRS/CORR 
Vascular access used at index date (April 01, 2017) 
 
Arteriovenous fistula 
 ORRS/CORR 
Arteriovenous graft 
 ORRS/CORR 
Central venous catheter 
 ORRS/CORR 
Hemodialysis Characteristics at Index Date 
 
 
Patients Dialyzing in an Acute Care Hospital 
 ORRS 
Patients Dialyzing in a Chronic or Community Hospital 
 ORRS 
Duration of all dialysis modalities (Months) 
 ORRS 
Centre Factors 
   
Number of patients at centre 
 ORRS 
Centre Transplant Rate in previous 24 months 
 ORRS /CIHI-DAD/OHIP 
Centre Death Rate in previous 24 months 
 ORRS/RPDB 
Centre Transfer rate in previous 24 months 
 ORRS 
Number of stations within centre 
 ORRS 
Centre uses electronic dialysis run sheets 
 Case Report Forms** 
Centre uses tympanic temperature measurement  
 Case Report Forms** 
Centre uses heated chairs 
 Case Report Forms** 
 
ODB= Ontario Drug Benefit database contains claims for prescription drugs received under the Ontario Drug 
Benefit program. Most are for those >=65 but from 1997 forward we also have data on other ODB program; OLIS= 

Ontario Laboratories Information System; ORRS=Ontario Renal Reporting System has information is a database of 
all pre-dialysis, acute dialysis and chronic dialysis patients in Ontario since 2010; CIHI-DAD= Discharge Abstract 
Database records detailed diagnosis and procedural information on all hospitalizations in Ontario. Up to 25 unique 
diagnostic and 20 procedural codes can be assigned to each hospitalization.; OHIP= Ontario Health Insurance Plan 
database contains health claims for inpatient and outpatient physician services; 
* Depression is defined as (1) having two events of OHIP diagnosis, hospitalizations, or ODB drug prescription; or 
(2) having at least one event in at least two of OHIP diagnosis, hospitalizations, or ODB drug prescription.80  
**This information is captured on the dialysis run sheet that is completed with every dialysis treatment in Ontario 
(i.e. centres do not have to collect additional data outside standard of care).  
++ History of components of primary or secondary outcomes  
 
 
 
 

Appendix 8: Algorithm for capturing primary composite outcome 
 
Outcome 
Algorithm 
Position 
of code 
Performance 
Cardiovascular-related 
death ₳, ¥ 
ORGD: Leading Cause of Death 
LCD_33 = Chronic rheumatic heart disease 
LCD_34 = Hypertensive disease 
LCD_35 = Ischemic heart disease 
LCD_36 = Pulmonary heart disease and related 
LCD_37 = Nonrheumatic valve disorders 
LCD_38 = Cardiomyopathy 
LCD_39 = Cardiac arrest 
LCD_40 = Cardiac arrhythmias 
LCD_41 = Heart failure and complications, ill-
defined heart disease 
LCD_42 = Cerebrovascular diseases 
LCD_43 = Atherosclerosis 
LCD_44 = Aortic aneurysm and dissection 
N/A 
Not available 
Cardiovascular-related 
death 
ICD-10: 
I00 - I78 
AND 
Dischdisp="07" or death in Registered Persons 
Database during the hospital stay 
Primary 
Diagnosis 
RPDB has an 
accuracy of 99% 
for capturing 
death 81 
Hospital admission with 
ischemic stroke 
ICD-10:  
I63 (excl. I63.6), I64, H341 
Primary 
Diagnosis 
PPV= 85% 82,83  
Hospital admission with 
myocardial infarction 
ICD-10:  
I21, I22 
Primary 
Diagnosis 
Sn= 89%, PPV= 
87% 84  
Hospital admission with 
heart failure 
ICD-10: 
I50 
Primary 
Diagnosis 
Sn=61% , 
Sp=98%, 
PPV=66%85 
Abbreviations: ICD = International Classification of Disease; OHIP = Ontario Health Insurance Plan; 
Dischdisp=Discharge disposition; Sn=Sensitivity; PPV= Positive Predictive Value; LCD=Leading Cause of Death; 
ORGD=Office of Registrar General – Deaths; RPDB = Registered Persons Database 
₳ Due to the time lag in data capture, deaths from ORGD will only capture events for the follow-up period between 
April 3rd, 2017 and December 31st, 2020. These events capture both in- and out-of-hospital cardiovascular-related 
deaths. For the remaining study period, we will only be able to capture in-hospital deaths using ICD-10 codes. 
¥ Personal communication with Dr. Jack Tu who is part of a working group conducting a validation of this outcome 
using existing Ontario clinical trial data as the reference standard. 

Appendix 9: Justification for using a composite primary endpoint 
Our composite primary endpoint is composed of individual components that we believe will have a 
treatment effect in the same direction and magnitude and are clinically important – appreciating 
cardiovascular-related mortality is a more detrimental outcome than hospitalization. The outcome will 
provide an overall sense of the impact of the intervention on cardiovascular morbidity.  
While there is some debate in the literature about including hospital admission with congestive heart 
failure as a component outcome of major cardiovascular events,86 we chose to include it given that a 
personalized dialysate temperature may lead to fewer heart failure admissions if there is less cardiac 
ischemia or less left ventricular dysfunction over time. As well, patients who have a preserved blood 
pressure during dialysis may be less likely to stop their dialysis treatments early or may have more fluid 
removed on their dialysis treatments. In our analysis of historic Ontario data, the median stay for a 
hospital admission with congestive heart failure (ICD-10 code I50) in dialysis was 6 days (25th, 75th 
percentiles: 3, 10).  
There is a strong relationship between intradialytic hypotension and myocardial stunning because of 
transient abnormalities in cardiac regional wall motion that occur in the presence of coronary 
hypoperfusion. Rapid reductions in blood pressure predispose to myocardial stunning because coronary 
flow is dependent on central arterial pressure. Hypotensive episodes also associate with aging of the 
arterial system, as well as extensive calcification and stiffening of the arterial walls.87 The cumulative 
contribution of hypotensive events to cardiovascular events have been significant.11,13,53 Reduction of 
dialysate temperature is one technique that has been shown to be effective in decreasing the of risk 
intradialytic hypotensive events and stabilizing blood pressure, reducing injury to the heart and brain as 
seen in magnetic imaging studies.9 

In observational studies, compared to patients that did not experience intradialytic hypotension, 
patients that experienced intradialytic hypotension in more than 10% of their hemodialysis treatments 
had a hazard ratio of 1.22 (95% CI: 1.02 to 1.48) for cardiovascular-related mortality, 1.20 (95% CI: 1.00 
to 1.45) for hospitalizations of non-fatal myocardial infarction, and 1.22 (95% CI: 1.11 to 1.34) for 
hospitalizations with heart failure or volume overload.44 Similarly, compared to patients that did not 
experience intradialytic hypotension, those that experienced intradialytic hypotension in more than 10% 
of their treatments had a 1.23 (95% CI: 1.08 to 1.41) risk of experiencing a major cardiovascular event 
(defined as nonfatal myocardial infarction, nonfatal stroke, or cardiovascular-related mortality).44 The 
study did not specifically provide the risk of experiencing an ischemic stroke. 
The historic annual hazard rate of the components of the primary outcome in our data sources have 
similar baseline annual event rates: 0.031 for cardiovascular-related mortality, 0.030 for hospital 
admission with myocardial infarction, 0.032 for hospital admission with congestive heart failure, and 
0.012 for hospital admission with ischemic stroke per person-year.  
 
 

Appendix 10: Other important outcomes 
Lower limb amputation: Patients on hemodialysis, especially those with diabetes, have a high hazard 
rate of amputation. The historic baseline hazard rate of lower extremity amputations over a 4-year 
period (from April 1, 2013 to March 31, 2017) for an open cohort was 0.026 events per person-year. For 
patients with diabetes, this historic hazard is 0.039 events per person-year. Amputations are associated 
with cardiovascular risk factors and likely linked to vascular injury caused by hemodialysis-induced 
ischemia, which complicates pre-existing arterial disease and diabetes related injury.  
 
Major falls and fractures: Many patients on dialysis are frail and prone to falling, which may also 
predispose them to suffer a fracture. Bone fractures are an important outcome and can result in 
morbidity, high economic costs, and mortality. The three-year incidence of falls requiring a 
hospitalization ranges from 3% to 12% for patients on dialysis, with elderly females being the highest 
risk.88 Major fractures (hip, forearm, pelvis, or proximal humerus) are also common occurring in nearly 
6% of patients each year.88 In our cohort, the historic hazard rate of major fractures over a 4-year period 
(from April 1, 2013 to March 31, 2017) for an open cohort was 0.037 events per person-year. Intra-
dialytic hypotension might increase the rate and severity of falls after a hemodialysis session leading to 
additional fractures requiring hospitalizations.  
 
Emergency department visits or hospitalizations (analyzed separately and as a composite): Patients on 
hemodialysis are frequently hospitalized and account for 5% to 7% of healthcare expenditures in 
developed countries despite comprising a very small percentage of the general adult population.89–92 
These patients have several characteristics that make them vulnerable to hospitalization and emergency 
department use, including multimorbidity, high rates cardiovascular complications, and complex 

medication regimens. The historic hazard rate for emergency department visits was 1.05, all-cause 
hospitalizations was 0.65, and the composite all-cause emergency department visits or hospitalizations 
over a 4-year period (from April 1, 2013 to March 31, 2017) was 1.22 events per person-year. 
 
Intradialytic hypotension: 
In general, there is no consensus, evidence-based, medical definition for intradialytic hypotensive 
episodes.93,94 Most definitions of intradialytic hypotension are made up of two or more components: 1) 
an absolute or relative decline in the intradialytic systolic blood pressure from the pre-dialysis systolic 
blood pressure reading; and 2) a nadir systolic blood pressure reading below a specific threshold.94 Some 
definitions include an additional component of intradialytic symptoms (e.g., cramping, yawning,) and/or 
the need for an intradialytic intervention (e.g., Trendelenburg position, fluid administration,). In our 
trial, we will not have information on patient symptoms of hypotension or interventions used to treat 
these episodes. It has been previously shown that adding symptom or intervention criteria to 
intradialytic hypotension definitions did not change the strength of association with mortality.10 
In MyTEMP, in post-hoc analysis, we will define intradialytic hypotension if the patient experiences any 
of the following: i) nadir systolic blood pressure < 90 mmHg anytime during the hemodialysis session 
(regardless if patients begin the hemodialysis session with systolic blood pressure below 90 mmHg); or 
ii) drop in systolic blood pressure by ≥ 30 mmHg from the pre-dialysis systolic blood pressure 
reading.95,96 We will also consider alternate definitions of intradialytic hypotension: 
a) Systolic blood pressure < 90 mmHg alone. A nadir systolic blood pressure of < 90 mmHg was 
strongly associated with all-cause mortality in a previous observational study.10,94  

b) At least a 25% relative reduction in nadir systolic blood pressure from pre-dialysis systolic 
blood pressure or nadir ≤ 90 mmHg.53,94,97 
c) At least a 25% relative reduction in nadir systolic blood pressure from pre-dialysis systolic 
blood pressure.53,94,97 
d) A drop in nadir systolic blood pressure by ≥ 35 mmHg from pre-dialysis systolic blood 
pressure.94,98 
 
 
 

Appendix 11a: Statistical power estimates for different hazard ratios of the treatment effect different 
coefficients of variation, and different rates of the primary composite outcome. A statistical power 
estimate of 0.8 means the trial has 80% power to detect the specified hazard ratio with the intervention 
vs. control, if the effect in truth exists.   
Different 
Hazard 
Ratios of 
the 
Treatment 
Effect 
Different rates  of the primary composite 
outcome (per person-year) 
0.08 
0.09 
0.1 
0.11 
CV=0.19 
0.75 
95% 
96% 
97% 
98% 
0.8 
80% 
83% 
85% 
88% 
0.85 
53% 
56% 
59% 
62% 
0.9 
25% 
27% 
29% 
31% 
CV=0.20 
0.75 
94% 
96% 
97% 
97% 
0.8 
78% 
82% 
84% 
86% 
0.85 
52% 
55% 
58% 
60% 
0.9 
25% 
26% 
28% 
30% 
CV=0.21 
0.75 
93% 
95% 
96% 
97% 
0.8 
77% 
80% 
83% 
85% 
0.85 
50% 
53% 
56% 
59% 
0.9 
24% 
26% 
27% 
28% 
CV=0.22 
0.75 
93% 
94% 
95% 
96% 
0.8 
76% 
79% 
81% 
83% 
0.85 
49% 
52% 
54% 
57% 
0.9 
23% 
25% 
26% 
27% 
CV=0.23 
0.75 
92% 
93% 
95% 
96% 
0.8 
74% 
77% 
80% 
82% 
0.85 
47% 
50% 
53% 
55% 
0.9 
23% 
24% 
25% 
27% 
CV=0.24 
0.75 
91% 
93% 
94% 
95% 
0.8 
73% 
76% 
78% 
80% 
0.85 
46% 
49% 
51% 
53% 
0.9 
22% 
23% 
24% 
26% 

CV=0.25 
0.75 
90% 
92% 
93% 
94% 
0.8 
71% 
74% 
76% 
79% 
0.85 
45% 
47% 
50% 
52% 
0.9 
21% 
23% 
24% 
25% 
  
CV = Coefficient of variation. We assumed a total follow-up of 4 years, a cluster harmonic average of 163 person-
years, alpha of 0.04, and 42 clusters per arm.  Starred values (*)highlights conditions where we have at least 80% 
power to detect a difference, if a difference truly exists.  
 
 

Appendix 11b: Details of power estimates using computer simulations. 
In addition to the closed form sample size estimation, we also confirmed our power calculations using 
simulation studies. This method allowed us to account for the complexity of our study design, variable 
cluster (HD centre) sizes, different follow-up periods among patients in participating centres, clustering, 
and censoring events during follow-up.99–102 We generated 1000 simulated data sets based on the 
correlation structure observed for the prevalent HD cohort from April 1st, 2013 to March 31st, 2017.  For 
each simulated dataset, 84 observations (i.e., HD centres) were generated and included information on 
the following: 1) number of outcome events that occurred within a 4-year period, 2) number of days of 
follow-up, and 3) a randomly allocated indicator representing the control or intervention arm. Assuming 
a two-tailed alpha 0.04, we have 56%, 81%, and 96% power to detect a 15%, 20%, and 25% hazard rate 
reduction in the primary composite endpoint, respectively. 
 
 

Appendix 11c: Power estimates for the key secondary endpoint of between group difference in the 
mean drop of systolic blood pressure (mmHg). 
 
Standard deviation of the cluster-period means 
Between group 
difference 
(mmHg)** 
2 
4 
5 
6 
7 
1 
54% 
16% 
12% 
6% 
5% 
2 
100% 
71% 
57% 
31% 
22% 
3 
100% 
98% 
94% 
71% 
55% 
4 
100% 
100% 
100% 
94% 
85% 
5 
100% 
100% 
100% 
100% 
97% 
6 
100% 
100% 
100% 
100% 
100% 
7 
100% 
100% 
100% 
100% 
100% 
8 
100% 
100% 
100% 
100% 
100% 
9 
100% 
100% 
100% 
100% 
100% 
10 
100% 
100% 
100% 
100% 
100% 
**Between-group difference in the mean drop of systolic blood pressure. 
The above data assumed there are 84 clusters with at least 6 repeated observations and a constant 
intraclass correlation coefficient (ICC) of 0.4 and an average drop across all sites/periods of 28 mmHg 
with an alpha of 0.01. 
Shaded area highlights conditions where we have at least 80% power to detect a difference, if a 
difference truly exists.  
 

Appendix 12: Bayesian analysis 
 
As a first step, we will use a minimally informative reference prior (which regards all possible log-hazard 
ratio values to be equally likely and will produce results largely dependent on observed data from 
MyTEMP). Sources of prior information will include: 1) results from published literature that compare 
temperature-reduced hemodialysis to standard hemodialysis temperature;66–68 and 2) historic data from 
our administrative data sources. At the analytic stage, we will update Table 3 based on current data 
from the literature. 
We will use PROC PHREG (SAS 9.4, NC Cary) – in a similar manner as conducted for the primary analysis 
– and invoke the BAYES statement to request that the parameters of the model be estimated by using 
Gibbs sampling techniques.103 This approach enables the specification of prior information, control the 
sampling, as well as obtain posterior summary statistics and convergence diagnostics. Convergence of 
the generated Markov chain will be assessed by examining the trace plot, autocorrelation function plot, 
and posterior density plot. 
 
 
 
 

Appendix 13: Planned additional analyses 
We will conduct several analyses to assess the robustness of the results from the primary analysis. These 
additional analyses will include: 
 
1. Adjusted Cox model to test the effect of the intervention vs. control on the primary composite 
outcome. 
2. Treating kidney transplants, switching to a home dialysis modality, and switching to a non-
participating hemodialysis centre as a censoring event. 
3. Assuming a closed cohort, where we will include only a subset of our cohort who were on 
hemodialysis prior to April 3rd, 2017. Using historic data, we estimate there will be ~7500 
patients included in this cohort. 
4. In our historic data, over a 4-year follow-up period we found 19% of patients experienced at 
least one event in our primary composite outcome and 4% of all patients had more than one 
event. Given the infrequent number of recurrent events, we decided to use a parsimonious 
approach of time-to-first event model for the primary analysis. However, it will be important to 
understand repeated events (i.e. one patient may contribute multiple events) that may occur 
during the study period.  
At first, we will explore these repeated events descriptively to estimate differences across the 
two arms. We will also conduct a Cox regression analysis that accounts for multiple events per 
patient. We will define a hospitalization episode of care as either a direct admission to an acute 
care hospital from which the patient is subsequently discharged home, or a continuous 
sequence of hospitalizations (i.e., a hospital discharge and admission within the same day is 

considered to all be part of the same episode of care). Unless the same event is within the 
episode of care, patients can contribute multiple events from the time they enter the study and 
until a censoring event.  
5. Patients on hemodialysis are at high risk of non-cardiovascular causes of death (e.g., sepsis, 
malignancy), may receive a kidney transplant, or switch to home dialysis. The extent to which 
these events impact the probability of observing the event of interest can be explored through 
competing risks. Ideally, we will see comparable results with the Cox model, however in absence 
of agreement, we will assume that the bias of results in the Cox model occurs due to the 
number, type, and distribution of competing events. In this analysis, we will censor follow-up 
when patients switch to another centre not in the same group allocation. 
6. For the as-treated analysis, patients will be coded as receiving the intervention depending on 
the centre where they are being treated. For patients that experience an outcome of interest 
within 30-days of switching to another centre, the outcome will be attributed to the previous 
centre. 
 
