Pilot-scale, Single-arm, Observational Study to Assess the Utility of a Machine Learning Algorithm in Assessing Fluid Status in Haemodialysis Patients
Conditions: Volume Overload, Dialysis
Sponsor: Royal College of Surgeons, Ireland

Pilot-scale, single-arm,
observational study to assess the
utility of a machine learning
algorithm to assess fluid status in
haemodialysis patients
Haemodialysis Outcomes & Patient
Empowerment study 03 (HOPE-03)
Protocol date: 28 July 2021
Version 1.0
Number of pages: 30
Principal investigator:
Prof. Conall O’Seaghdha, Beaumont Hospital, Beaumont Road, Dublin D09 V2N0,
Ireland.
Co-investigators:
Dr. Donal Sexton, St. James’ Hospital, James Street, Dublin D08 NHY1, Ireland
Dr. Vicki Sandys, Beaumont Hospital, Beaumont Road, Dublin D09 V2N0, Ireland.
Dr. Colin Edwards, patientMpower Ltd., 60 Merrion Square South, Dublin D02
KX26, Ireland.
www.clinicaltrials.gov NCT 00000000
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Table of Contents
Protocol Summary
4
Flow charts
6
Flow chart 1 Assessments during observation period
7
List of figures and tables
8
List of abbreviations and terms
9
1. Introduction
9
1.1  Medical background
10
1.2  Product profile
11
Figure 1.2: 1 Diagram of machine learning model
12
2. Rationale, objectives and benefit-risk assessment
12
2.1  Rationale for performing the study
13
2.2 Study objectives
13
2.3  Benefit-risk assessment
14
3.  Description of design and study population
15
3.1  Overall design and plan
15
Figure 3.1: 1 Diagram of study design
15
3.1.1   Administrative structure of study
15
3.2  Discussion of study design, including choice of control group
16
3.3  Selection of study population
17
3.3.1  Main diagnosis for study entry
17
3.3.2  Inclusion criteria
17
3.3.3  Exclusion criteria
17
3.3.4  Removal of patients from therapy or assessments
17
3.3.4.1  Removal of individual patients
17
3.3.4.2  Discontinuation of the trial
18
4.  Study observational intervention
18
4.1  Observational intervention to be assessed
18
4.1.1  Identity of observational intervention and comparator(s)
18
4.1.2  Method of assigning patients to observational groups
18
4.1.3  Blinding and procedures for unblinding
18
4.2  Concomitant therapy, restrictions and rescue treatment
18
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4.2.1  Rescue medication, emergency procedures and other treatment
19
4.2.2  Restrictions
19
4.3  Treatment adherence
19
5  Variables and their assessment
19
5.1  Efficacy
19
5.1.1  Efficacy endpoints
19
5.1.1.1  Primary endpoint
20
5.1.1.2  Secondary endpoints
20
5.1.2  Assessment of efficacy
21
5.2  Safety
21
5.3  Other variables
22
5.4  Appropriateness of measurements
22
6 Investigational plan
22
6.1  Visit schedule
22
6.2. Patient recruitment process
23
6.3  Details of trial procedures
23
6.3.1  Baseline clinic visit (start of study)
23
6.3.2  Haemodialysis visits during study observation period
24
6.3.3  Week 2, Week 4 and Week 6 clinic visits
24
6.3.4 Week 8 visit (end of study)
25
7.  Statistical methods
25
7.1  Statistical design and model
25
7.2  Null and alternative hypotheses
26
7.3  Planned analyses
26
7.3.1  Primary analyses
26
7.3.2  Secondary analyses
26
7.3.3  Safety analyses
26
7.3.4  Interim analyses
26
7.3.5  Health economic analyses
27
7.4  Handling of missing data
27
7.5.  Randomisation
27
7.6  Determination of sample size
27
8.  Informed consent, data quality and storage
27
8.1  Study approval, patient information and informed consent
27
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8.2  Data quality assurance
27
8.3  Records
27
8.3.1  Source documents
27
8.3.2  Direct access to source data and documents
28
8.3.3  Storage of records
28
8.4  Statement of confidentiality
28
8.5  Completion of trial
28
8.6  Protocol violations
28
8.7  Compensation available in the event of study-related injury
28
9. References
29
10. Appendices
30
Appendix 10.1 Cointerventions protocol
30
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Protocol Summary
Product
Machine learning algorithm to assess fluid status
www.clinicaltrials.gov
identifier
Protocol title
Pilot-scale, single-arm, observational study to
assess the utility of a machine learning algorithm
to assess fluid status in haemodialysis patients
Principal investigator
Prof. C. O’Seaghdha, Beaumont Hospital,
Beaumont Road, Dublin D09 V2N0, Ireland
Number of study sites
1: Beaumont Hospital dialysis centre.