 

Appendix 14: Main responsibilities of the data safety monitoring board  
1. Consider factors external to this trial when relevant information becomes available. This 
includes any scientific or therapeutic developments that may have an impact on the safety of 
the participants or the ethics of this trial.  
2. Review the conduct of the trial, including protocol violations.  
3. Review data on hemodialysis centre recruitment, accrual, and retention, as well as assessments 
of data quality, completeness, and timeliness.  
4. Protect the confidentiality of the trial data and the DSMB discussions. 
5. Approve the statistical analysis plan prior to trial analysis. 
6. Make recommendations to continue, modify, or stop the trial if necessary. 
To date, with the information available about the safety of temperature-reduced dialysis, the DSMB is 
not planning to perform any between-group interim analyses during the trial period. 
 
 

References 
1.  
Liyanage T, Ninomiya T, Jha V, et al. Worldwide access to treatment for end-stage kidney disease: 
a systematic review. Lancet. 2015;385(9981):1975-1982. doi:10.1016/S0140-6736(14)61601-9 
2.  
CIHI. CORR Annual Statistics 2017. https://www.cihi.ca/en/canadian-organ-replacement-register-
2016. Accessed January 2, 2018. 
3.  
Foley RN, Collins AJ. End-stage renal disease in the United States: an update from the United 
States Renal Data System. J Am Soc Nephrol. 2007;18(10):2644-2648. 
doi:10.1681/ASN.2007020220 
4.  
Foote C, Ninomiya T, Gallagher M, et al. Survival of elderly dialysis patients is predicted by both 
patient and practice characteristics. Nephrol Dial Transplant. 2012;27(9):3581-3587. 
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Table of Changes to Protocol 
Note: This table was posted publicly on ClinicalTrial.Gov on August 14, 2020 
 

MyTEMP 
 
 
 
Version Date: July 6, 2022 
 
 
Protocol Updates 
** All Protocol updates below were made without any viewing of any outcome data (viewing and analysis will only occur after the trial period is over) and 
were done after the start of the MyTEMP Trial period (April 3rd, 2017).  Please note that a revision date of Dec 2, 2019 indicates when protocol updates 
were posted to clinicaltrials.gov, however, these changes are reflected in the final published version of the MyTEMP protocol. Any changes to the protocol 
that occurred after the protocol was published were deemed to be minor. ** 
Revision  
Date of revision 
Details of Revision  
Rationale  
Title Change  
March 05, 2019  
Changed from: Major cardiovascular and other patient 
important outcomes with personalized dialYsate 
TEMPerature (MY TEMP): A registry-based cluster 
randomized controlled trial. 
 
Changed to: Major outcomes with personalized 
dialysate TEMPerature (MyTEMP): A pragmatic, 
registry-based, cluster randomized controlled trial   
Our updated title is more reflective of the trial.  
Study follow-up period 
March 05, 2019 
Changed from: Two-year follow-up 
 
Change to:  Four-year follow-up 
To increase statistical power, the trial was extended from a 
two- to a four-year follow-up period (see Statistical Power 
below).  
Objective 
March 05, 2019 
Changed from: To test for differences in the rate of the 
composite outcome of all-cause mortality and major 
cardiovascular events among centres that provide 
temperature-reduced personalized hemodialysis compared 
with centres that provide standard-temperature 
hemodialysis. 
Changed to: To test the effect of randomizing hemodialysis 
centres to provide (i) a personalized temperature-reduced 
dialysate protocol of 0.5 to 0.9 °C below the patient’s pre-
dialysis body temperature measured before each dialysis 
session, to a minimum dialysate temperature of 35.5 °C, vs. 
(ii) a standard-temperature dialysate protocol of 36.5 °C, for 
a period of four years, on a composite outcome of 
cardiovascular-related death or hospitalization for major 
cardiovascular events in outpatients receiving maintenance 
hemodialysis. 
After undergoing peer-review at the Heart and Stroke 
Foundation, peer-reviewers strongly recommended using CV-
mortality as opposed to all-cause mortality. With regards to 
the follow-up time, please see rationale in the “Power 
Estimates” section. 
 
We also added additional details to the objective to improve 
clarity. 

MyTEMP 
 
 
 
Version Date: July 6, 2022 
 
 
Primary Outcome 
March 05, 2019 
 
Changed from: Composite outcome of all-cause 
mortality or a hospitalization for a major 
cardiovascular event.  
CV-related hospitalizations included: 
• 
Hospital admission with Ischemic Stroke 
• 
Hospital admission with Myocardial infarction 
• 
Coronary revascularization (includes 
CABG/PCI) 
 
Changed to: Composite outcome of time to first 
cardiovascular-related mortality or hospitalization. 
CV-related hospitalizations included:  
• 
Hospital admission with ischemic stroke 
• 
Hospital admission with myocardial infarction 
Hospital admission with heart failure 
After undergoing peer-review at the Heart and Stroke 
Foundation, peer-reviewers strongly recommended using CV-
mortality as opposed to all-cause mortality. We chose a 
cause-specific death (i.e. cardiovascular) in our endpoint, in 
contrast to all-cause mortality, because non-cardiovascular 
causes of death are common in the hemodialysis population 
and the intervention is less likely to reduce the rate of such 
deaths. Since submitting the grant for peer-review, a 
validation study of CV mortality database codes against 
clinical trial adjudicated outcomes has been done in our 
province, which shows the CV mortality codes operate well. 
As a secondary outcome, we will also test the effect of 
personalized temperature-reduced dialysate temperature on 
all-cause mortality.  
 
The primary composite outcome will provide an overall 
measure of the intervention’s impact on cardiovascular-
related morbidity and mortality. The outcome components 
are each expected to respond similarly to the intervention 
(i.e., be reduced by a similar magnitude) and have a similar 
rate of occurrence, and each is clinically important—
appreciating that while death is far worse than a 
cardiovascular-related hospitalization—avoiding the latter is 
also important to patients. 
 
The removal of “Coronary revascularization” was related to 
the wide variation in practice across hospitals and individual 
physicians. In previous research, the physician performing the 
diagnostic catheterization and the treating hospital were 
strong independent predictors of the mode of 
revascularization. As such, we removed this outcome because 
differences between the two groups may have been related 
to varying hospital practices rather than the intervention 
itself. 
 

MyTEMP 
 
 
 
Version Date: July 6, 2022 
 
 
 
Primary Outcome 
 
July 06, 2022 
 
Changed from: Data on cause of death will primarily 
be obtained from the Office of the Registrar General 
Deaths (ORGD) database, which records the cause of 
death for all deaths in Ontario. Deaths that occur in 
the last 3 months of follow-up will be captured using 
the Registered Persons Database and cause of death 
will be defined as cardiovascular-related if the patient 
dies in hospital (or the emergency department) with a 
cardiovascular event as the main diagnosis on the 
discharge summary. Hospital encounters for 
cardiovascular events will be ascertained using the 
10th version of the International Classification of 
Diseases (ICD-10) codes.  
 
Changed to: Data on cardiovascular-related causes of 
death will primarily be captured using the following 
definition: (a) in-hospital (or emergency department) 
death with a cardiovascular disease diagnosis in the 
primary/most responsible diagnosis position or (b) 
out-of-hospital death (including death in an 
emergency department) without documentation of 
cancer in the 365 days before and including the date 
of death and without documentation of trauma in the 
30 days before and including the date of death. A 
death in the emergency department with a 
cardiovascular event as the primary diagnosis will be 
defined as “in-hospital” cardiovascular-related death, 
regardless of whether they had cancer or trauma 
code. 
 
 
 
 
 
 
 
We were recently notified of unanticipated delays in the 
linkage of the Office of the Register General deaths database 
at ICES, which will now take several more years to cover the 
entire trial period. As a result, we will use an alternate 
method of ascertaining cardiovascular-related deaths, 
validated by Lix et al. (2021)1. 
 
1. Lix LM, Sobhan S, St-Jean A, et al. Validity of an algorithm to 
identify cardiovascular deaths from administrative health 
records: a multi-database population-based cohort study. 
BMC Health Serv Res. 2021;21(1). doi:10.1186/S12913-021-
06762-0 

MyTEMP 
 
 
 
Version Date: July 6, 2022 
 
 
Key Secondary Outcome 
March 05, 2019 
 
 
Added: proportion of sampled hemodialysis sessions 
complicated by intradialytic hypotension. 
While there are complex physiologic mechanisms by which a 
personalized dialysis temperature may have beneficial effects 
compared to standard dialysis temperature, a reduction in the 
risk of intradialytic hypotension is a key consideration. 
 
December 02, 
2019 
Changed from: Proportion of sampled hemodialysis 
sessions complicated by intradialytic hypotension 
 
Changed to: Between-group mean difference in the 
intradialytic drop of systolic blood pressure. 
Given the change in alpha level (see Statistical Significance, 
below) we have modified the key secondary outcome due to a 
lack of statistical power. 
Other Important 
Outcomes 
March 05, 2019 
Changed from: 
(i) Ability to live independently 
(ii) Amputation rate 
(iii) Rate of major falls and fractures 
 
Changed to: 
(i) We now focus on “Lower Limb Amputation”. 
(ii) We now include composite outcome of 
hospital encounter of either falls and 
fractures, rather than fractures alone. 
(iii) We added the following outcomes 
▪ 
Emergency department 
visits or hospital admissions 
▪ 
Intensity use of blood 
pressure medications 
▪ 
Intradialytic hypotension 
 
We are reporting on several patient-important outcomes that 
may be:  
(i) responsive to our intervention; and 
reliably assessed using the administrative data sources. 
 
December 02, 
2019  
Removed: Intensity use of blood pressure medication.  
 
 
Given our data administrative data sources, we will not have 
drug prescription data on all patients included in the trial. We 
are also not confident we can accurately capture this outcome 
in our data sets.  
Statistical Significance 
December 02, 
2019 
Added: The first family of hypotheses includes both 
the primary and key secondary hypotheses (composite 
primary outcome and drop in intra-dialytic systolic 
blood pressure), with weights of 0.8 and 0.2, 
respectively. If the intervention improves at least one 
of the two outcomes in the first family of hypotheses, 
In keeping with recommended practice 2, 3, 4 our aim is to 
avoid Type I errors due to multiple comparisons. We will use 
the parallel gatekeeping procedure to control the overall 
familywise error rate at 0.05. The first family of hypotheses 
includes both the primary and key secondary hypotheses 

MyTEMP 
 
 
 
Version Date: July 6, 2022 
 
 
outcomes in a second family of hypotheses will be 
tested in the following order at a level of significance 
that maintains the overall error rate across all prior 
testing at 0.05:  all-cause mortality or cardiovascular-
related hospitalization, all-cause mortality, hospital 
admission with myocardial infarction, hospital 
admission with congestive heart failure, hospital 
admission with ischemic stroke. Any reported 
confidence intervals that maintain the familywise 
error rate at 0.05 will be adjusted for the tested level 
of significance. 
Added: Cardiovascular death was added as the last 
secondary outcome that will be tested at a level of 
significance that maintains the overall error rate 
across all prior testing at 0.05. 
(composite primary outcome and drop in intra-dialytic systolic 
blood pressure), with weights of 0.8 and 0.2, respectively. 
 
2. Harrington D, D’Agostino RB, Gatsonis C, et al. New Guidelines for 
Statistical Reporting in the Journal. N Engl J Med. 2019;381(3):285-286. 
doi:10.1056/NEJMe1906559 
 
3. U.S. Department of Health and Human Services, Food and Drug 
Administration, Center for Drug Evaluation and Research (CDER), Center for 
Biologics Evaluation and Research (CBER). Multiple Endpoints in Clinical Trials: 
Guidance for Industry. Silver Spring, MD; 2017. 
 
4. Massachusetts Medical Society. Submitting to NEJM - Statistical Reporting 
Guidelines. https://www.nejm.org/author-center/new-manuscripts. Accessed 
September 4, 2019. 
 
Power Estimates 
December 02, 
2019 
Changed from: More than 80% power to detect a 15% 
relative-rate reduction in all-cause mortality. 
 
Changed to: Our trial will have at least 80% power to 
detect a hazard rate reduction of 20% or more 
(corresponding to a hazard ratio of 0.80; 2-sided 
α=0.04; 0.04 chosen to control the familywise error 
rate, see Statistical significance). We assumed a 
hazard rate for the composite outcome of 
cardiovascular-related death or hospitalization of 
0.095 events per person-year (termed the baseline 
hazard). We used a coefficient of variation (the ratio of 
the between-cluster variance to the average baseline 
hazard rate) of 0.216, and a cluster harmonic mean of 
the total person-time follow-up of 163 person-years.   
See Primary Outcome (above) for explanation of change in the 
outcome.  
 
 
Cohort Selection 
March 05, 2019 
Changed from: Closed cohort - prevalent patients who 
were alive on maintenance in-centre hemodialysis 
April 3rd, 2017. Additional analyses - open cohort, 
where only patients that remain on HD during the trial 
period for at least 30 days. 
 
To minimize the inclusion of patients who leave the study or 
switch centres soon after starting in-centre hemodialysis, we 
will restrict the cohort to patients who received treatment at 
the same participating study centre for at least 90 days before 
their cohort entry date (the index date), after which the 
patient’s observation time will begin (termed the 90-day rule).  

MyTEMP 
 
 
 
Version Date: July 6, 2022 
 
 
Changed to: At the time of the analysis, we will restrict 
the study cohort to outpatients who received in-
centre maintenance hemodialysis at a participating 
study centre between April 3rd, 2017 and March 31st, 
2021. 
 
This added restriction would exclude (i) patients who quickly 
recover renal function (e.g., patients with acute kidney injury) 
(ii) early scheduled transfers to home dialysis or those 
receiving kidney transplants; and (iii) those with arranged 
dialysis treatments away from home (transient dialysis). 
Analysis 
 
 
 
 
December 02, 
2019 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
April 20, 2022 
 
 
 
 
 
 
 
Changed from: Using  a modified Poisson regression 
model for patient level data and account for the effect 
of clustering at the centre level.  
 
Changed to: Our analyses will account for the design 
and covariate-constrained randomization. In the 
primary intention-to-treat analyses, we will also assess 
the effect of the intervention on the rate of the 
composite outcome of cardiovascular-related death or 
hospitalization using the multivariable generalized-
estimating-equations extension for the Cox 
proportional hazard model, with an exchangeable 
covariance matrix, to account for the clustering of 
individuals within hemodialysis centres. Patients will 
be censored at end of study follow-up or earlier if they 
die due to a non-cardiovascular–related cause. 
Patients who recover renal function, switch to a 
hemodialysis centre with the alternative or no 
temperature allocation, receive a kidney transplant, or 
switch to home dialysis will be followed for outcomes 
according to their initial random allocation. 
 
Changed from: Patients will be censored at end of 
study follow-up or earlier if they die due to a non-
cardiovascular–related cause 
 
Changed to: In the analysis of the primary composite 
outcome, patients will be censored at end of study 
follow-up, upon emigration or experiencing the 
This statistical method is more appropriate given our data 
structure and our updated primary outcome. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

MyTEMP 
 
 
 
Version Date: July 6, 2022 
 
 
 
 
 
August 27, 2021 
primary composite outcome. Non-cardiovascular 
death will be treated as a competing risk event. 
 
Changed from: We will examine the center’s 
proportion of patients (weighted by the dialysis center 
size) whose (1) systolic blood pressure dropped from 
≥90 mm Hg before dialysis to <90 mm Hg during 
dialysis; (2) nadir intradialytic systolic blood pressure 
was ≥25% lower than their pre-dialysis level or whose 
systolic blood pressure dropped from ≥90 mm Hg 
before dialysis to <90 mm Hg during dialysis; (3) nadir 
intradialytic systolic blood pressure was ≥25% lower 
than their pre-dialysis level; and (4) nadir intradialytic 
systolic blood pressure was ≥35 mm Hg lower than 
their pre-dialysis level. 
 
Changed to: We will examine the center’s proportion 
of patients whose (1) nadir systolic blood pressure is < 
90 mmHg anytime during a dialysis session when the 
value before the session was ≥ 90 mmHg, or drop in 
systolic blood pressure ≥ 30 mmHg anytime during 
session from value before session; (2) systolic blood 
pressure dropped from ≥90 mm Hg before dialysis to 
<90 mm Hg during dialysis; (3) nadir intradialytic 
systolic blood pressure was ≥25% lower than their pre-
dialysis level or whose systolic blood pressure dropped 
from ≥90 mm Hg before dialysis to <90 mm Hg during 
dialysis; (4) nadir intradialytic systolic blood pressure 
was ≥25% lower than their pre-dialysis level; and (5) 
nadir intradialytic systolic blood pressure was ≥35 mm 
Hg lower than their pre-dialysis level. 
 
Clarification regarding weighting: 
1)We are analyzing intradialytic hypotension in a similar 
manner to out “key secondary outcome.” 
2)Weights are estimated using the cluster size based on the 
measured monthly observations, which is the standard 
approach (cluster size or the sample size selected in each 
cluster if subsampling is carried out). In our trial, this results in 
constant cluster sizes because each cluster contributes 15 
randomly selected individuals to provide pre-dialysis systolic 
blood pressure and the nadir intradialytic systolic blood 
pressure. As such, the weighted and unweighted analysis will 
align since the same weight would be applied to all clusters. 
After some data cleaning, there may be slight variations in the 
number of contributing patients per cluster (i.e., 13-15). 
However, this likely will not impact the overall point estimate. 
Bayesian analysis of the 
primary outcome 
March 05, 2019 
Added: We will assess the robustness of the primary 
findings (based on the classical frequentist analytic 
approach) to various assumptions about the use of 
prior information from various sources. As a 
We will have limited statistical power to detect a risk reduction below 20%, yet 
a risk reduction of even 5% (i.e., hazard ratio of 0.95) could be clinically 
meaningful. In the absence of any harm, even a small risk reduction on the 
primary outcome would likely convince dialysis providers worldwide to adopt a 
personalized temperature-reduced dialysate protocol as the standard of care. 
To address this limitation, we will conduct a pre-specified Bayesian analysis to 

MyTEMP 
 
 
 
Version Date: July 6, 2022 
 
 
supplement, we will conduct and report a Bayesian 
analysis based on existing guidelines. 
examine the probability that the intervention is effective under differing 
thresholds that could be clinically relevant, in keeping with advice that 
investigators should pre-specify both frequentist and Bayesian analyses as part 
of their statistical analysis plan for randomized clinical trials 5. 
5. Food and Drug Administration. Guidance for Industry and FDA Staff: 
Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials. 
http://www.rkstatistics.com/files/public/Knowledge_Bank/FDA-
Bayesian_Stats_in_Medical_Device_Trials.pdf. Published 2010. Accessed 
January 11, 2019. 
Analysis – Key Secondary 
Outcome 
December 02, 
2019 
Added: Between-group mean differences in the drop 
of mean systolic blood pressure, analyzed at the 
centre level using a repeated measures random-
effects linear mixed model. 
This statistical method is more appropriate given our data 
structure and updated secondary outcome. 
Analysis - 
Sub Group 
March 05, 2019 
 
 
 
 
 
 
 
 
December 02, 
2019 
 
 
 
 
 
 
 
 
 
 
 
 
                           
Changed from: No pre specified subgroup analyses. 
Changed to: Pre specified subgroups:  
 
(i) 
patients with a history of prior hospital 
admission for myocardial infarction, 
ischemic stroke, or congestive heart 
failure; and 
(ii) 
 patients with diabetes. 
 
Changed to: Estimate of the hazard ratio for the 
primary outcome with its accompanying 95% 
confidence intervals across subgroups for consistency 
of the effect. 
 
Pre-specified subgroups: 
(i) 
Patients with a baselines hospital 
admission for myocardial infarction, 
ischemic stroke, or conestive heart 
failure, and 
(ii) 
Incident patients, defined as new 
patients starting in-centre hemodialysis 
during follow-up 
 
 
These are two subgroups of interest, where a personalized 
dialysis temperature may have a larger treatment effect. 
 

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Journal canadien de la santé et de la maladie rénale
https://doi.org/10.1177/20543581211041182
Canadian Journal of Kidney Health 
and Disease 
Volume 8: 1 –11
© The Author(s) 2021
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DOI: 10.1177/20543581211041182
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Clinical Research Protocol
1041182 CJKXXX10.1177/20543581211041182Canadian Journal of Kidney Health and DiseaseDixon et al
research-article20212021
MyTEMP: Statistical Analysis  
Plan of a Registry-Based, Cluster-
Randomized Clinical Trial
Stephanie N. Dixon1,2,3
, Jessica M. Sontrop1,3
,  
Ahmed Al-Jaishi1,2,4
, Lauren Killin1,2,  
Christopher W. McIntyre1,3,5, Sierra Anderson1, Amit Bagga6, 
Derek Benjamin7, Peter Blake1,3,5, P. J. Devereaux4,  
Eduard Iliescu8, Arsh Jain2,3,5, Charmaine E. Lok9,  
Gihad Nesrallah10,11
, Matthew J. Oliver10,12, Sanjay Pandeya13, 
Manish M. Sood2,14,15
, Paul Tam16, Ron Wald2,10,17
,  
Michael Walsh4,18, Merrick Zwarenstein2,3,  
and Amit X. Garg2,3,4,5
, On behlaf of the MyTEMP Investigators
Abstract
Background: Major Outcomes with Personalized Dialysate TEMPerature (MyTEMP) is a 4-year cluster-randomized clinical 
trial comparing the effect of using a personalized, temperature-reduced dialysate protocol versus a dialysate temperature of 
36.5°C on cardiovascular-related death and hospitalization. Randomization was performed at the level of the dialysis center 
(“the cluster”).
Objective: The objective is to outline the statistical analysis plan for the MyTEMP trial.
Design: MyTEMP is a pragmatic, 2-arm, parallel-group, registry-based, open-label, cluster-randomized trial.
Setting: A total of 84 dialysis centers in Ontario, Canada.
Patients: Approximately 13 500 patients will have received in-center hemodialysis at the 84 participating dialysis centers 
during the trial period (April 3, 2017, to March 1, 2021, with a maximum follow-up to March 31, 2021).
Methods: Patient identification, baseline characteristics, and study outcomes will be obtained primarily through Ontario 
administrative health care databases held at ICES. Covariate-constrained randomization was used to allocate the 84 dialysis 
centers (1:1) to the intervention group or the control group. Centers in the intervention group used a personalized, 
temperature-reduced dialysate protocol, and centers in the control group used a fixed dialysate temperature of 36.5°C.
Outcomes: The primary outcome is a composite of cardiovascular-related death or major cardiovascular-related 
hospitalization (defined as a hospital admission with myocardial infarction, congestive heart failure, or ischemic stroke) 
recorded in administrative health care databases. The key secondary outcome is the mean drop in intradialytic systolic 
blood pressure, defined as the patients’ predialysis systolic blood pressure minus their nadir systolic blood pressure during 
the dialysis treatment. Anonymized data on patients’ predialysis and intradialytic systolic blood pressure were collected at 
monthly intervals from each dialysis center.
Analysis plan: The primary analysis will follow an intent-to-treat approach. The primary outcome will be analyzed at the 
patient level as the hazard ratio of time-to-first event, estimated from a subdistribution hazards model. Within-center 
correlation will be accounted for using a robust sandwich estimator. In the primary analysis, patients’ observation time will 
end if they experience the primary outcome, emigrate from Ontario, or die of a noncardiovascular cause (which will be 
treated as a competing risk event). The between-group difference in the mean drop in intradialytic systolic blood pressure 
obtained during the dialysis sessions throughout the trial period will be analyzed at the center level using an unadjusted 
random-effects linear mixed model.
Trial status: The MyTEMP trial period is April 3, 2017, to March 31, 2021. We expect to analyze and report results by 2023 
once the updated data are available at ICES.
Trial registration: MyTEMP is registered with the US National Institutes of Health at clincaltrials.gov (NCT02628366).
Statistical analytic plan: Version 1.1 June 15, 2021.

2 
Canadian Journal of Kidney Health and Disease
Abrégé 
Contexte: L’essai MyTEMP (Major Outcomes with Personalized Dialysate Temperature) est un essai clinique randomisé en 
grappes d’une durée de 4 ans comparant l’effet d’un protocole de dialysat personnalisé à température réduite par rapport 
au dialysat à 36,5 °C sur les hospitalisations et les décès dus à des problèmes cardiovasculaires. La répartition aléatoire des 
sujets a été effectuée au niveau du centre de dialyse (ci-après appelé « groupe »).
Objectifs: Exposer les grandes lignes du plan d’analyse statistique de l’essai MyTEMP.
Type d’étude: MyTEMP est un essai clinique pragmatique ouvert, à deux bras, en groupes parallèles, basé sur un registre, 
et randomisé en grappes.
Cadre: L’essai est mené dans 84 centres de dialyse en Ontario (Canada).
Sujets: On estime qu’environ 13 500 patients auront reçu des soins d’hémodialyse dans les 84 centres de dialyse participants 
au cours de la période de l’essai (3 avril 2017 au 1er mars 2021; suivi maximal jusqu’au 31 mars 2021).
Méthodologie: Les résultats et les données concernant l’identification des patients et leurs caractéristiques initiales seront 
principalement tirés des bases de données administratives du système de santé ontarien tenues par l’ICES. Une répartition 
aléatoire restreinte par les covariables a été employée pour classer les 84 centres de dialyse (1:1) dans le groupe d’intervention 
ou le groupe témoin. Le groupe d’intervention a utilisé un protocole personnalisé de dialysat à température réduite et le 
groupe témoin un dialysat à température fixe (36,5 °C).
Résultats: Le principal critère d’évaluation est la combinaison d’un décès d’origine cardiovasculaire ou d’une hospitalisation 
majeure liée à la santé cardiovasculaire (définie comme une hospitalisation pour un infarctus du myocarde, une insuffisance 
cardiaque congestive ou un AVC ischémique) enregistrée dans les bases de données administratives du système de santé. 
Le principal critère d’évaluation secondaire est la baisse moyenne de la tension artérielle systolique intradialytique, laquelle 
est définie comme la tension artérielle systolique du patient avant la dialyse moins la tension artérielle systolique minimale 
pendant la dialyse. Les données anonymisées sur la tension artérielle systolique initiale et la tension artérielle systolique 
intradialytique des patients ont été colligées à intervalles mensuels dans chaque centre de dialyse.
Plan d’analyse: L’analyse primaire adoptera une approche fondée sur l’intention de traiter. Le principal critère d’évaluation 
sera analysé au niveau du patient comme le risque relatif de survenue d’un premier événement, estimé à partir d’un modèle 
de risques de sous-distribution. La corrélation intracentre sera prise en compte à l’aide d’un robuste estimateur sandwich. 
Dans l’analyse primaire, le temps d’observation des patients prendra fin s’ils présentent le principal critère d’évaluation, s’ils 
déménagent hors de l’Ontario ou s’ils décèdent d’une cause non cardiovasculaire (qui sera traitée comme un événement 
à risque concurrentiel). La différence entre les groupes quant à la baisse moyenne de la tension artérielle systolique 
intradialytique, obtenue pendant les séances de dialyse tout au long de l’essai, sera analysée au niveau du centre avec un 
modèle linéaire mixte à effets aléatoires non corrigé.
1Lawson Health Research Institute, London, ON, Canada
2ICES, London, ON, Canada
3Western University, London, ON, Canada
4McMaster University, Hamilton, ON, Canada
5London Health Sciences Centre, ON, Canada
6Windsor Regional Hospital, ON, Canada
7Royal Victoria Regional Health Centre, Barrie, ON, Canada
8Kingston Health Sciences Centre, ON, Canada
9University Health Network, Toronto, ON, Canada
10University of Toronto, ON, Canada
11Nephrology Program, Humber River Hospital, Toronto, ON, Canada
12Division of Nephrology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
13Halton Healthcare, Oakville, ON, Canada
14Ottawa Hospital Research Institute, ON, Canada
15University of Ottawa, ON, Canada
16Scarborough Health Network, Toronto, ON, Canada
17St. Michael’s Hospital, Toronto, ON, Canada
18St. Joseph’s Healthcare Hamilton, ON, Canada
Corresponding Authors:
Stephanie N. Dixon, SAP Author and Senior Statistician Responsible, London Health Sciences Centre, Western University, Westminster Tower, 800 
Commissioners Road East, ELL-215, London, ON N6A 5W9, Canada. 
Email: stephanie.dixon@ices.on.ca
Amit X. Garg, MyTEMP clinical lead, London Health Sciences Centre, Western University, Westminster Tower, 800 Commissioners Road East, ELL-215, 
London, ON N6A 5W9, Canada. 
Email: amit.garg@lhsc.on.ca

Dixon et al 
3
Statut de l’essai: L’essai MyTEMP couvre la période du 3 avril 2017 au 31 mars 2021. Nous comptons analyser et rendre 
compte des résultats d’ici 2023, dès que les données mises à jour seront disponibles à l’ICES.
Enregistrement de l’essai: MyTEMP est enregistré auprès du National Institute of Health des États-Unis sur clincaltrials.
gov (NCT02628366).
Keywords
cluster-randomized control trial, pragmatic trial, personalized dialysate temperature, cardiovascular events, hemodialysis
Received April 27, 2021. Accepted for publication July 13, 2021.
Introduction
This article details the statistical analysis plan for the Major out-
comes with personalized dialysate TEMPerature (MyTEMP) 
trial. Details on the background, rationale, and design of 
MyTEMP are provided elsewhere.1 Briefly, the trial was under-
taken to test the effect of randomizing outpatient hemodialysis 
centers to provide (1) a personalized, temperature-reduced dial-
ysate protocol (intervention) or (2) a dialysate temperature of 
36.5°C (control) on cardiovascular-related death and hospital-
ization. Centers in the intervention arm were asked to set the 
dialysate temperature to between 0.5°C and 0.9°C below the 
patient’s predialysis body temperature for each dialysis session, 
to a minimum dialysate temperature of 35.5°C. Centers in the 
control arm were asked to use a dialysate temperature of 36.5°C 
for all patients.
This province-wide trial is embedded into routine care 
with the intervention delivered by dialysis unit personnel. 
We expect approximately 13 500 patients will have received 
in-center hemodialysis at the 84 participating dialysis centers 
during the 4-year trial period (April 3, 2017, to March 31, 
2021). Patient characteristics and outcomes will primarily be 
obtained from routinely collected data captured in Ontario 
provincial administrative health care databases held at ICES. 
The pragmatic design of MyTEMP allows broad inclusion of 
dialysis centers and a large representative sample of patients 
that should yield highly generalizable findings.
Trial Objectives and Hypotheses
The primary objective of MyTEMP is to examine the effect of 
the intervention on a composite outcome of cardiovascular-
related death or major cardiovascular-related hospitalization 
(a hospital admission with myocardial infarction, congestive 
heart failure, or ischemic stroke). We hypothesize that this 
composite outcome will be lower in patients in the interven-
tion arm than in the control arm. Patient-level data for this out-
come will be obtained from administrative health care 
databases.
The key secondary objective is to examine the effect of the 
intervention on the mean drop in intradialytic systolic blood 
pressure, defined as the patients’ predialysis systolic blood 
pressure minus their intradialytic nadir systolic blood pres-
sure. We hypothesize that the average drop in intradialytic 
systolic blood pressure obtained during the dialysis sessions 
throughout the trial period will be smaller in centers in the 
intervention arm than in the control arm. Anonymized, center-
level data on intradialytic systolic blood pressure were 
obtained at monthly intervals from each dialysis center.
Study Methods
The trial will be analyzed and reported in accordance with 
the Consolidated Standards of Reporting Trials (CONSORT) 
statement extended to cluster-randomized trials.2 We will 
also adhere to the extension of the CONSORT statement for 
routinely collected data and pragmatic trials.3 The statistical 
analysis plan was developed in accordance with the 
Guidelines for the Content of Statistical Analysis Plans in 
Clinical Trials.4 A table showing the revision history of this 
plan is provided in Appendix 1 in the Supplemental Material.
Trial Design
MyTEMP is a pragmatic, 2-arm, parallel-group, registry-based, 
open-label, cluster-randomized trial. The trial started on April 
3, 2017, and enrolled 84 of Ontario’s 97 hemodialysis centers 
(Canada). This province-wide trial was embedded into routine 
care with center-wide implementation of the intervention by 
dialysis unit personnel. Patients in the trial’s analysis popula-
tion will be identified from the Ontario Renal Reporting System 
(ORRS), an administrative health care registry managed by the 
Ontario Renal Network, the provincial organization that man-
ages the delivery of chronic kidney disease services in Ontario, 
Canada.5 Baseline characteristics and trial outcomes will be 
primarily obtained through routinely collected data captured in 
administrative health care databases held at ICES. The data sets 
will be linked using unique encoded identifiers and analyzed at 
ICES. More information about the ICES databases are pro-
vided in the trial protocol.1
Randomization, Sample Size, Hypothesis Testing 
Framework, and Interim Analysis
The randomization procedures and sample size calculation 
are detailed in the study protocol.1 Briefly, 84 centers were 
randomly allocated in a 1:1 ratio using covariate-constrained 

4 
Canadian Journal of Kidney Health and Disease
randomization to balance key characteristics between the 
trial arms.6-8 We expect approximately 13 500 patients will 
have received in-center hemodialysis at the 84 participating 
dialysis centers during the trial period. The trial is powered 
to detect a hazard rate reduction in the primary composite 
outcome of at least 20% (corresponding to a hazard ratio of 
0.80); described in more detail below (see sections “Statistical 
Principles”, “Confidence Intervals and P Values: Level of 
Statistical Significance”), a 2-sided α of 0.04 for a superior-
ity hypothesis test was chosen for the primary outcome to 
control the error rate (total 2-sided α of 0.05) with a 2-sided 
α of .01 for the key secondary outcome (mean drop in intra-
dialytic systolic blood pressure).
No interim analyses were planned or performed.
Timing of Final Analysis
All analyses described in this document will be conducted 
after the trial ends and when the data covering the trial period 
are available at ICES. We expect to complete the analysis 
when the data covering the trial period are released from the 
Office of the Registrar General Database (ORGD) (updated 
releases from this database occur every 2-3 years).
Timing of Outcome Assessments
Data on the primary outcome (cardiovascular-related death or 
major cardiovascular-related hospitalization) will be obtained 
from routinely collected data captured in administrative health 
care databases held at ICES. Data on the key secondary out-
come (the mean drop in intradialytic systolic blood pressure) 
were obtained directly from hemodialysis run sheets; the pre-
dialysis systolic blood pressure is taken before each dialysis 
treatment and the nadir systolic blood pressure during the 
dialysis treatment, both typically measured while seated. The 
blood pressure data are recorded as part of routine care as part 
of the patients’ medical record. We collected anonymized 
blood pressure data from hemodialysis run sheets from each 
center weekly for the first month of the trial, biweekly for the 
second month, and monthly thereafter. Each collection con-
sisted of data for hemodialysis sessions on the last Friday or 
Saturday of the period from 15 different patients, who were 
randomly selected from all patients receiving maintenance 
hemodialysis at the center at that time.
Statistical Principles
Confidence Intervals and P Values: Level of 
Statistical Significance
We planned the trial using a parallel gatekeeping procedure9 to 
control the overall error rate at 0.05 (to control for multiple test-
ing).1 The 2-sided significance level will be 0.04 for the pri-
mary hypothesis and 0.01 for the key secondary hypothesis.
The remaining secondary outcomes will be tested as fol-
lows: If the overall error rate for the primary hypothesis and 
key secondary hypothesis exceeds 0.05, the results of subse-
quent tests will be provided as point estimates with 95% con-
fidence intervals (CIs; without P values), and we will indicate 
that the interval widths are not adjusted for multiple testing 
and that the inferences may not be reproducible.10
If there is a statistically significant improvement in the 
primary outcome or the key secondary outcome, the remain-
ing secondary outcomes will be tested in the following order 
at a level of significance that maintains the overall error rate 
across all prior testing at 0.05 (example below): (1) A com-
posite of all-cause mortality or cardiovascular-related hospi-
talization, (2) all-cause mortality, (3) hospital admission with 
myocardial infarction, (4) hospital admission with conges-
tive heart failure, (5) hospital admission with ischemic 
stroke, and (6) cardiovascular death.
For example, if the primary outcome is nonsignificant 
(P ≥ .04) and the key secondary outcome is nonsignificant (P 
≥ .01), no further hypothesis testing will be performed. If the 
primary outcome is significant and the key secondary out-
come is nonsignificant, then the remaining secondary out-
comes will be tested at a significance level of 0.04 until a 
P value is ≥.04. If the primary outcome is nonsignificant and 
the key secondary outcome is significant, then the remaining 
secondary outcomes will be tested at a significance level of 
0.01 until a P value is ≥.01. Once one of these tests exceeds 
an overall error rate across all prior testing at 0.05, as described 
above, the results of subsequent analyses will be limited to 
point estimates with 95% CIs (without P values), and we will 
indicate that the interval widths are not adjusted for multiple 
testing and that the inferences may not be reproducible.10
Adherence and Protocol Deviations
We obtained anonymized data on the programmed dialysate 
temperature from hemodialysis run sheets in the same man-
ner and following the same schedule as for the collection of 
blood pressure data (described in section “Timing of 
Outcome assessments,” above). We used these temperature 
data to assess adherence to the intervention. During the trial 
period, we identified and worked with centers if they demon-
strated <80% adherence (ie, if the dialysate temperature was 
routinely being set outside the specified range in the proto-
col; strategies used to address non-adherence are provided in 
Al-Jaishi et al).1 In the final report, we will display the distri-
bution of dialysate temperatures used in the intervention and 
control groups over the trial period, and the difference 
between patients’ mean predialysis temperature and the pro-
grammed dialysate temperature. Summary measures for 
each group and between-group differences will be presented 
with 95% CIs.
The allocated dialysate temperature protocol was imple-
mented by hemodialysis centers and did not follow patients 
who transferred to centers using a different temperature 