Study objectives
To determine the validity and reproducibility of a
machine learning algorithm that assesses fluid
status compared with bioimpedance and clinical
dry weight  estimation in ambulatory
haemodialysis patients.
Methodology
Open, prospective, single-arm observational
Number of subjects
Total treated: 20
Diagnosis
End Stage Kidney Disease requiring regular
ongoing ambulatory haemodialysis
Main criteria for inclusion
Age ≥18 years, demonstrated understanding of
study processes, written informed consent
Test product/intervention
Fluid status estimation model
Comparator
product/intervention
None
Duration of observation
8 weeks/patient
Primary endpoint(s)
Fluid status predicted by machine learning
algorithm compared to bioimpedance data
Secondary endpoint(s)
Symptoms, adverse events.
Interim analysis
None planned
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Statistical methods
Descriptive tables or figures will be prepared.
Within-patient comparison of fluid status data as
determined by different methodologies.
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Flow charts
Flow chart 1
Assessments during observation period
study observation period (8 weeks)
Baseline
visit
At each
dialysis
session
Week 2
Week 4
Week 6
Week 8
(end)
Informed consent
X
Demographic data (include
dialysis & medicines
history)
X
Vital signs, body weight,
routine hemodialysis
measurements in-clinicA
X
X
X
X
Assess fluid status with
bioimpedanceB
X
X
X
Estimate fluid status using
machine learning
algorithmC
X
X
X
X
Record symptoms related
to fluid statusD
X
X
X
Record adverse eventsE
X
X
X
End of study
X
A Routine measurements (at each hemodialysis session) include total fluid
removal, interdialytic weight gain (IDWG), proportion of haemodialysis sessions
in which IDWG is ≤ 4%, pre-dialysis and post-dialysis weight, pre-dialysis,
intradialytic and post dialysis BP and pulse rate.
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B Fluid status will be assessed by bioimpedance measurements immediately
before start of haemodialysis.
C The machine learning model is a software program which examines the
demographic data, recent vital signs, body weight and hemodialysis measures to
predict a patient’s fluid status.
D Symptoms related to fluid status will be recorded before haemodialysis.
E For example, requirement for nursing intervention such as stopping dialysis or
administering fluid bolus. Adverse events include, but are not limited to muscle
cramps, symptomatic hypotension, access-related complications,
hypertension-related symptoms, congestive heart failure, pulmonary edema,
requirement for additional unscheduled dialysis, or need for hospitalisation and
changes in number and type(s) of BP medications.
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List of figures and tables
Figure 1.2: 1
Diagram of machine learning model
Figure 3.1: 1
Diagram of study design
List of abbreviations and terms
BP
blood pressure
BVM
blood volume monitoring
CV
cardiovascular
DTIF
Disruptive Technologies Innovation Fund
ECW
extracellular water
HOPE
Haemodialysis Outcomes & Patient Empowerment
IDWG
interdialytic weight gain (fluid weight gain between dialysis
sessions)
L
litre
OH
overhydration
PND
paroxysmal nocturnal dyspnea
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1.
Introduction
1.1
Medical background
End stage kidney disease is defined by kidney failure severe enough to require
some form of renal replacement therapy. This replacement has three forms,
kidney transplant, haemodialysis and peritoneal dialysis. Haemodialysis is the
most common form of replacement therapy and usually entails three sessions of
dialysis per week on alternate days starting either on Monday or Tuesday. The
period between dialysis sessions is known as the interdialytic period and as
dialysis is delivered on alternate days there is one interdialytic period per week
that is longer than the other. In order to maintain extracellular fluid balance and
to prevent high blood pressure, haemodialysis sessions focus on removing the
fluid that was gained between dialysis sessions.
Haemodialysis patients experience high rates of mortality, driven largely by an
exceptionally high rate of cardiovascular (CV)-related mortality, which exceeds
that of the general population by 10-to 20-fold [1, 2]. High levels of both
interdialytic weight gain and rate of fluid removal during dialysis have been
linked with increased mortality, particularly during the longer interdialytic period
[1]. It is postulated that the haemodynamic effects of high fluid removal rates
during dialysis may result in cardiovascular morbidity and mortality. Similarly,
volume depletion induced by excessive ultrafiltration is associated with
myocardial stunning, cerebrovascular events and mortality [3,4].
However, at present no method exists that can consistently refine volume status
and provide patients with feedback to allow adjustments to their fluid intake.