Dixon et al 
5
protocol (ie, patients received the protocol used at the center 
they transferred to [also referred to as patient crossovers]). As 
well, patients no longer received the allocated protocol if they 
switched to peritoneal dialysis, home hemodialysis, or noc-
turnal hemodialysis, or if they transferred to a center not par-
ticipating in the trial in Ontario or a center outside Ontario.
For both the intervention and control groups, we will 
report the proportion of patients’ observation time spent 
receiving (1) in-center hemodialysis at the index center (or a 
center providing the same allocated temperature protocol as 
the index center), (2) in-center hemodialysis at a study center 
providing the other allocated temperature protocol (“patient 
crossovers”), (3) dialysis at a nonstudy center in Ontario, (4) 
other types of dialysis (ie, home hemodialysis, peritoneal 
dialysis, in-center nocturnal hemodialysis, or in-center self-
care hemodialysis), (5) no dialysis due to receipt of a kidney 
transplant, and (6) no dialysis due to receiving palliative care 
or due to recovered kidney function. The aggregate propor-
tions will be weighted by the patent’s observation time. 
Based on our analysis of historical records, we expect ≥85% 
of the patients’ observation time will be spent receiving in-
center hemodialysis with the originally allocated tempera-
ture protocol.
Analysis Populations
The trial’s analysis population will include adult outpatients 
who received maintenance hemodialysis at a study center for 
at least 90 days between April 3, 2017, and March 1, 2021, and 
who met the trial’s eligibility criteria (defined in section 
“Eligibility Criteria,” below). Patients’ observation time in the 
trial will begin on their index date: Patients already receiving 
maintenance hemodialysis at the beginning of the trial will 
have an index date of April 3, 2017; the index date of other 
patients will be when they initiate maintenance hemodialysis 
for at least 90 days during the trial period (where the index 
date is the 90-day date; described in more detail in the “Trial 
Population”, “Eligibility Criteria” section below).
Intent-to-treat population. The primary analysis will use 
an intent-to-treat approach, which consists of all eligible 
patients from the 84 study centers who entered the trial 
regardless of what kidney replacement treatments they 
received in follow-up. All outcome events will be attributed 
to the center that patients received hemodialysis at on their 
index date.
As-treated population. As an additional analysis, we will 
analyze the data using an as-treated approach, which will 
account for patient crossovers to centers in the other trial arm 
and patient transfers to different treatment modalities. Given 
the potential biases of an as-treated analysis, we will give 
precedence to the results of the intent-to-treat analysis.11 For 
crossovers, where a patient transfers to a center providing the 
other allocated temperature protocol, the observation time 
for the second center will begin after 30 days, and events that 
occur after this date will be attributed to the second center.
In the as-treated analysis, transfer to a dialysis center 
providing the other allocated temperature protocol (for >30 
consecutive days) will be treated as a time-varying exposure 
variable. The patient’s observation time will end when they 
(1) switch to another type of dialysis for >30 consecutive 
days (eg, home hemodialysis, peritoneal dialysis, in-center 
nocturnal hemodialysis, or in-center self-care hemodialy-
sis), (2) transfer to a center not participating in the trial for 
>30 consecutive days, (3) receive a kidney transplant, or (4) 
no longer receive any form of kidney replacement therapy 
for at least 90 consecutive days. These events will be treated 
as competing-risk events in the as-treated analysis (see sec-
tion “Additional Analyses,” below, for more details). In 
addition, for patients at a small proportion of centers that 
started delivering the allocated temperature protocol after 
the trial start date (ie, after April 3, 2017) due to delays in 
ethics and institutional approvals, the patients’ observation 
time will begin after the center started delivering the allo-
cated treatment protocol. Finally, as in the intent-to-treat 
analysis, we will assume all patients in a given center 
received hemodialysis using the temperature protocol allo-
cated to that center; as described above in section “Adherence 
and Protocol Deviations,” we expect >80% of hemodialysis 
treatments on average will be adherent to the allocated 
protocol.
Trial Population
Eligibility Criteria
The MyTEMP trial had 2 center-level inclusion criteria: (1) 
The hemodialysis center had to expect to treat a minimum of 
15 outpatients with maintenance in-center hemodialysis at 
the start of the trial period and (2) the medical director of the 
hemodialysis center (who acted as the center’s gatekeeper) 
had to allow their center to implement the randomly allo-
cated dialysate temperature protocol for the duration of the 
trial. On February 1, 2017 (the randomization date), 84 of 
Ontario’s 97 hemodialysis centers met the trial’s eligibility 
criteria and were included in the trial.
At the time of the analysis, we will restrict the trial’s anal-
ysis population to patients who received maintenance in-
center hemodialysis at a trial center between April 3, 2017, 
and March 1, 2021 (to allow for at least 30 days of follow-
up). To minimize the inclusion of transient patients and those 
receiving temporary dialysis, we will further restrict the 
analysis to patients who received dialysis at the same partici-
pating study center for at least 90 days, which will be the 
patient’s index date (see Al-Jaishi et al.)1. We term this the 
90-day stability rule. Patients who met the stability rule 
before April 3, 2017, will have an index date of April 3, 2017 
(the trial start date). Patients who started maintenance in-
center hemodialysis after April 3, 2017 (eg, patients new to 
in-center hemodialysis, or patients returning to in-center 
hemodialysis from home dialysis or a failed transplant), will 

6 
Canadian Journal of Kidney Health and Disease
be assigned an index date on the date they meet the stability 
rule. The index date is start of the patient’s observation time 
in the trial.
We will exclude patients who are not Ontario residents, 
patients with missing data on age or sex, and patients with an 
invalid health card number. We will also exclude patients 
older than 105 years, given the possibility the value was 
entered in error (a common exclusion used in ICES studies). 
These exclusions are primarily for data cleaning purposes to 
ensure that we can link patients across the different data sets, 
and we expect to exclude very few patients for these reasons. 
We will also exclude patients younger than 18 years because 
they are not recorded in the ORRS registry.
Flow Diagram
We will report the number of eligible and recruited centers, 
and the corresponding patients included in the analyses by 
the allocation group in a flow diagram (Figure 1 in the 
Supplemental Material).
Withdrawal and Loss to Follow-up
No centers withdrew from the study during the trial period. 
One dialysis center closed, and patients assigned to this cen-
ter before it closed will continue to be followed up for the 
trial period. In addition, one center divided into two centers 
after the trial started; these centers will be treated as a single 
cluster for the primary intent-to-treat analysis. Given that 
patient follow-up is performed through provincial adminis-
trative health care data, the only reason for loss to follow-up 
is emigration from the province, which occurs at a rate of 
0.5% per year for the general population;12 however, we can 
ascertain any outcomes that occur before emigration.
Baseline Patient Characteristics
Baseline characteristics will be obtained through administra-
tive data and center-level survey reporting. A list of baseline 
characteristics and database sources is available in appendix 7 
of Al-Jaishi et al.1 Continuous data will be summarized using 
means (standard deviations) or medians (25th, 75th percen-
tiles) as appropriate. Binary and categorical variables will be 
summarized using counts and percentages. We expect to report 
a key set of baseline characteristics in the primary paper, with 
additional characteristics provided in an appendix.
We will use the Registered Persons Database supplied 
from the Ontario Ministry of Health and Long-term Care and 
enriched with other data sources at ICES to obtain demo-
graphic information. Kidney characteristics will be obtained 
from ORRS and the Canadian Organ Replacement Registry. 
Baseline outpatient medication dispensing will be obtained 
through the Ontario Drug Benefit (ODB) database using a 
120-day lookback before the index date. Health care use in 
the year before the index date and baseline characteristics in 
the 5 years before the index date will be assessed using the 
Ontario Health Insurance Plan Claims Database (OHIP), the 
Canadian Institute for Health Information Discharge Abstract 
Database (CIHI-DAD), CIHI’s Same Day Surgery database, 
and the National Ambulatory Care Reporting System data-
base. The OHIP Claims Database is supplemented with the 
ICES Physician Database and the Corporate Provider 
Database to obtain data on health care use with specific pro-
vider types. Long-term care status will be obtained from 
ODB, OHIP, and the Continuing Care Reporting System. 
Baseline laboratory information in the year before the index 
date will be obtained through the Ontario Laboratories 
Information System. We will use ICES-derived cohorts to 
determine the history of certain chronic conditions such as 
diabetes,13 congestive heart failure,14 hypertension,15,16 and 
chronic obstructive pulmonary disease.17 Whenever possi-
ble, we will use validated algorithms to define baseline vari-
ables that have been used in multiple prior studies.
Analysis
Outcome Definitions
The primary outcome is a composite of cardiovascular-
related death or hospital admission with myocardial 
infarction,18 congestive heart failure,14 or ischemic stroke.19,20 
Cardiovascular-related hospitalization will be defined using 
main diagnostic codes from CIHI-DAD. Data on cause of 
death will be obtained from the ORGD, which uses a modi-
fied version of Becker’s groupings based on International 
Classification of Diseases 10th Revision (ICD-10) coding.21 
The specific algorithm for the primary composite outcome is 
provided in the trial protocol.1
The key secondary outcome is the mean drop in intradia-
lytic systolic blood pressure. To calculate this, we will sub-
tract each patient’s nadir intradialytic systolic blood 
pressure from their predialysis systolic blood pressure, and 
then average these values at the center level for each of the 
48 timepoints during the 4-year trial period. As described in 
section “Timing of Outcome Assessments” above, anony-
mized data on patients’ predialysis systolic blood pressure 
and their intradialytic systolic blood pressure were col-
lected from a random sample of 15 patients at monthly 
intervals from each dialysis center. These blood pressure 
data will be averaged monthly for each center. As such, dur-
ing the 4-year trial period, we will have a total of 48 sum-
mary measures (ie, 1 a month), for each of the 84 centers in 
our trial.
The other secondary outcomes are a composite of all-
cause mortality or cardiovascular-related hospitalization, all 
cause-mortality, and the components of the primary outcome 
examined separately: hospital admission with myocardial 
infarction, hospital admission with congestive heart failure, 
hospital admission with ischemic stroke, and cardiovascular-
related mortality.

Dixon et al 
7
We will also examine a composite of all-cause emergency 
room visits or all-cause hospitalizations (each will also be 
examined separately as the number of visits and hospitaliza-
tions, respectively), a hospital encounter with lower limb 
amputation, and a hospital encounter with a major fall or 
fracture.22-24
Finally, we will examine four definitions of intradialytic 
hypotension using the same blood pressure data as for the 
key secondary outcome at the cluster level. We will examine 
the center’s proportion of patients (weighted by the dialysis 
center size) whose (1) systolic blood pressure dropped from 
≥90 mm Hg before dialysis to <90 mm Hg during dialysis; 
(2) nadir intradialytic systolic blood pressure was ≥25% 
lower than their predialysis level or whose systolic blood 
pressure dropped from ≥90 mm Hg before dialysis to <90 
mm Hg during dialysis; (3) nadir intradialytic systolic blood 
pressure was ≥25% lower than their predialysis level; and 
(4) nadir intradialytic systolic blood pressure was ≥35 mm 
Hg lower than their predialysis level.
Analytic Methods
For both the intervention and control groups, we will sum-
marize the weighted proportion of the patients’ observation 
time spent receiving hemodialysis at an index center (or a 
center providing the same allocated temperature protocol as 
the index center), the time spent receiving hemodialysis at 
other centers, and the time spent receiving other forms of 
kidney replacement therapy (as described in the “Adherence 
and Protocol Deviations” section, above, and in Figure 2 in 
the Supplemental Material). Patient crossovers between the 
intervention and control arms will also be reported.
Analysis of the primary outcome. The primary analysis will 
follow an intention-to-treat approach. Eligible patients will 
be analyzed according to their index center’s treatment allo-
cation (regardless of whether they transitioned to other dialy-
sis centers or received other types of kidney replacement 
therapy in follow-up). Patients will be followed up until they 
experience the primary outcome, emigrate from Ontario, or 
die of a noncardiovascular cause (which will be treated as a 
competing-risk event).
We will report the crude frequency (%) and crude event 
rate (number of events per 100 person-years) for the time to 
first event for the primary composite outcome. We will cre-
ate a graph of the nonparametric cumulative incidence func-
tion (CIF) showing the time followed, number of events, and 
patients at risk during regular intervals in the trial for the 
intervention and control groups.25 Noncardiovascular death 
will be treated as a competing-risk event.26 We will present 
the curves for visualization purposes only (no statistical tests 
will be conducted for differences between curves); we will 
simultaneously present the curves of the primary outcome, 
the components of the composite outcome, and the compet-
ing risk of noncardiovascular death.
We will assess the intervention’s effect on the rate of the 
primary outcome using the multivariable generalized-esti-
mating-equations extension for the Fine and Gray’s subdis-
tribution proportional hazards, with an exchangeable 
covariance matrix, to account for the clustering of patients 
within hemodialysis centers and the competing risk of non-
cardiovascular death.26-28 We will supplement the primary 
analysis with the cause-specific hazard model for both the 
primary outcome and competing-risk of noncardiovascular 
death.26,29-31 We also will explore the composite of our pri-
mary event with the competing risk as described in the 
“Additional Analyses” section below.
As our study used covariate-constrained randomization, 
we will adjust for constrained covariates in our analyses; 
these patient-level covariates include age, biological sex, 
rural status, race, modified Charlson comorbidity index,32,33 
number of unique hospital admissions (in the 12 months 
before the index date), number of unique hypertensive pre-
scriptions, referral to a nephrologist <3 months before initi-
ating dialysis, type of vascular access on the index date, 
serum albumin on the index date, and the following baseline 
comorbidities: myocardial infarction, congestive heart fail-
ure, peripheral vascular disease, and diabetes mellitus. We 
will also adjust for the cluster-level historical rate of the 
composite outcome of cardiovascular-related death and 
major cardiovascular-related hospitalization.
We will evaluate and report the model assumptions for the 
clustered subdistribution hazard and cause-specific hazards 
models. Appropriate techniques will be applied when model 
assumptions are violated. Specifically, this model assumes a lin-
ear relationship between the log hazard and covariates. We will 
assess the assumption for linearity using residual plots. From his-
toric data, we expect that the linearity assumption will be vio-
lated for our continuous covariates. If this occurs, we plan to 
correct for the nonlinear covariates using restricted cubic splines. 
We will assess the proportionality of the hazard visually and use 
the Schoenfeld test for the intervention. If the assumption of pro-
portionality is violated, we will consider alternative methods so 
that the model remains valid (ie, a time-stratified model to iden-
tify constant hazard ratios within appropriate time intervals).
If we observe a statistically significant effect of the inter-
vention on the rate of the primary outcome, we will provide 
the absolute risk difference (and 95% CI) of the CIF for the 
intervention and control groups at different time points dur-
ing follow-up (including the median time points).
Bayesian analysis of the primary outcome. Our trial is powered 
to detect a hazard rate reduction in the primary composite out-
come of at least 20% (corresponding to a hazard ratio of 0.80).1 
We acknowledge that effects smaller than 20% could still be 
clinically meaningful. While we will give precedence to the 
results of the frequentist analysis, we will additionally conduct 
a prespecified Bayesian analysis to examine the probability 
that the intervention reduces the rate of the primary composite 
outcome by 5%, 10%, and 15% compared with the control 

8 
Canadian Journal of Kidney Health and Disease
group based on the trial results. This analysis is motivated by 
advice that prespecified Bayesian analyses can complement 
frequentist analyses in the interpretation of the results of ran-
domized clinical trials.34,35
We will conduct and report a Bayesian analysis based on 
existing guidelines.36 We aim to determine the probability that 
the intervention (1) affects the primary outcome and (2) reduces 
the hazard rate of the primary outcome by 5% to 30%, given 
the observed data. The range of hazard ratios will be presented 
in a figure. We considered a minimum 5% hazard rate reduc-
tion (ie, hazard ratio = 0.95) in the primary composite outcome 
as clinically relevant, as adopting the intervention with such an 
effect would still prevent many major cardiovascular-related 
hospitalizations and/or deaths each year.
We will explore a range of prior distributions (see 
Appendix 1 and Table 1 in the Supplemental Material) that 
can condition the posterior distribution. We will use priors to 
reflect varying degrees of enthusiasm and skepticism for the 
benefit of a personalized temperature-reduced dialysate 
before starting MyTEMP. The parameter’s estimate and stan-
dard errors will be obtained from the analysis (as described 
in the “Analysis of the primary outcome” section above) and 
combined with prior distributions to obtain the model’s pos-
terior distributions. We will estimate the Bayes factor and 
credible intervals using Markov chain Monte Carlo sampling 
techniques with at least 3 parallel chains. We will report the 
probability of the truth of our conclusions. This approach 
enables the specification of prior information, controls the 
sampling, and obtains posterior summary statistics and con-
vergence diagnostics. The convergence of the generated 
Markov chain will be assessed by examining the trace plot, 
autocorrelation function plot, and posterior density plot.
Analysis of the key secondary outcome. The between-group 
difference in the key secondary outcome (the mean drop in 
patients’ intradialytic systolic blood pressure) will be 
obtained using an unadjusted, generalized linear mixed 
model on the cluster-period summaries that accounts for the 
repeated measurements at the cluster level over time using 
longitudinal analysis methods,37 and appropriate CIs will be 
generated as described in section “Confidence Intervals and 
P Values: Level of Statistical Significance” in the “Statistical 
Principles” section above.
Analysis of other secondary outcomes. We will use the same 
analytic approach as for the primary outcome to examine 
each component of the primary outcome separately and the 
other secondary time-to-event outcomes (ie, see secondary 
outcomes listed in “Confidence Intervals and P Values: Level 
of Statistical Significance” and “Outcome Definitions” sec-
tions). Death (or noncardiovascular-related death) will be 
treated as a competing-risk event in these analyses when not 
part of the outcome. The model assumptions will be assessed 
as described in the “Analysis of the primary outcome” sec-
tion, above.
The number of all-cause hospital admissions and emer-
gency department visits during the study period will be ana-
lyzed using a negative binomial model relevant for count 
data while adjusting for clustering of dialysis centers with 
generalized estimating equations and using the log of time as 
an offset. We have chosen a negative binomial model because 
the historic data show a propensity for overdispersion (ie, the 
mean and variance of all-cause hospitalization and emer-
gency room visits appear to be different). We will assess 
model assumptions and evaluate the fit of our model. From 
historic data, we expect that 85% of our cohort will have at 
least 1 hospital admission and/or emergency department visit 
in follow-up, and we also expect this outcome to be right 
skewed. We will perform and report model diagnostics.
The four definitions of intradialytic hypotension will be 
analyzed at the center level in a similar manner as the key 
secondary outcome (ie, unadjusted, generalized linear mixed 
model on the cluster-period summaries).
Additional Analyses
We will conduct several additional analyses to assess the 
robustness of the results of the primary analysis. We will con-
duct an as-treated analysis (see the “Analysis Populations” 
section, above). We will also conduct a additional competing-
risk analysis, a recurrent-event analysis, and prespecified sub-
group analyses.
Competing-risk events. Additional events that may influ-
ence a patient’s chance of experiencing the primary outcome 
include receipt of a kidney transplant, switching to a non-in-
center hemodialysis modality, and emigrating from the prov-
ince. As such, we will conduct additional analyses and treat 
these events as competing risks.31,38 We will report how often 
these events occur during the follow-up period (see Figure 2 
in the Supplemental Material for more detail) and examine 
the extent to which treating these as competing events 
impacts our estimate of the intervention effect.26
Recurrent events. In an analysis of historical data, over a 
4-year follow-up period predating the trial, we found that 19% 
of patients experienced at least one event in our primary com-
posite outcome. Only 4% of all patients experienced more than 
one event. Given the infrequent number of recurrent events, we 
decided to use a parsimonious approach of time-to-first event 
model for the primary analysis. However, we will repeat the 
primary analysis using a recurrent-event model such that 
patients may contribute multiple outcome events during the 
trial period. We will use a cluster analog of the mean and rate 
functions in a recurrent-event multivariate regression model39 
to accommodate multiple events per patient while accommo-
dating the clustered design using a marginal approach (ie, a 
common baseline rate function).40 We will define a hospitaliza-
tion episode of care as a direct admission to an acute care hos-
pital from which the patient has subsequently been discharged 
home (ie, a hospital discharge and admission within the same 
day is considered to all be part of the same episode of care, as 
this could be simply a transfer between hospitals).

Dixon et al 
9
Subgroup analyses. In our protocol, we have prespecified 
two subgroup analyses for the MyTEMP trial. We will simply 
provide point estimates and corresponding 95% CIs for sub-
groups and will not perform significance testing. First, a sub-
group analysis of patients with preexisting cardiovascular 
disease (ie, patients hospitalized with myocardial infarction, 
ischemic stroke, or congestive heart failure at least once before 
study entry). Second, a subgroup of new hemodialysis patients, 
that is, those starting in-center hemodialysis for the first time 
during the trial period. These analyses will follow the same 
approaches as described above. We hypothesize that the inter-
vention may confer a larger absolute benefit to these 2 sub-
groups of patients. To examine the presence of additive 
interaction, we will calculate the hazard ratios (and 95% CI) 
between the intervention and control groups in patients with 
and without preexisting cardiovascular disease, and in new 
hemodialysis patients versus those who were already receiv-
ing dialysis when the trial started on April 3, 2017.
Missing Data
Given that our trial follow-up is through administrative 
health care data held at ICES, we anticipate no missing data 
for our outcomes unless a patient emigrated from Ontario 
during follow-up (anticipated in <0.5%), which will be 
treated as a censoring event in the primary analysis.
From previous work, we anticipate a small amount of 
missing data on some baseline characteristics. We will recode 
missing data on rural status as urban, missing data on race as 
Caucasian, and missing data on the modified Charlson 
comorbidity index as 2 (the minimum value associated with 
kidney failure).41 If there is <10% missing data on baseline 
serum albumin, we will use simple imputation; if there is 
between 10% and 40% missing, we will use multiple imputa-
tion; if there is >40% missing, we will exclude serum albu-
min from the adjusted analyses.
Harms
As described in prior studies (summarized in the protocol),1 
the MyTEMP intervention is well tolerated by patients. It 
was deemed a minimal risk to patients by the Research Ethics 
Review Boards which approved MyTEMP (further details in 
the protocol).1 There was a waiver of patient consent for 
enrollment into MyTEMP, and patients were notified through 
posters and letters about their dialysis center’s allocation. A 
patient or their nephrologist retained the option to opt out of 
the random allocation (ie, receipt of the treatment); however, 
patients could not opt out of data collection of the primary 
analysis as data are obtained as secondary use of routinely 
collected data. There are no prior data to suggest the inter-
vention will increase the risk of our primary or secondary 
outcomes compared with the control group (although our 
hypothesis testing approach will examine for this possibil-
ity). We have undertaken a separate independent substudy in 
a small set of centers to confirm there are no large between-
group differences on patient-reported symptoms (eg, feeling 
cold on dialysis). We are also linking some electronic dialy-
sis medical records to the ICES databases, to confirm in an 
observational cohort that the intervention is not associated 
with an altered risk of missed dialysis treatments or coming 
off hemodialysis treatments early.
Statistical Software
The primary analyses will be performed in SAS software ver-
sion 9.4 (NC, Cary). We may use additional software (eg, R 
Project, STAN or WinBUGS) for some analyses (eg, Bayesian 
analysis).
Trial Status
Eligible hemodialysis centers in Ontario were randomized 
on February 1, 2017. Centers began delivering the interven-
tion on April 3, 2017. The last day of follow-up is March 31, 
2021. Eligible patients receiving in-center hemodialysis 
between April 3, 2017, and March 1, 2021, will be included 
in the analysis.
Discussion
We designed a pragmatic, cluster-randomized registry trial to 
compare the effect of using a personalized, temperature-
reduced dialysate protocol versus a fixed dialysate tempera-
ture of 36.5°C on the rate of cardiovascular-related mortality 
and hospitalizations. This work provides a comprehensive 
outline of the analytic plan for the MyTEMP trial. We dis-
cussed the methods used for our prespecified primary, key 
secondary, and other secondary outcomes. We also provided 
details on additional analyses, which will be used to assess 
the robustness of the findings in our primary analysis. We 
hope this article will aid in the interpretation of MyTEMP 
and the design and analysis of other hemodialysis cluster-
randomized trials in the future.
Ethics Approval
The Health Sciences Research Ethics Board at Western University 
centrally approved the research ethics application for Ontario 
through the Streamlined Research Ethics Review System managed 
by Clinical Trials Ontario (Application Number: CTO-0736). The 
use of the data for the ICES portion of the project is authorized under 
section 45 of Ontario’s Personal Health Information Protection Act 
and does not require review by a Research Ethics Board.
Consent to Participate
The Research Ethics Board approved our application with alteration 
to the informed consent process as described in the protocol.
Consent for Publication
Consent for publication was obtained from all authors.

10 
Canadian Journal of Kidney Health and Disease
Availability of Data and Materials
The ICES Analyst and Scientist involved in the study will have 
access to the trial data obtained through ICES. The data set from 
this study is held securely in coded form at ICES. While legal data 
sharing agreements between ICES and data providers (eg, health 
care organizations and government) prohibit ICES from making the 
data set publicly available, access may be granted to those who 
meet prespecified criteria for confidential access, available at www.
ices.on.ca/DAS (e-mail: das@ices.on.ca). The full data set creation 
plan and underlying analytic code may be available from the authors 
upon request, understanding that the computer programs may rely 
upon coding templates or macros unique to ICES and are therefore 
either inaccessible or may require modification. The study investi-
gators will have access to the trial data collected outside of ICES. 
These data will not be available to the public.
Declaration of Conflicting Interests
The author(s) declared the following potential conflicts of interest 
with respect to the research, authorship, and/or publication of this 
article: G.N. has received consulting fees from Baxter Healthcare 
and Amgen. M.J.O. is the owner of Oliver Medical Management 
Inc, which licenses Dialysis Management Analysis and Reporting 
System software. He has received honoraria for speaking from 
Baxter Healthcare and participated on advisory boards for Janssen 
and Amgen. R.W. has received unrestricted research support from 
Baxter. The other authors declare no potential conflicts of interest 
to disclose.
Funding
The author(s) disclosed receipt of the following financial support 
for the research, authorship, and/or publication of this article: We 
received funding for the Major Outcomes with Personalized 
Dialysate TEMPerature (MyTEMP) trial from the Lawson Health 
Research Institute, the Ontario Renal Network, Dialysis Clinic Inc, 
the Heart and Stroke Foundation of Canada, and the Canadian 
Institutes of Health Research (CIHR). Funding is also provided by 
the Ontario Strategy for Patient-Oriented Research SUPPORT 
Unit, which is supported by the CIHR and the Province of Ontario. 
This work is supported through a CIHR SPOR Innovative Clinical 
Trial Multi-Year Grant (Grant number: MYG-151209). A.A.-J. is 
supported by the Allied Health Doctoral Fellowship from the 
Kidney Foundation of Canada, Michael DeGroote Scholarship, and 
CIHR Doctoral Award. C.W.M. is supported by the Dr Robert 
Lindsay Chair in Hemodialysis Innovation. P.J.D. is supported by a 
Tier 1 Canada Research Chair in Perioperative Medicine and the 
McMaster 
University/Hamilton 
Health 
Sciences 
Chair 
in 
Perioperative Care. M.M.S. is supported by the Jindal Research 
Chair for the Prevention of Kidney Disease. A.X.G. is supported by 
the Dr Adam Linton Chair in Kidney Analytics and a Clinician 
Investigator Award from the CIHR. ICES is funded by an annual 
grant from the Ontario Ministry of Health and Long-Term Care.
Data Monitoring
The Data Safety Monitoring Board (DSMB) (Drs Lehana Thabane, 
John Eikelboom, and Clara Bohm) are experts in clinical trials and 
hemodialysis. The DSMB reviewed the protocol, statistical analytic 
plan, and other trial documents. No interim analyses were con-
ducted beyond assessing protocol adherence.
Dissemination Policy
We plan to disseminate the results of the MyTEMP trial through 
peer-reviewed publication.
ORCID iDs
Stephanie N. Dixon 
 https://orcid.org/0000-0002-7566-6574
Jessica M. Sontrop 
 https://orcid.org/0000-0001-7784-2028
Ahmed Al-Jaishi 
 https://orcid.org/0000-0003-0376-2214
Gihad Nesrallah 
 https://orcid.org/0000-0002-2280-3811
Manish M. Sood 
 https://orcid.org/0000-0002-9146-2344
Ron Wald 
 https://orcid.org/0000-0003-4411-8169
Amit X. Garg 
 https://orcid.org/0000-0003-3398-3114
Supplemental Material
Supplemental material for this article is available online.
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1 
 
SUPPLEMENT 
Supplementary content for Dixon SN, Sontrop JM, Al-Jaishi A, et al. Major Outcomes With 
Personalized Dialysate TEMPerature (MyTEMP) Trial: Statistical Analysis Plan of a Registry-
Based, Cluster-Randomized Clinical Trial. Can J Kid Health Dis. 20xx: doi 
 
Figure 1. Flow of hemodialysis centers (clusters) and patients in the Major Outcomes With 
Personalized Dialysate TEMPerature (MyTEMP) Trial 
Figure 2. Potential transitions of patients during their observation period in the MyTEMP trial  
Appendix 1. Bayesian analysis: Prior beliefs  
Table 1. Characteristics of reference prior probability distributions representing prior beliefs 
about the primary composite endpoint benefit in the MyTEMP trial 
 
 

2 
 
Figure 1. Flow of hemodialysis centers (clusters) and patients in the Major Outcomes with 
Personalized Dialysate TEMPerature (MyTEMP) Triala 
 
 
a No loss to follow-up is expected other than if a patient emigrates from Ontario (expected in 
less 0.5% of patients). 
 
 

3 
 
Figure 2. Potential patient transitions during their observation time in the MyTEMP trial 
 
Abbreviations: *CV, cardiovascular; ++ ICHD, in-center hemodialysis.  
Patients can only enter states 2, 3 and 4 (i.e., change in modality and/or change in dialysis 
center) after they have remained in the new modality or center for at least 30 days. Their time 
in this state begins to accrue once they have met the 30-day rule. 
Patients can only enter the “6. No kidney replacement therapy” state after they have remained 
off kidney replacement for at least 90 days. Their time in this state begins to accrue once they 
have met the 90-day rule. 
   
LEGEND 
Black arrows: Patient transitions to terminating events (i.e., where a patient’s follow-up ends). 

4 
 
Dash arrow: Patient crossovers between the intervention and control arms (this transition will 
be ignored in the intent-to-treat analysis and treated as a time-varying exposure variable in the 
as-treated analysis).  
Dotted arrows: Patient transitions away from the allocated therapy (i.e., to dialysis centers not 
participating in the trial, to other types of treatments for kidney failure, and to no kidney 
replacement therapy). These transitions will be ignored in the intent-to-treat analysis and 
treated as competing-risk events in the as-treated analysis, where a patient’s observation time 
will end when they experience any of these events. 
Light grey-shaded box: The index center where a patient’s observation time begins. 
Dark grey-shaded boxes: Terminating events (i.e., the outcome of interest [e.g., the primary 
outcome or another study outcome], non-cardiovascular death [which will be treated as a 
competing-risk event in the primary analysis], the study end date (March 31, 2021), or 
emigration from Ontario. 
Non-shaded boxes: Transient events (i.e., changes in treatment modality or center), where 
patients may move back and forth between different treatment modalities or hemodialysis 
centers.  
 
The care of patients receiving hemodialysis can be complex. Patients may transition to different 
hemodialysis centers or to different types of treatment for kidney failure. For both the 
intervention and control groups, we will report the proportion of the patients’ observation time 
spent receiving: 

5 
 
1. In-center hemodialysis at the index center (or a center providing the same allocated 
temperature protocol as the index center);  
2. In-center hemodialysis at a study center providing the other allocated temperature 
protocol (‘patient crossovers’);  
3.  Dialysis at a non-study center in Ontario; 
4. Other types of dialysis (i.e., home hemodialysis, peritoneal dialysis, in-center nocturnal 
hemodialysis, or in-center self-care hemodialysis); 
5. No dialysis due to receiving a kidney transplant; and  
6. No dialysis for at least 90 days which could represent recovered kidney function.  
 
The proportions will be weighted by patient’s observation time on study. Based on historical 
data (over a 4-year follow-up period pre-dating the trial), we expect 86.7% of the patients’ 
observation time will be spent receiving in-center hemodialysis using the originally allocated 
temperature protocol. We expect 2.4% of patients’ observation time will be spent receiving 
other forms of dialysis (i.e., home hemodialysis, peritoneal dialysis, in-center nocturnal 
hemodialysis, or in-center self-care hemodialysis), 3.2% will be spent receiving hemodialysis at 
a center providing the other allocated therapy (or a center not participating in the trial), 2.6% 
will be spent not receiving kidney replacement (due to withdrawal or recovered kidney 
function), 0.5% will be spent outside of Ontario, and 4.6% will be spent living with a transplant. 
We expect < 1% of patients in the trial population to emigrate from Ontario during the trial 
period (we can ascertain any outcomes that occur before emigration), 21% to experience the 

6 
 
primary composite outcome, 23% to die of non-cardiovascular causes, and the remaining 55% 
to reach the end of the trial period. 
 
 

7 
 
Appendix 1: Bayesian analysis: Prior beliefs 
We will conduct and report a Bayesian analysis based on existing guidelines.1 We aim to 
determine the probability that the intervention (1) affects the primary outcome and (2) reduces 
the hazard rate of the primary outcome by 5%, 10%, and 15%, given the observed data. We 
considered a minimum 5% hazard rate reduction (i.e., hazard ratio = 0.95) in the primary 
composite outcome as clinically relevant, as adopting the intervention with such an effect 
would still prevent many major cardiovascular-related hospitalizations and/or deaths each year. 
We will explore several prior distributions (Table 1, below) that can condition the posterior 
distribution and provide insight into the primary results' sensitivity. We are using priors to 
reflect varying degrees of enthusiasm and skepticism for the benefit of personalized dialysate 
temperature before the start of the MyTEMP trial. 
We will conduct the Bayesian analysis similar to the primary analysis. However, the parameters 
of the model will be estimated by using Gibbs sampling techniques. This approach enables the 
specification of prior information, controls the sampling, and obtains posterior summary 
statistics and convergence diagnostics. The convergence of the generated Markov chain will be 
assessed by examining the trace plot, autocorrelation function plot, and posterior density plot. 
 