Current standards used to assess volume are either poorly predictive of fluid
status, cumbersome to use, or lack an adequate patient interface [1].
The advent of the electronic health record has given us access to large quantities
of patient data, which are ideal for data analytics and machine learning [2, 3].
Dialysis datasets contain large amounts of heterogeneous clinical data collected
at thrice weekly intervals, creating the ideal environment to form individualised
predictive algorithms. Machine learning methods have been applied to guide
complex decision making within the dialysis unit [4, 5, 6], and in the prediction of
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volume-related adverse events, such as intradialytic hypotension [7] and dry
weight.
Volume assessment is arguably an ideal task for machine learning. Each method
regularly used to assess volume, such as blood pressure (BP), and the relative
blood volume slope, has partial predictive power in assessing fluid status [8,9].
Machine learning has the ability to synthesise multiple variables with partial
predictive power in order to improve prediction accuracy. Several prototype
models for dry weight assessment exist [10, 11, 12] but to date, only one of
these models has been assessed in a prospective clinical trial [10].
We have created a prototype machine learning model based upon the data of
100,000 dialysis patients collected over 1 year from an anonymised database
provided by Fresenius.
This study will evaluate if this machine-learning model for assessing fluid status
is acceptable and usable for managing fluid volume in haemodialysis patients,
while also assessing validity and reproducibility against validated
measurements.
As the machine learning model for assessing fluid status was trained and tested
on retrospective data, there is sufficient justification in testing the performance
plus acceptability and usability of the model in a controlled observational setting
in a prospective study. To date, this approach has not been explored in
haemodialysis patients.
This is a pilot-scale study to assess if this approach has merit and could be
tested and developed further.
1.2
Product profile
The machine learning model is a software program designed to assess fluid
status in patients with end stage renal disease. It was developed on a large
retrospective global dataset of anonymised dialysis patient data.  The
machine-learning model software program was designed to predict fluid status
using patient demographic information and data from their recent dialysis
sessions. This machine-learning model software program was trained using
standard algorithm approaches on the global dialysis dataset.
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In this prospective pilot study, the machine-learning model will be run every two
weeks for each patient on their recently recorded information to estimate their
fluid status. The inputs for the machine-learning model include demographic
data (include dialysis & medicines history), vital signs, body weight and routine
hemodialysis measurements in-clinic for the patient. The output from the
machine-learning model is the predicted fluid status individualised for the
patient. An illustration of the inputs and output for the machine-learning model
is shown in Figure 1.2: 1 below.
Figure 1.2: 1
Diagram of machine learning model
The study team will complete all tasks required to run and assess the
machine-learning model. (No action is required by the patient in running or
assessing the machine-learning model.) The machine-learning predicted fluid
status will not be used to adjust the patient’s treatment regimen and the patient
will not be informed of the machine-learning predicted fluid status.
2.
Rationale, objectives and benefit-risk assessment
2.1
Rationale for performing the study
This study will evaluate if a machine learning model to estimate fluid status is
acceptable and usable for managing fluid volume in haemodialysis patients, and
will also evaluate validity and reproducibility against validated measurements.
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This is a pilot-scale study to assess if this approach has merit and could be
tested and developed further.
As the machine-learning model for estimating fluid status was trained and
tested on retrospective data, there is sufficient justification in testing the
performance plus acceptability and usability of the model in a controlled
observational setting.
2.2
Study objectives
The objective of this pilot-scale study is to assess how the machine learning
estimation model functions when used in a range of haemodialysis patients of
varying age, gender, body habitus and volume status.
The primary objective is to determine the validity and reproducibility of the
machine learning estimation model  in haemodialysis patients. (The goal is to
determine if the machine learning estimation model can provide actionable
insights to better manage fluid overload in the interdialytic period.)
Haemodialysis parameters will be assessed as usual during the study and will
include:
●
total fluid removal (each haemodialysis session)
●
interdialytic weight gain (IDWG)
●
proportion of haemodialysis sessions in which IDWG is ≤ 4%.
●
pre-dialysis weight
●
post-dialysis weight
●
Pre-, intra- and post-dialysis BP
●
pulse rate
●
dialysis access pressures
●
dialysis flow rate and blood flow rate
●
dialysis temperature
●
routine haemodialysis monthly haematology (e.g. haemoglobin) and
blood chemistry [renal profile (e.g. sodium, potassium), bone profile (e.g.
calcium, phosphate)] data
●
number and type(s) of BP medications
●
requirement for nursing interventions (e.g. stopping dialysis or
administering fluid bolus)
●
adverse events requiring or prolonging hospitalisation
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●
requirement for additional unscheduled dialysis
Fluid status estimated by the machine learning estimation model will be
compared with bioimpedance findings.
Patient-reported symptoms related to haemodialysis and their underlying
medical condition will be recorded before haemodialysis. Adverse events and
unscheduled interventions will be recorded. The effect of symptoms, adverse
events and interventions on the performance of the model will be evaluated.
2.3
Benefit-risk assessment
This study is designed to contribute to the development of a multivariate
algorithm to help haemodialysis patients monitor and manage their fluid intake.
The machine learning estimation model will be used to estimate fluid removal
requirements during multiple haemodialysis sessions. The estimated values will
be compared with actual values to assess the validity of the machine learning
estimation model.
The machine learning estimation model will not be used to adjust the patient’s
haemodialysis treatment regimen during this study. The machine learning
estimation model does not provide feedback to the patient. It is not expected
that the study or the machine learning estimation model will create any
additional risks for the haemodialysis patients in this study.
All patients will receive optimal haemodialysis during the study. In addition, the
on-call nephrology registrar at Beaumont hospital will be available to handle any
medical enquiries from patients enrolled in the study.
3.
Description of design and study population
3.1
Overall design and plan
Prospective, open-label run-in observation period (8 weeks). (See Figure 3.1: 1
for diagram of design.)
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The study will not make any changes to the usual haemodialysis schedule for the
patients.
Figure 3.1: 1
Diagram of study design
3.1.1
Administrative structure of study
This is a single centre study at the Beaumont Hospital, a tertiary care centre for
nephrology in Dublin, Ireland.
Fresenius supplied an anonymized dataset which was used to create the
machine learning model to estimate fluid status which is being evaluated in this
study. Fresenius did not have any input into the design of this study.
The study is being conducted as part of the Haemodialysis Outcomes & Patient
Empowerment (HOPE) project supported by the Disruptive Technology
Innovation Fund of Ireland (DTIF). The HOPE project is a collaboration between
the Royal College of Surgeons in Ireland, Beaumont Hospital, patientMpower
Ltd., 60 Merrion Square South, Dublin D02 KX26 and Sixty (registered as Design
to Value Ltd.), Innishannon, Co. Cork. The objective of HOPE is to develop
artificial intelligence-enabled software to improve determination and
maintenance of fluid balance in haemodialysis patients with a long-term goal of
increasing the proportion of patients receiving dialysis at home. This study is a
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collaboration between all partners in HOPE and the data and outcomes will be
shared with all partners. The study is funded by the DTIF.
The protocol was designed by Dr. Conall O’Seaghdha (Principal Investigator), Dr.
Vicki Sandys (both of Beaumont Hospital, Dublin 9, Ireland), Dr. Donal Sexton, St.
James’ Hospital, James Street, Dublin D08 NHY1, Ireland and patientMpower Ltd.,
60 Merrion Square South, Dublin D02 KX26, Ireland.
The study sponsor is the Royal College of Surgeons in Ireland, 123 St. Stephen’s
Green, Dublin D02 YN77, Ireland.
patientMpower will be the Data Processor (on behalf of the HOPE project
partners) for data collected in this study.
3.2
Discussion of study design, including choice of control group
This is a pilot-scale, prospective, open-label, single-arm observational study.
All patients will be allocated to the same observation sequence.
Patients will follow their usual haemodialysis regimen throughout the study.
Clinical assessments (e.g. IDWG, BP, pulse rate, ultrafiltration volume,
symptoms) will be assessed at each dialysis visit.
The study design will allow assessment of the validity and reproducibility of data
provided by the machine learning fluid status estimation model in haemodialysis
patients. These data  will be compared against other validated methods of
volume assessment (haemodialysis machine and bioimpedance data) and with
observed symptoms in patients across a range of age, genders and volume
status.
The study will also allow evaluation of the utility and acceptability of the machine
learning fluid status estimation estimation model in a haemodialysis patient
population. This will be assessed from the healthcare professional perspective.
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3.3
Selection of study population
3.3.1 Main diagnosis for study entry
Require maintenance haemodialysis in an ambulatory care setting.
Patients with a range of baseline fluid status will be considered for enrollment.
3.3.2 Inclusion criteria
Aged at least 18 years
Demonstrates understanding of the study requirements.
Willing to give written informed consent.
3.3.3 Exclusion criteria
Conditions precluding accurate use of bioimpedance (e.g. limb amputations,
severe malnourishment, pregnancy, cardiac resynchronisation devices,
pacemakers).