 
 
 

8 
 
 
References 
1. van Doorn J, van den Bergh D, Böhm U, et al. The JASP guidelines for conducting and 
reporting a Bayesian analysis. Psychon Bull Rev. 2020. doi:10.3758/s13423-020-01798-5 
 
 

9 
 
Table 1. Characteristics of Reference Prior Probability Distributions Representing Prior Beliefs 
About Primary Composite Endpoint Benefit 
Prior Belief 
Assumed 
HR 
Assumed 
SD of log 
HR 
Probability of treatment effect ≤ than the specified 
threshold 
  
Rationale for Specifying 
Distribution Characteristics 
< 1.00 
< 0.95 
< 0.90 
< 0.85 
< 0.80 
< 0.7 
- 
Non-informative 
1.0 
10 
50% 
50% 
50% 
49% 
49% 
49% 
All possible values for 
treatment effect for log HR 
are equally likely. 
Strongly 
Enthusiastic 
0.8 
0.1 
99% 
96% 
88% 
73% 
50% 
9% 
Based on historical data 
from our data sources, the 
standard deviation is 
generally less than 0.1, and 
published observational 
studies have shown the 
intervention can have less 
than an HR of 0.8.  
Moderately 
Enthusiastic 
0.8 
0.135 
95% 
90% 
81% 
67% 
50% 
16% 
Probability of observing a 
treatment effect greater 
than that assumed in the 
MyTEMP trial design (HR = 
0.8) is 50%; the probability 
of no benefit is 5%. 
Moderately 
Skeptical 
0.9 
0.125 
80% 
67% 
50% 
32% 
17% 
2% 
Probability of observing a 
treatment effect greater 
than an HR of 0.90 is 50%; 
the probability of any 
benefit is 80%. 
Skeptical 
1.0 
0.135 
50% 
35% 
22% 
11% 
5% 
0% 
Probability of observing a 
treatment effect greater 
than that assumed in the 
MyTEMP trial design (HR = 
0.8) is 5%; the probability of 
any benefit or harm is 
equivalent. 
Strongly Skeptical 
1.0 
0.07 
50% 
23% 
7% 
1% 
0% 
0% 
Probability of observing a 
treatment effect greater 
than that assumed in the 
MyTEMP trial design (HR = 
0.8) is 5%; the probability of 
any benefit or harm is 
equivalent. 
 
 

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Journal canadien de la santé et de la maladie rénale
https://doi.org/10.1177/20543581221110098
Canadian Journal of Kidney Health 
and Disease 
Volume 9: 1
© The Author(s) 2022
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/20543581221110098
journals.sagepub.com/home/cjk
Dixon, Stephanie N., et al. “MyTEMP: Statistical Analysis 
Plan of a Registry-Based, Cluster-Randomized Clinical 
Trial.” Canadian journal of kidney health and disease 8 
(2021): 20543581211041182.
In the published version of the above article, there are 
three corrections or updates the authors want to make as 
stated below:
1) Correction: One definition of intradialytic hypoten-
sion, which was reported in the study protocol, was 
accidentally missed in the statistical analysis plan.
 
The published statistical plan stated: Finally, we will 
examine four definitions of intradialytic hypotension 
using the same blood pressure data as the key second-
ary outcome at the cluster level. We will examine the 
center’s proportion of patients (weighted by the dialy-
sis center size) whose (1) systolic blood pressure 
dropped from ≥ 90 mm Hg before dialysis to < 90 mm 
Hg during dialysis; (2) nadir intradialytic systolic blood 
pressure was ≥ 25% lower than their pre-dialysis level 
or whose systolic blood pressure dropped from ≥ 90 
mm Hg before dialysis to < 90 mm Hg during dialysis; 
(3) nadir intradialytic systolic blood pressure was ≥ 
25% lower than their pre-dialysis level; and (4) nadir 
intradialytic systolic blood pressure was ≥ 35 mm Hg 
lower than their pre-dialysis level.
 
However, it should have stated: Finally, we will 
examine five definitions of intradialytic hypotension 
using the same blood pressure data as the key second-
ary outcome at the cluster level. We will examine the 
center’s proportion of patients whose (1) nadir sys-
tolic blood pressure is < 90 mmHg anytime during a 
dialysis session when the value before the session 
was ≥ 90 mmHg, or drop in systolic blood pressure 
≥ 30 mmHg anytime during the session from value 
before session; (2) systolic blood pressure dropped 
from ≥ 90 mm Hg before dialysis to < 90 mm Hg 
during dialysis; (3) nadir intradialytic systolic blood 
pressure was ≥ 25% lower than their pre-dialysis 
level or whose systolic blood pressure dropped from 
≥ 90 mm Hg before dialysis to < 90 mm Hg during 
dialysis; (4) nadir intradialytic systolic blood pres-
sure was ≥ 25% lower than their pre-dialysis level; 
and (5) nadir intradialytic systolic blood pressure was 
≥ 35 mm Hg lower than their pre-dialysis level.”
2) The published statistical plan stated: All analyses 
described in this document will be conducted after 
the trial ends and when the data covering the trial 
period are available at ICES. We expect to complete 
the analysis when the data covering the trial period 
are released from the Office of the Registrar General 
Database (ORGD) (updated releases from this data-
base occur every 2-3 years).
 
Update: All analyses described in the statistical anal-
ysis plan will be conducted after the trial ends and 
when the data covering the trial period are available 
at ICES. We expect to complete the analysis in 2022.
3) The published statistical plan stated: Data on cause 
of death will be obtained from the ORGD, which 
uses a modified version of Becker’s groupings based 
on International Classification of Diseases 10th 
Revision (ICD-10) coding.
 
Update: Data on cardiovascular-related causes of death 
will primarily be captured using the following definition: 
(a) in-hospital (or emergency department) death with a 
cardiovascular disease diagnosis in the primary/most 
responsible diagnosis position or (b) out-of-hospital 
death (including death in an emergency department) 
without documentation of cancer in the 365 days before 
and including the date of death and without documenta-
tion of trauma in the 30 days before and including the 
date of death.2 A death in the emergency department 
with a cardiovascular event as the primary diagnosis will 
be defined as “in-hospital” cardiovascular-related death, 
regardless of whether they had cancer or trauma code.
 
Justification: We were recently notified of unanticipated 
delays in the linkage of the Office of the Register General 
deaths database at ICES, which will now take several 
more years to cover the entire trial period. As a result, we 
will use an alternate method of ascertaining cardiovascu-
lar-related deaths, validated by Lix et al.2 (2021).
References
1. Dixon S, Sontrop J, Al-Jaishi A, et al. MyTEMP: Statistical 
Analysis Plan of a Registry-Based, Cluster-Randomized Clinical 
Trial. Can J kidney Heal Dis. 2021;8:205435812110411. 
doi:10.1177/20543581211041182
2. Lix LM, Sobhan S, St-Jean A, et al. Validity of an algorithm 
to identify cardiovascular deaths from administrative health 
records: a multi-database population-based cohort study. BMC 
Health Serv Res. 2021;21(1). doi:10.1186/S12913-021-06762-0
1110098 CJKXXX10.1177/20543581221110098Canadian Journal of Kidney Health and DiseaseCorrigendum
correction20222022
Corrigendum

MyTEMP PRO 
 
Dr. Amit Garg 
 
 
 
 
Version 3.0 
September 13, 2019  
 
 
MyTEMP sub-study investigating Patient Reported Outcomes (MyTEMP PRO) of patients 
receiving personalized or standard dialysate temperatures 
 
Protocol Version: 3.0 
Funding: 
MyTEMP is funded from partnering organizations including the Ontario Strategy for 
Patient-Oriented Research (SPOR) Support Unit, Lawson Health Research Institute, 
Ontario Renal Network, Dialysis Clinic Inc., Heart and Stroke Foundation of Canada, and 
Canadian Institutes of Health Research (CIHR).  
Roles and Responsibilities 
Principle Investigator: 
• Dr. Amit Garg 
Research Coordinator: 
• Sierra Anderson 
o Assisting with study coordination 
 
 
 
 
 
 
 

MyTEMP PRO 
 
Dr. Amit Garg 
 
 
 
 
Version 3.0 
September 13, 2019  
Table of Contents 
Protocol Summary ........................................................................................................................................ 4 
Background ................................................................................................................................................... 5 
What symptoms and outcomes will be measured? ................................................................................... 6 
Dialysate Temperature and Outcomes .......................................................................................................... 6 
Pain........................................................................................................................................................ 6 
Tiredness, Drowsiness and Problems Sleeping ..................................................................................... 7 
Nausea .................................................................................................................................................. 8 
Appetite ................................................................................................................................................ 9 
Shortness of Breath............................................................................................................................... 9 
Depression .......................................................................................................................................... 10 
Anxiety ................................................................................................................................................ 11 
Itchiness .............................................................................................................................................. 11 
Wellbeing ............................................................................................................................................ 12 
Time to Recover Following Dialysis ..................................................................................................... 13 
Objective ..................................................................................................................................................... 13 
Methods ...................................................................................................................................................... 15 
Study Design ........................................................................................................................................... 15 
Study Setting ........................................................................................................................................... 15 
Eligibility Criteria ................................................................................................................................... 16 
Other Trials ............................................................................................................................................. 16 
Intervention ............................................................................................................................................. 16 
Monitoring: ............................................................................................................................................. 16 
Safety Oversight: .................................................................................................................................... 16 
Outcome: ................................................................................................................................................. 17 
Study Methods ........................................................................................................................................ 17 
Participant Timeline ................................................................................................................................ 18 
Sample Size ............................................................................................................................................. 18 
Recruitment ............................................................................................................................................. 18 
Blinding: ................................................................................................................................................. 19 
Data Collection: ...................................................................................................................................... 19 

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Electronic PRO Measurement Validation ............................................................................................... 20 
Harms and Benefits: ................................................................................................................................ 21 
Data linkage ............................................................................................................................................ 21 
Data Privacy and Security: ...................................................................................................................... 21 
Data Destruction: .................................................................................................................................... 22 
Statistical Analysis: ................................................................................................................................. 22 
Missing Data: .......................................................................................................................................... 22 
Ethics and Dissemination ............................................................................................................................ 22 
Research Ethics Approval: ...................................................................................................................... 22 
Protocol Amendments: ............................................................................................................................ 23 
Declaration of Interests: .......................................................................................................................... 23 
Ancillary and Post-Trial Care ................................................................................................................. 23 
Dissemination Policy: ............................................................................................................................. 23 
References ................................................................................................................................................... 24 
Appendix 1 .................................................................................................................................................. 32 
 
 
 
 
 
 
 
 
 
 
 
 

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Protocol Summary 
 
Objective: To report descriptive symptomology statistics of a group of patients receiving 
personalized dialysate temperatures (0.5°C-0.9°C below patients’ body temperature) and a group 
of patients receiving  usual dialysate temperature of 36.5°C. 
Settings: Ten hemodialysis centres across the London Health Sciences Centre renal program that 
are participating in the MyTEMP clinical trial.  Five centres are allocated to personalized 
reduced dialysate temperature and five centres are allocated to control dialysate temperature. 
Patients: Consenting adults at least 18 years old, with no communication impairment 
(blind/deaf), and reasonable ability to speak and understand English. 
Interventions: MyTEMP PRO will be utilizing treatment allocations from the MyTEMP parent 
randomized trial. 
Measurements: Patients will be asked to: 1) complete the Edmonton Symptom Assessment 
System (revised) Renal (ESASr: Renal) questionnaire using a visual analog scale; 2) how cold 
they feel while on dialysis; 3) severity of muscle cramps; 4) headaches while on hemodialysis; 5) 
record their self-rated health; 6) record if they have missed a session in the last month; and 7) 
record their average amount of time required to recover from their dialysis session (i.e. return to 
normal duties). We will capture this information electronically using a website accessed on a 
tablet.  
Outcomes: ESASr: Renal item scores, self-rated temperature, self-rated cramp severity, self-
rated health, missed sessions, and time to recover following dialysis. 
Planned analysis:  Descriptive statistics will be reported. 
 
 
 
 
 
 
 
 
 
 
 
 

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Background 
 
For persons whose kidneys have permanently failed (~ 3 million worldwide and ~23,000 
in Canada), maintenance hemodialysis provides a life-saving treatment option (1,2). However, 
over 500,000 individuals receiving hemodialysis worldwide and 2,500 in Canada are admitted to 
hospital or die from a major cardiovascular event each year (1–3). We are currently conducting a 
pragmatic cluster randomized controlled trial in Ontario (known as the MyTEMP clinical trial) 
that is investigating if personalized dialysate temperature (0.5°C-0.9°C below patients’ body 
temperature)  reduces the risk of cardiovascular death and cardiovascular events compared to 
usual dialysate temperature of 36.5°C.  The MyTEMP trial involves 84 hemodialysis centres that 
care for more than 7000 patients across Ontario.  This current sub-study will capture patient 
reported outcomes for 10 of the 84 centres participating in MyTEMP; we call this sub-study 
MyTEMP PRO.  MyTEMP PRO aims to assess the symptomology of patients receiving 
personalized dialysate and usual dialysate temperature of 36.5°C. 
The outcomes that are most important to patients are often different than what healthcare 
professional might perceive as important (4).  Patients have routinely placed an emphasis on 
trying to ameliorate the symptoms experienced on hemodialysis, such as itching, cramping and 
poor energy (4,5).  Physicians on the other hand focus more on dialysis care, and outcomes such 
as all-cause mortality (4).  The current pan-provincial primary outcomes of MyTEMP relate to 
cardiovascular mortality and hospitalizations with major cardiovascular events (defined as 
hospitalization for myocardial infarction, ischemic stroke, and congestive heart failure). Further 
evaluation of the dialysate temperature intervention on patient priorities (e.g. symptoms) is 
warranted and is the focus of the current study protocol. 

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What symptoms and outcomes will be measured? 
 
Patients on maintenance hemodialysis are characterized by an extreme reduction in 
quality of life (6). Patients on hemodialysis often require three visits per week to adequately 
remove toxins from the blood, where each session is commonly up to four hours in duration.  
The most frequently reported symptoms include fatigue, headaches, shortness of breath, 
insomnia, nausea and vomiting, and loss of appetite (7).  The modified Edmonton Symptom 
Assessment Scale (ESASr: Renal) can be used in the hemodialysis population to assess symptom 
frequency and burden, with proven cross-sectional validity, as it shown to be highly correlated 
scores with the Kidney Disease Quality of Life Short Form (KDQOL-SF) subscales of 
symptom/problem list, and the lower RAND-12 physical health and mental health composites 
(8). The symptoms measured by ESASr: Renal include pain, tiredness, drowsiness, nausea, 
appetite, shortness of breath, depression, anxiety, well-being, itchiness, problems sleeping, and 
restless legs.  This is included in MyTEMP PRO for symptomology assessment. 
With these symptoms, the quality of life of many patients is drastically reduced, as it can 
often take an extended amount of time to recover from a dialysis session (6).  In addition to the 
ESASr: Renal items, MyTEMP PRO also aims to investigate feeling of coldness of dialysis, 
severity of muscle cramps, general overall health, missed dialysis sessions, self-rated overall 
health and time to recover following dialysis. 
Dialysate Temperature and Outcomes 
Pain  
In a prospective cohort study of 205 Canadians on hemodialysis treatment, 50% of 
patients reported chronic pain as a problem, with 55% of these patients reporting the chronic pain 

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as severe (9) compared with 1% to 14%  in the general population (10,11).  The cause of pain 
has been reported to be highly diverse in the hemodialysis population; however, 63% of 
hemodialysis patients have reported musculoskeletal pain as the most common cause of pain 
(attributable to osteoarthritis and undiagnosed musculoskeletal pain). This study also reported 
suboptimal pain management in patients undergoing long-term hemodialysis therapy.  A 
previous study showed 46% of patients with moderate to severe pain considered withdrawing 
from hemodialysis, compared to 16.7% of patients with no to mild pain (12).  These findings 
suggest a need to further develop treatment options that can reduce the severity of pain in this 
population.   
A reduction in dialysate temperature may reduce pain.  Patients receiving a lowered 
dialysate temperature may be less likely to stop their treatment due to hypotension, and so may 
also experience less frequent muscl cramps and associated pains from trying to take off too much 
fluid after a patient misses a dialysis session. 
Tiredness, Drowsiness and Problems Sleeping 
Tiredness has routinely been reported to be the most frequent symptom for patients on 
hemodialysis.  Previous reports suggest 92% of patients report tiredness, with 73% of the 
patients considering it to be moderate or severe problem (13). Sleep disturbance has been 
reported alongside tiredness where 70% of patients on hemodialysis report sleep disturbance 1-2 
times per week (14).  Fatigue and tiredness is multidimensional that can be attributed to a variety 
of mechanisms, such as: physiology, psychology, social, and hemodialysis treatment itself (15).  
Patients on hemodialysis consider fatigue a key symptom that requires further 
investigation.  Prior evidence  suggests that cooler dialysate temperatures of 35.5°C reduces 

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fatigue scores by 31% on the Piper Fatigue Scale when compared to patients using a dialysate 
temperature of 37°C (16).   
The reduction in fatigue scores may be linked to a reduction in hypotensive events during 
hemodialysis.  This is supported by a number of previous studies suggesting reduced dialysate 
temperatures reduces the severity of fatigue symptoms in hemodialysis patients and patients are 
more energetic after dialysis (17,18).  Drowsiness has not been rigorously measured in the 
hemodialysis population with respect to dialysate temperature; however, there is the possible 
implication of reducing itchiness through reduced dialysate temperatures, which offsets the need 
for drowsiness inducing antihistamines and may directly promote sleep quality (19). 
Nausea  
The symptom of nausea in the hemodialysis populations can typically be associated with 
events of intradialytic hypotension, fluid overload, diet, and hemodialysis effects on the digestive 
system (20).  Previous reports have shown that 18% of patients on maintenance hemodialysis 
experience nausea during their hemodialysis session (21). A cooler dialysate temperature 
increases peripheral vascular resistance, improves cardiac response, and alters the level of 
vasoactive peptides — all which may decrease the risk of intra-dialytic hypotension and thus, 
reduce the frequency of nausea (22,23).  Other mechanisms that may be implicated with reduced 
dialysate temperature is through the promotion of more complete sessions and less frequent 
missed hemodialysis sessions to prevent fluid overload.  Fluid overload, which may occur in 
patients not receiving complete sessions, can result in mucosal edema in the gastrointestinal 
system that may result in nausea (21).   
 

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Appetite 
Loss of appetite is a symptom that occurs in 30 to 40% of patents on chronic 
hemodialysis (24).  Previous research has linked the loss of appetite in hemodialysis patients to 
an increase in systemic inflammation, as measured through an increase in pro-inflammatory 
cytokines (25).   Fluid overload has also been linked to a reduction in appetite, as mucosal edema 
can coincide with early satiety (21).  In addition to a loss of appetite affecting a patient’s quality 
of life, reduced appetite has also be linked to a substantial increase in mortality for patients on 
maintenance hemodialysis(25).   Self-reported measures of appetite have revealed that a loss of 
appetite is worse on days when the patient is undergoing hemodialysis treatment, which may 
lead to the suggestion that approaches to alter hemodialysis sessions may lead to better appetite 
outcomes(26,27).  Uremic toxins are believed to be implicated in the process of anorexia 
development in end-stage renal disease patients on hemodialysis (28).  
Previous reports show a lower dialysate temperature results in higher urea removal, 
which may be implicated in reduced appetite in hemodialysis patients (29).  However, there may 
be added benefits to the use of reduced dialysate temperatures and its implications on appetite.   
A reduction in gastrointestinal edema through the promotion of fluid removal, using reduced 
dialysate temperatures, could ultimately limit anorexia in hemodialysis patients. 
Shortness of Breath 
Many patients on hemodialysis experience fluid overload.  Previous reports show 100% 
of chronic kidney disease patients experience at least mild chronic dyspnea; however, in most 
circumstances, this is treated adequately through dialysis.  Shortness of breath typically occurs in 
patients with fluid overload due to a build-up of fluids in the lungs that limits oxygen exchange.  
Dialysis can address the issue of fluid overload; however, breathlessness is not always improved 

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in patients following treatment (30).  Congestive heart failure, chronic lung disease, pulmonary 
hypertension, lung fibrosis, air micro-embolism, dialyzer bio-incompatibility, anemia, and 
sodium overload are other causes that can be linked to the etiology of breathlessness in dialysis 
patients (30).  
A reduction in dialysate temperatures may provide additional benefits to lowering the 
incidence of dyspnea in hemodialysis patients.  Due to hemodialysis sessions being complicated 
with intradialytic hypotension, lower dialysate temperatures will promote less missed sessions 
and less intradialytic complications to meet fluid removal goals.  A reduction in pulmonary fluid 
may have the added benefit of limiting dyspnea both during dialysis and on days between 
hemodialysis treatments.   
Depression 
Depression has been reported to occur in 43% of patients on hemodialysis (as reported 
through Beck’s Depression Inventory) and is associated with lower self-rated quality of life 
(31,32). It is important to understand the etiology and treatment options for patients with chronic 
kidney disease, as patients who show depressive symptoms have an increased risk of death and 
hospitalizations (33).  Previous links have been found between depression and the following 
factors: dialysis shift, high levels of phosphorus, and lower levels of hemoglobin (31).  
In the typical stroke population, depression is a symptom that occurs in approximately 33% of 
the survivors (34,35).  New evidence has also provided support for the idea that hemodialysis 
can directly cause progressive white matter injury; the level of damage in the brain has been 
associated with dialysis-induced blood pressure instability (36). Should direct white matter injury 
be linked to the development of more frequent or severe depression, the use of personalized 

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dialysate temperatures may provide benefit to the prevention of white matter damage through an 
increase in hemodynamic tolerability (36). 
Anxiety 
The prevalence of anxiety in the hemodialysis population can vary dramatically across 
various studies; the reported incidence can range from 12% to 52%.  A robust, and 
geographically relevant study to MyTEMP PRO, reported an anxiety disorder incidence of 
45.7% in hemodialysis patients in Brooklyn, New York (37).  Similar to depression, the etiology 
of anxiety in hemodialysis patients can be considered multifactorial.  Anxiety, however, is a 
symptom that can be experienced alongside intradialytic hypotension.  Reduced dialysate 
temperatures may be useful for the reduction in intradialytic hypotension, and therefore reduce 
the incidence and severity of anxiety behaviour and disorders.   However, it is also important to 
consider the overlap of anxiety disorder causes in the hemodialysis population, as a life-
threatening illness may invoke anxiety disorders from both a social and psychological 
perspective that is independent of the treatment itself.    
Itchiness 
Itchiness is one of the most concerning symptoms to both patients and physicians (5).  
Approximately 60-80% of dialysis patients experience itchiness that is related to hemodialysis 
(38).  There is currently inadequate treatment for pruritus across the hemodialysis population, as 
traditional treatment options often fail in alleviating the symptoms.   
Uremic toxins are believed to be the driving force for hemodialysis-induced Itchiness, 
which are largely derived from intestinal putrefaction and gastrointestinal dysbiosis. Through the 
mechanistic and physiological properties of dialysis, the hemodynamic stress placed on the gut 

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results in intestinal content translocation.  The liver receives all products of intestinal absorption 
via the portal circulation and is the final barrier before the systemic circulation. Damage to the 
liver has been shown to lead to an impaired functional barrier, leading to a greater exposure to 
these substances systemically. 
An unpublished study (19) has suggested a therapeutic benefit of reduced dialysate 
temperatures to limit the severity of uremic pruritus in the hemodialysis population.  A robust 
controlled randomized clinical trial published results in 2017 provided further support to the idea 
that reduced dialysate temperatures improve itchiness outcomes in the hemodialysis population 
(39).   
Wellbeing 
Utilizing the Personal Wellbeing Index, previous research on wellbeing has shown that 
patients on hemodialysis show a mean score of 64.72, which is statistically different compared to 
the general population (mean score of 74.75) (40).  Through the potential reductions in various 
symptoms previously mentioned, there is the possibility that well-being may be improved.  
Reducing complications, such as intradialytic hypotension, through the use of personalized 
dialysate may have a profound impact.  A reduction in all symptoms previously discussed can be 
improved through reduction in both incidence and severity, and promoting the continuation of 
hemodialysis (fewer missed sessions and fewer sessions with early termination due to intra-
dialytic complications).  No studies have directly investigated this measure to date, thus 
descriptive results from this sub-study would help inform future studies. 
 
 

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Time to Recover Following Dialysis 
Time to recover following dialysis is an important measure, as many patients are unable 
to return to their regular schedule immediately following the hemodialysis session.  It is common 
for patients to tired or drowsy for hours after their hemodialysis session.  The Dialysis Outcomes 
and Practice Patterns Study (2014) found that approximately 68% of patients take more than 2 
hours to recover from their dialysis session, with approximately 27% of patients requiring more 
than 7 hours (6).  Post-dialysis recovery time is likely multifactorial; however, previous research 
has suggested that reduced dialysate temperatures have the potential of drastically reducing 
recovery times in dialysis patients (41) 
Self-Rated Temperature 
Measuring how cold a patient feels on dialysis is integral to understanding the systemic 
effects of personalized dialysate temperature.  There have been previous reports that have shown 
that 20% of patients report feeling cold, and another study has reported 2 out of 10 patients 
shivering on lower dialysate temperatures of 35.0°C (18,42).  Gathering further information in 
this sub-study would provide further insight into the differentiation in temperature sensations 
between personalized dialysate temperatures and standard 36.5°C.   
Muscle Cramps  
 
In a prospective cohort study of 103 patients on hemodialysis the cumulative occurrence 
of muscle cramps over 14,000 hemodialysis treatments was reported to be 86%. Patients on 
hemodialysis commonly experience lower extremity muscle cramping as a result of the removal 
of fluids causing a drop in systolic blood pressure between 20-30 mm Hg. This leads to the 
discomfort of patients in addition to increasing risk factors influencing cardiovascular events, as 
well as ischemic injury to the heart and brain (1). There are currently not many treatments to 

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prevent muscle cramps. However, a personalized lower dialysate temperature has the potential to 
reduce the risk for intradialytic hypotension by up to 70% and improve cardiovascular outcomes 
(2). By reducing the frequency and intensity of intradialytic hypotension patients may experience 
less frequent muscle cramping (44, 45).  
 
Headaches  
A frequently encountered neurological symptom in hemodialysis is headaches 
experienced by 27% - 70% of all patients with chronic kidney disease. The pathophysiology of 
hemodialysis headaches is not fully understood however, triggering factors that contribute to the 
onset of hemodialysis headaches include changing levels in blood pressure, body weight, serum 
sodium and magnesium. (46). It has also been reported that duration of time between dialysis 
sessions increases the occurrence of headaches (46). Treatments to alleviate headache symptoms 
include maintenance of blood volume, electrolytes and blood pressure and avoidance of caffeine. 
In addition a personalized dialysate temperature has the potential to reduce intradialytic 
hypotension episodes ultimately decreasing headache like symptoms.  
 
Objective 
 
Report descriptive statistics for both intervention and control arms of MyTEMP on the 
symptoms of patients through the use of the Edmonton Symptom Assessment System (Revised): 
Renal.  Descriptive statistics will also be reported for time to recover following dialysis, and self-
rated health.   

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Methods 
Study Design 
 
MyTEMP PRO is a nested patient-level cross-sectional qualitative and registry-based 
study that will gather patient reported outcome (PRO) data from the 10 centres participating in 
the MyTEMP trial (~600 patients).  
Study Setting 
 
MyTEMP PRO will involve 10 centres participating in MyTEMP and part of the London 
Health Sciences Centre Renal Program that care for approximately 600 patients.  These sites 
include: 
Parent MyTEMP Intervention arm: 
• Adam Linton Dialysis Unit (London, Ontario) 
• Bluewater Health (Sarnia, Ontario) 
• Chatham-Kent Health Alliance (Chatham, Ontario) 
• Huron Perth Hopps Partnership (Stratford, Ontario) 
• London Health Sciences Centre (London, Ontario) 
Parent MyTEMP Control arm: 
• Grey-Bruce Health Services (Owen Sound, Ontario) 
• Hanover and District Hospital (Hanover, Ontario) 
• Tillsonburg District Memorial Hospital (Tillsonburg, Ontario) 
• Woodstock General Hospital (Woodstock, Ontario) 
• Westmount Kidney Care Centre (London, Ontario) 

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Eligibility Criteria 
 
All patients at each of the centres may participate in this study if they meet the following 
requirements: 
• Able to provide consent 
• 18 years of age or older 
• No communication impairment at baseline (blind/deaf) 
• Reasonable use of English language 
Other Trials 
This is a one-time cross-sectional study, thus there will be no issue for patients to 
participate in other trials. 
Intervention 
There is no active intervention for the current study (MyTEMP PRO); the study is 
utilizing assignments in the parent MyTEMP trial.   
 
Monitoring: 
 
This is a one-time cross-sectional survey with no follow-up and thus will not require 
monitoring.   
Safety Oversight: 
 
An independent Data and Safety Monitoring Board (DSMB) has been established for the 
parent MyTEMP clinical trial.  If the MyTEMP trial is stopped for any reason, the research 
ethics board for MyTEMP PRO will be notified and the study will be ceased.  All centres will be 

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notified of any concerns. No known direct risks are associated with items found in the MyTEMP 
PRO assessment. 
Outcome: 
 
The outcome measure will involve the scores on each of the ESASr: Renal item scores, 
self-rated temperature, self-rated cramp severity, self-rated health, missed sessions, and time to 
recover following dialysis. Descriptive statistics will be reported for both personalized dialysate 
temperature (MyTEMP intervention) and standard 36.5°C dialysate temperature (MyTEMP 
control).   
Study Methods 
MyTEMP PRO will utilize ESASr: Renal to gather PRO data on dialysis-related 
symptomology and will also collect additional data on self-rated temperature, self-rated cramp 
severity, self-rated health, missed sessions and time to recover following dialysis.  Patients will 
be approached by a nurse within their circle of care during their hemodialysis session. Patients 
will be provided with a tablet to view a short three-minute video introducing the MyTEMP PRO 
questionnaire. Patients will also have access to a full letter of information via the tablet, or a 
nurse will provide a printed copy if desire. The video and letter of information will provide 
patients with an overview of the study procedures, voluntary participation, confidentiality and 
what to do should they choose not to participate. 
The idea of an introductory video was presented by patient partners as an additional aid 
to allow patients with physical limitations such as double vision to participate in the trial. The 
script within the study introductory video will be presented by patient partners, in an attempt to 
deliver information at a peer to peer level.   

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A nurse (part of the circle of care) will ask the patient to complete the assessment on a 
provided tablet should they choose to participate.  If the patient is physically unable to complete 
the questionnaire, and a caregiver is not present, the nurse will read aloud each item and record 
the patient’s response. No further contact will be made with the patients following the 
completion of the online symptom assessment.    
Participant Timeline 
 
Data collection is a one-time event that will take approximately 2-5 minutes to complete. 
There will be no additional follow-up with patients.  Only interaction will be during the consent 
process and data collection. 
 
Sample Size 
 
All patients at each of the centres will have the opportunity to enroll into the trial, if they 
meet the inclusion criteria.  We aim to recruit as many patients as possible that are receiving 
hemodialysis across the LHSC renal program. 
Recruitment 
 
A nurse (within the circle of care) will introduce the study to the patient verbally. 
Immediately following the nurse's description of the study, we will ask if we have verbal consent 
to continue. The patient will also be given the option to think about participating in this study 
and if they choose, they may complete the questionnaire while at their next dialysis treatment.  If 
the patient is interested in participating, they will be provided a video outlining the study 
procedures, consent process, and who has access to the study data.  

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Due to the extensive amount of symptoms often experienced during hemodialysis (ex. 
double vision, nausea) our patient partners have expressed that providing a video for patients 
outlining the trial details would be beneficial.  The extensive amounts of written material 
provided during the consent process may become overwhelming for patients during their 
hemodialysis session; therefore we believe the study’s introduction video will provide a clear 
and efficient presentation to recruit a wider range of participants. In addition, an electronic copy 
of a full letter of information will be available for patients via the tablet, as well as paper copies 
if required.                                                                                                                                                                   
Both the video and the letter of information will include sections such as risk, benefits, 
study procedures, an outline of voluntary participation, and confidentiality. Upon receiving the 
information from the video and letter of information, patients will be prompted to agree to 
participate in the study by clicking on the “I agree to the terms and conditions of patient reported 
outcomes while on hemodialysis and consent that my participation in this study is voluntary” 
button. Patients will also have the option to decline participation and hand the tablet back to the 
nurse.   
Blinding: 
 
Patients participating in MyTEMP PRO are not blinded to their allocation in the parent 
MyTEMP trial. 
 
 
Data Collection: 
 

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The Edmonton Symptom Assessment System revised: Renal (ESASr: Renal) 
questionnaire has been converted into a web-based electronic questionnaire.  We are utilizing all 
items found on the ESASr: Renal to assess symptoms.  The same scale as the ESASr: Renal will 
be used; where patients will place their finger on the numbered scale that corresponds to their 
symptom severity (Appendix 1). 
Electronic PRO Measurement Validation 
 
With the use of the electronic system, there is no concern of duplicate entries by patients.   
A pilot study in home dialysis clinics concluded that patients were satisfied with instrument 
completion over a tablet (43).  The Ontario Renal Network is also currently utilizing the 
Interactive Symptom Assessment Collection (ISAAC) tool to assess patient symptomology 
through touch screen kiosks or over the internet on a specifically designed website 
(https://isaac.cancercare.on.ca/).  The ISAAC tool contains ESAS to assess general symptoms 
using the same 0 to 10 scale for each ESAS instrument item.   
Sensation responses to the dialysate temperature will be measured utilizing a 0 to 10 scale, 
similar to the ESAS instrument.  The lower end (0) will be labelled as “No Feeling of Being 
Cold on Dialysis” and the higher end (10) will be labelled as “Worst Possible Feeling of Being 
Cold on Dialysis”. 
We will also be utilizing a measurement of dialysis recovery time that has been validated 
in a large-scale dialysis study (6).  The question is as follows: How long does it take you to 
recover from a dialysis session and resume your normal, usual activities?  Patients will record the 
total number of minutes it typically takes to recover from their hemodialysis session. 

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A health thermometer will also be used to assess the patient’s self-rated health on a scale 
of 0 – worst possible health to 100 – best possible health. 
Harms and Benefits:  
 
There are no expected harms in the completion of the MyTEMP PRO questionnaire; 
however, as with any questionnaire study, there is a minimal risk of privacy breach and some 
participants may feel uncomfortable completing questions related to their symptomology. Should 
patients feel uncomfortable, the participant will not be required to answer the question. The 
patients will be made aware that their survey responses are being used to assess the impact of 
dialysis temperature, not to be used to influence their clinical care.  Patients will be asked to 
communicate symptom concerns to their healthcare team. 
The results of MyTEMP PRO will provide further information on potential benefits of 
personalized dialysate.  Because no statistical comparisons are being made in this study, 
MyTEMP PRO will set a foundation for future patient-reported outcome studies investigating 
personalized dialysate temperatures. 
Data linkage 
We are not collecting any participant identifiers and as such, collected data will not be 
linked to any additional database. 
Data Privacy and Security: 
 
We will not be recording personal identifiers. All questionnaire responses will be 
recorded and stored on the tablet. All data will be encrypted.  
 

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Data Destruction: 
 
 
Any data generated by this study will be destroyed within 8 years of study completion 
(expected date of study completion is March 31st, 2021, expected date of destruction is March 
31st, 2029). Study data specified for destruction will be destroyed according to Western 
University and Lawson Health Research Institute’s standard operating procedures, which include 
document shredding, electronic erasure, and physical destruction of electronic media.  
Statistical Analysis: 
 
We will report descriptive statistics for MyTEMP PRO outcomes for each arm of 
MyTEMP.  This will include the mean, standard deviation, median, and interquartile ranges for 
each individual ESASr: Renal item, feeling of coldness, cramp severity, missed sessions, self-
rated health and time to recover following dialysis.  
Missing Data: 
 
In cases where a patient does not record their response for an item, it will be disregarded 
and not included in the analysis.  The final report will include the number of missing data 
elements for each questionnaire item.   
Ethics and Dissemination 
Research Ethics Approval: 
MyTEMP PRO will be submitted to the local research ethics board (REB) for review and 
approval. 
 
 

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Protocol Amendments: 
 
Should any changes be made to the MyTEMP PRO protocol, the local REB will be 
notified of the respective changes.  This will be done through online submissions. 
Declaration of Interests: 
 
There are no financial or competing interests for the principal investigator or for any site. 
Ancillary and Post-Trial Care 
 
No changes in a patient’s care will be made following their participation in the trial.  
There are no expected harms from their participation in regard to the completion of the online 
questionnaire.   
Dissemination Policy: 
 
Results of this study will be published in a well-respected journal. 
 
 
 
 
 
 

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References 
1.  
Foley RN, Collins AJ. End-Stage Renal Disease in the United States: An Update from the 
United States Renal Data System. J Am Soc Nephrol [Internet]. 2007;18(10):2644–8. 
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Foote C, Ninomiya T, Gallagher M, Perkovic V, Cass A, McDonald SP, et al. Survival of 
elderly dialysis patients is predicted by both patient and practice characteristics. Nephrol 
Dial Transplant. 2012;27(9):3581–7.  
3.  
Jassal SVS, Trpeski L, Zhu N, S F, Hemmelgarn B, Fenton S, et al. Changes in survival 
among elderly patients initiating dialysis from 1990 to 1999. Can Med …. 2007 
Oct;177(9):1033–8.  
4.  
Jun M, Manns B, Laupacis A, Manns L, Rehal B, Crowe S, et al. Assessing the extent to 
which current clinical research is consistent with patient priorities: A scoping review using 
a case study in patients on or nearing dialysis. Can J Kidney Heal Dis [Internet]. 
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Manns B, Hemmelgarn B, Lillie E, Dip SCPG, Cyr A, Gladish M, et al. Setting research 
priorities for patients on or nearing dialysis. Clin J Am Soc Nephrol. 2014;9(10):1813–21.  
6.  
Rayner HC, Zepel L, Fuller DS, Morgenstern H, Karaboyas A, Culleton BF, et al. 
Recovery time, quality of life, and mortality in hemodialysis patients: The Dialysis 
Outcomes and Practice Patterns Study (DOPPS). Am J Kidney Dis [Internet]. 
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7.  
Flythe J, Powell J, Poulton C, Westreich K, Handler L, Reeve B, et al. Patient-reported 
outcome instruments for physical symptoms among patients receiving maintenance 

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dialysis: a systematic review. Am J kidney Dis. 2015;66(6):1033–46.  
8.  
Davison SN, Jhangri GS, Johnson JA. Cross-sectional validity of a modified Edmonton 
symptom assessment system in dialysis patients: A simple assessment of symptom burden. 
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http://dx.doi.org/10.1038/sj.ki.5000184 
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Davison SN. Pain in Hemodialysis Patients: Prevalence, Cause, Severity, and 
Management. Am J Kidney Dis [Internet]. 2003;42(6):1239–47. Available from: 
http://dx.doi.org/10.1053/j.ajkd.2003.08.025 
10.  
Von Korff M, Dworkin SF, Le Resche L, Kruger A. An epidemiologic comparison of pain 
complaints. Pain [Internet]. 1988 Feb [cited 2018 Aug 23];32(2):173–83. Available from: 
http://www.ncbi.nlm.nih.gov/pubmed/3362555 
11.  
Smith BH, Elliott AM, Chambers WA, Smith WC, Hannaford PC, Penny K. The impact 
of chronic pain in the community. Fam Pract [Internet]. 2001 Jun [cited 2018 Aug 
23];18(3):292–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11356737 
12.  
Davison SN, Jhangri GS. The impact of chronic pain on depression, sleep, and the desire 
to withdraw from dialysis in hemodialysis patients. J Pain Symptom Manage. 
2005;30(5):465–73.  
13.  
Davison SN, Jhangri GS. Impact of Pain and Symptom Burden on the Health-Related 
Quality of Life of Hemodialysis Patients. J Pain Symptom Manage [Internet]. 
2010;39(3):477–85. Available from: http://dx.doi.org/10.1016/j.jpainsymman.2009.08.008 
14.  
Brown SA, Tyrer FC, Clarke AL, Lloyd-Davies LH, Stein AG, Tarrant C, et al. Symptom 

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burden in patients with chronic kidney disease not requiring renal replacement therapy. 
Clin Kidney J [Internet]. 2017;10(6):788–96. Available from: 
https://academic.oup.com/ckj/article-lookup/doi/10.1093/ckj/sfx057 
15.  
Jhamb M, Weisbord SD, Steel JL, Unruh M. Fatigue in patients receiving maintenance 
dialysis: a review of definitions, measures, and contributing factors. Am J Kidney Dis 
[Internet]. 2008 Aug [cited 2018 Oct 11];52(2):353–65. Available from: 
http://www.ncbi.nlm.nih.gov/pubmed/18572290 
16.  
Sajadi M, Gholami Z, Hekmatpour D, Soltani P, Haghverdi F. Cold dialysis solution for 
hemodialysis patients with fatigue a cross-over study. Iran J Kidney Dis. 2016;10(5):319–
24.  
17.  
Azar AT. Effect of dialysate temperature on hemodynamic stability among hemodialysis 
patients. Saudi J Kidney Dis Transplant [Internet]. 2009;20(4):596–603. Available from: 
http://www.ncbi.nlm.nih.gov/pubmed/19587499 
18.  
Ayoub A, Finlayson M. Effect of cool temperature dialysate on the quality and patients ’ 
perception of haemodialysis. 2004;19(1):190–4.  
19.  
Tamadon M. Effect of hemodialysis fluid cooling uremic pruritus in hemodialysis 
patients. 2012;1(2):27–8.  
20.  
Daugirdas JT. Measuring Intradialytic Hypotension to Improve Quality of Care. J Am Soc 
Nephrol [Internet]. 2015;26(3):512–4. Available from: 
http://www.jasn.org/cgi/doi/10.1681/ASN.2014090860 
21.  
Chong VH, Tan J. Prevalence of gastrointestinal and psychosomatic symptoms among 

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Dr. Amit Garg 
 
 
 
 
Version 3.0 
September 13, 2019  
Asian patients undergoing regular hemodialysis. Nephrology [Internet]. 2013 Feb [cited 
2018 Aug 23];18(2):97–103. Available from: http://doi.wiley.com/10.1111/nep.12000 
22.  
1982 - Maggiore et al. - Blood temperature and vascular stability during demodialysis and 
hemofiltration.pdf.  
23.  
Parker LA, Limebeer CL, Rock EM, Sticht MA, Ward J, Turvey G, et al. A comparison of 
novel, selective fatty acid amide hydrolase (FAAH), monoacyglycerol lipase (MAGL) or 
dual FAAH/MAGL inhibitors to suppress acute and anticipatory nausea in rat models. 
Psychopharmacology (Berl). 2016;233(12).  
24.  
Lopes AA, Elder SJ, Ginsberg N, Andreucci VE, Cruz JM, Fukuhara S, et al. Lack of 
appetite in haemodialysis patients - Associations with patient characteristics, indicators of 
nutritional status and outcomes in the international DOPPS. Nephrol Dial Transplant. 
2007;22(12):3538–46.  
25.  
Kalantar-Zadeh K, Block G, McAllister CJ, Humphreys MH, Kopple JD. Appetite and 
inflammation, nutrition, anemia, and clinical outcome in hemodialysis patients. Am J Clin 
Nutr. 2004;80(2):299–307.  
26.  
Burrowes JD, Larive B, Cockram DB, Dwyer J, Kusek JW, McLeroy S, et al. Effects of 
dietary intake, appetite, and eating habits on dialysis and non-dialysis treatment days in 
hemodialysis patients: Cross-sectional results from the HEMO study. J Ren Nutr. 
2003;13(3):191–8.  
27.  
Burrowes JD, Larive B, Chertow GM, Cockram DB, Dwyer JT, Greene T, et al. Self-
reported appetite, hospitalization and death in haemodialysis patients: Findings from the 
Hemodialysis (HEMO) Study. Nephrol Dial Transplant. 2005;20(12):2765–74.  

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Dr. Amit Garg 
 
 
 
 
Version 3.0 
September 13, 2019  
28.  
Carrero JJ, Aguilera A, Stenvinkel P, Gil F, Selgas R, Lindholm B. Appetite Disorders in 
Uremia. J Ren Nutr [Internet]. 2008 Jan [cited 2018 Aug 23];18(1):107–13. Available 
from: http://www.ncbi.nlm.nih.gov/pubmed/18089455 
29.  
Muhd Ariffin NF, Naing L, Pisharam J, Maboud Khalil MA, Tamin N, Chong VH, et al. 
Appetite and Gastrointestinal Symptoms in End Stage Renal Disease Patients. J Clin Exp 
Nephrol [Internet]. 2016 [cited 2018 Sep 6];1(2). Available from: http://clinical-
experimental-nephrology.imedpub.com/appetite-and-gastrointestinal-symptoms-in-end-
stage-renal-disease-patients.php?aid=9441 
30.  
Salerno FR, Parraga G, McIntyre CW. Why Is Your Patient Still Short of Breath? 
Understanding the Complex Pathophysiology of Dyspnea in Chronic Kidney Disease. 
Semin Dial [Internet]. 2017 Jan [cited 2018 Aug 23];30(1):50–7. Available from: 
http://www.ncbi.nlm.nih.gov/pubmed/27680887 
31.  
Teles F, de Azevedo V, Miranda C, Miranda M, Teixeira M, Elias R. Depression in 
hemodialysis patients: the role of dialysis shift. Clinics [Internet]. 2014 Mar 1 [cited 2018 
Aug 23];69(3):198–202. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24626947 
32.  
Watnick S, Kirwin P, Mahnensmith R, Concato J. The prevalence and treatment of 
depression among patients starting dialysis. Am J Kidney Dis [Internet]. 2003 Jan [cited 
2018 Aug 23];41(1):105–10. Available from: 
http://www.ncbi.nlm.nih.gov/pubmed/12500227 
33.  
Hedayati SS, Grambow SC, Szczech LA, Stechuchak KM, Allen AS, Bosworth HB. 
Physician-Diagnosed Depression as a Correlate of Hospitalizations in Patients Receiving 
Long-Term Hemodialysis. Am J Kidney Dis [Internet]. 2005 Oct [cited 2018 Aug 

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23];46(4):642–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16183419 
34.  
Espárrago Llorca G, Castilla-Guerra L, Fernández Moreno MC, Ruiz Doblado S, Jiménez 
Hernández MD. Depresión post ictus: una actualización. Neurología [Internet]. 2015 Jan 
[cited 2018 Aug 23];30(1):23–31. Available from: 
http://www.ncbi.nlm.nih.gov/pubmed/22901370 
35.  
Whyte EM, Mulsant BH. Post stroke depression: epidemiology, pathophysiology, and 
biological treatment. Biol Psychiatry [Internet]. 2002 Aug 1 [cited 2018 Aug 
23];52(3):253–64. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12182931 
36.  
Eldehni MT, Odudu A, McIntyre CW. Randomized Clinical Trial of Dialysate Cooling 
and Effects on Brain White Matter. J Am Soc Nephrol [Internet]. 2015;26(4):957–65. 
Available from: http://www.jasn.org/cgi/doi/10.1681/ASN.2013101086 
37.  
Cohen SD, Cukor D, Kimmel PL. Anxiety in Patients Treated with Hemodialysis. Clin J 
Am Soc Nephrol [Internet]. 2016 Dec 7 [cited 2018 Aug 23];11(12):2250–5. Available 
from: http://www.ncbi.nlm.nih.gov/pubmed/27660303 
38.  
Keithi-Reddy SR, Patel TV, Armstrong AW, Singh AK. Uremic pruritus. Kidney Int 
[Internet]. 2007 Aug [cited 2018 Aug 23];72(3):373–7. Available from: 
http://www.ncbi.nlm.nih.gov/pubmed/17429345 
39.  
Rad M, Jaghouri E, Sharifipour F, Rakhshani MH. The Effects of Cool Dialysate on 
Pruritus Status During Hemodialysis of Patients With Chronic Renal Failure: A 
Controlled Randomized Clinical Trial. Iran Red Crescent Med J [Internet]. 2016 Jun 8 
[cited 2018 Sep 6];19(1). Available from: 
http://ircmj.neoscriber.org/en/articles/16828.html 

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40.  
Bennett PN, Weinberg MK, Bridgman T, Cummins RA. The happiness and subjective 
well-being of people on haemodialysis. J Ren Care [Internet]. 2015 Sep [cited 2018 Aug 
23];41(3):156–61. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25819036 
41.  
Azar AT. Effect of dialysate temperature on hemodynamic stability among hemodialysis 
patients. Saudi J Kidney Dis Transpl [Internet]. 2009 Jul [cited 2018 Aug 23];20(4):596–
603. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19587499 
42.  
Toth-Manikowski SM, Sozio SM. Cooling dialysate during in-center hemodialysis: 
Beneficial and deleterious effects. World J Nephrol [Internet]. 2016 Mar 6 [cited 2018 Sep 
7];5(2):166–71. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26981441 
43.  
Schick-Makaroff K, Molzahn A. Strategies to use tablet computers for collection of 
electronic patient-reported outcomes. Health Qual Life Outcomes [Internet]. 2015 Jan 22 
[cited 2018 Oct 11];13(1):2. Available from: 
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44.  Benna, P., Lacquaniti, F., Triolo, G et al. ital. J Neuro Sci (1981) 2: 53  
       https://doi-org.proxy1.lib.uwo.ca/10.1007/BF02351687 
45. Ward, Jordan M., et al “Patient and caregiver involvement in a multicentre clustered  
hemodialysis trial.” CMAJ: Canadian Medical Association Journal, 7, Nov, 2018, p. 
S32+. Available from: https://www-ncbi-nlm-nih-
gov.proxy1.lib.uwo.ca/pmc/articles/PMC6472463/ Accessed 29 May 2019 
 
 

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46. Melo, Eduardo S., Aguair, Filipe C., Rocha-Filho, Sampaio R et al “Dialysis Headdache: a  
Narrative Review.” The Journal of Head and Face Pain. January 2017 P.161-164.  

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Appendix 1 
 
 
Consent  
  
 
 
 
 
  
 
                                      INSERT PATIENT VIDEO 
 
 
 
 
 
 
 
Click HERE to access the full letter of information 

MyTEMP PRO 
 
Dr. Amit Garg 
 
 
 
 
Version 3.0 
September 13, 2019  
Study Title: MyTEMP sub-study investigating Patient Reported Outcomes (MyTEMP PRO) while on hemodialysis.   
Name of Principal Investigator: Dr. Amit Garg  
Email: amit.garg@lhsc.on.ca  
Phone: (519) 685-8500 extension 77867 
 
Consent to Participate in Research 
 
● 
 I understand that I am being asked to complete a 2-5 minute survey of symptoms while I am on and shortly after my dialysis treatment.   
● 
 I have read, or have had it read to me, each page of this Participant Informed Consent Form.  
● 
All of my questions regarding the sub-study and consent form have been answered to my satisfaction. 
● 
I understand that upon consenting to the sub-study, my questionnaire cannot be retracted from the sub-study  
● 
I voluntarily agree to participate in this sub-study. 
  
☐  I agree to the terms and conditions of “MyTEMP sub-study investigating Patient Reported Outcomes (MyTEMP PRO) while on hemodialysis” and consent that my participation in this study is 
voluntary  
 
 
 
 
 
 
 
 
 
 

MyTEMP PRO 
 
Dr. Amit Garg 
 
 
 
 
Version 3.0 
September 13, 2019  
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Pain  
 
               0            1             2             3             4             5             6            7             8             9             10  
Worst Possible Pain 
 
 
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Tiredness 
(Tiredness = lack of 
energy) 
               0            1             2             3             4             5             6            7             8             9             10  
Worst Possible Tiredness 
 
 
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Drowsiness 
(Drowsiness = feeling 
sleepy) 
               0            1             2             3             4             5             6            7             8             9             10  
Worst Possible Drowsiness 
 
 
 

MyTEMP PRO 
 
Dr. Amit Garg 
 
 
 
 
Version 3.0 
September 13, 2019  
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Nausea 
 
               0            1             2             3             4             5             6            7             8             9             10  
 
Worst Possible Nausea 
 
 
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Lack of Appetite 
 
               0            1             2             3             4             5             6            7             8             9             10  
 
Worst Possible Lack of Appetite 
 
 
 
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Shortness of Breath 
 
               0            1             2             3             4             5             6            7             8             9             10  
 
Worst Possible Shortness of Breath 
 
 
 
 

MyTEMP PRO 
 
Dr. Amit Garg 
 
 
 
 
Version 3.0 
September 13, 2019  
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Depression 
(Depression = feeling 
sad) 
               0            1             2             3             4             5             6            7             8             9             10  
Worst Possible Depression 
 
 
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Anxiety 
(Anxiety = feeling 
nervous) 
               0            1             2             3             4             5             6            7             8             9             10  
Worst Possible Anxiety  
 
 
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
Best Wellbeing 
(Wellbeing = how you 
feel overall) 
               0            1             2             3             4             5             6            7             8             9             10  
Worst Possible Wellbeing 
 
 
 

MyTEMP PRO 
 
Dr. Amit Garg 
 
 
 
 
Version 3.0 
September 13, 2019  
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Itching 
 
               0            1             2             3             4             5             6            7             8             9             10  
Worst Possible Itching 
 
 
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Problem Sleeping 
 
               0            1             2             3             4             5             6            7             8             9             10  
Worst Possible Problem Sleeping 
 
 
Renal (ESAS-r: Renal) 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Restless Legs 
(Restless legs = moving 
legs due to discomfort)  
               0            1             2             3             4             5             6            7             8             9             10  
Worst Possible Restless Legs 
 
 
 
 
 
 
 
 
 

MyTEMP PRO 
 
Dr. Amit Garg 
 
 
 
 
Version 3.0 
September 13, 2019  
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Feeling of Being 
Cold on Dialysis 
               0            1             2             3             4             5             6            7             8             9             10  
 
Worst Possible Feeling of Being 
Cold on Dialysis 
 
 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Muscle Cramps 
No Muscle Cramps 
(A sudden painful 
muscle contraction— 
anywhere in the body — 
during dialysis or after 
dialysis — including at 
night) 
  
               0            1             2             3             4             5             6            7             8             9             10  
 
Worst Possible Muscle Cramps 
 
 
Please circle the number that best describes, on average how this symptom has bothered you this past week. 
No Headache on 
Dialysis 
               0            1             2             3             4             5             6            7             8             9             10  
 
Worst Possible Headache on 
Dialysis 
  
 
 

MyTEMP PRO 
 
Dr. Amit Garg 
 
 
 
 
Version 3.0 
September 13, 2019  
 
How good or bad is your health state TODAY? 
          
                                                 0 . . . . . . . . . 10. . . . . . . . . 20 . . . . . . . . . 30 . . . . . . . . . 40 . . . . . . . . . 50 . . . . . . . . . 60 . . . . . . . . . 70 . . . . . . . . . 80 . . . . . . . . . 90 . . . . . . . . . 100 
 
                                 Worst Possible Health                                                                                                                                                                                Best Possible Health 
 
In the past week have you missed at least one dialysis session (where the session was NOT rescheduled)? 
 
 ☐ Yes ☐ No 
 
In the past week on average, how long did it take you to recover from a dialysis session and resume your normal, usual activities?  
 
                
 Minutes (There are 60 minutes in an hour)