Significant confusion or any concomitant medical condition, which would limit
the ability of the patient to record symptoms or other parameters.
3.3.4 Removal of patients from therapy or assessments
3.3.4.1
Removal of individual patients
Patients are free to withdraw from the study at any time without any impact on
their ongoing medical care.
The investigator may withdraw a patient from the study at any time if they
believe that further participation in the study is not in the best interests of the
patients.
3.3.4.2
Discontinuation of the trial
The study may be terminated early if recruitment is significantly behind schedule
or if for any other reason, it is unlikely that the study can be completed.
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4.
Study observational intervention
All patients will continue to receive their usual haemodialysis regimen
throughout the study as prescribed by their healthcare professionals.
4.1
Observational intervention to be assessed
4.1.1 Identity of observational intervention and comparator(s)
The machine learning estimation model is a software program to assess fluid
level that was developed on a large retrospective dataset of anonymised dialysis
patient data. The machine-learning model processes recent information for the
dialysis session and the patient to predict their fluid status. The recent
information includes demographic data (including dialysis & medicines history),
vital signs, body weight and their routine hemodialysis measurements in-clinic.
The patient will not be involved in running or assessing the machine-learning
model software program. The machine-learning model predicted fluid status will
not be used to assess the patient’s dialysis regimen and no information on the
machine-learning model predicted fluid status will be provided to the patient.
There is no comparator intervention.
4.1.2 Method of assigning patients to observational groups
All patients who give informed consent and enter the study will be allocated to
the same observation sequence.
4.1.3 Blinding and procedures for unblinding
The study is open-label.
4.2
Concomitant therapy, restrictions and rescue treatment
4.2.1 Rescue medication, emergency procedures and other treatment
If any patient or their healthcare professional has a concern the renal registrar
on call at Beaumont Hospital will respond to calls out of hours and at weekends.
Any exacerbations of the patient’s underlying medical condition(s) should be
treated according to standard procedures.
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Any other adverse events should be treated according to standard procedures.
4.2.2 Restrictions
There are no restrictions on concomitant treatment. All concomitant treatments
as prescribed by the patients’ healthcare professionals are allowed. Patients will
continue to take all medicines and other treatments as prescribed by their
healthcare professionals. As per the co-interventions protocol (Appendix 10.1),
all nephrologists working at Beaumont Hospital will be asked not to alter the
patients’ usual fluid balance management during the study.
The study does not mandate any additional restrictions on diet or life-style.
Patients will continue to follow all instructions on diet, exercise and lifestyle as
directed by their healthcare professionals.
4.3
Treatment adherence
Patients will be asked to follow all study-specific instructions as directed by their
healthcare professionals.
5
Variables and their assessment
5.1
Efficacy
5.1.1 Efficacy endpoints
5.1.1.1
Primary endpoint
The primary endpoint will be to determine the validity and reproducibility of the
machine learning model to estimate dry weight in assessing fluid removal
requirements in haemodialysis patients. This will be evaluated by comparing
validated monitoring techniques such as bioimpedance.
The primary endpoint variables include:
●
volume status as measured by bioimpedance pre-dialysis.
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●
volume overload as defined by bioimpedance: overhydration
(OH)/extracellular water (ECW) > 15% (corresponding to 2.5L)
●
normohydration defined as: ECW -7 to + 7% (corresponding to -1.1L to +
1.1L)
5.1.1.2
Secondary endpoints
The secondary endpoints are:
●
to evaluate the performance of the machine learning model in assessing
fluid status compared to clinical estimation of dry weight.
●
to evaluate the effect of patient symptoms, adverse events and dialysis
data (including relative plasma volume) on the performance of the
machine learning model.
Patient symptoms (before haemodialysis) will be recorded and before
haemodialysis [at baseline, weeks 2, 4, 6 and at week 8 (end of study)].
Symptoms of hypovolaemia:
●
thirst directly after haemodialysis
●
symptomatic hypotension, on change of position
●
symptomatic hypotension
●
nausea and vomiting
●
muscle cramps
●
limpness/ tiredness between dialysis sessions
●
dizziness between dialysis sessions
Symptoms of hypervolaemia
●
chronic coughing (new)
●
dyspnea on exertion
●
dyspnea at rest; 1 pillow
●
dyspnea at rest: 2 pillow
●
dyspnea at rest: 3 pillows
●
pretibial oedema
●
paroxysmal nocturnal dyspnea (PND)
Haemodialysis parameters will be assessed and will include:
●
total fluid removal (each haemodialysis session)
●
IDWG
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●
proportion of haemodialysis sessions in which IDWG is ≤ 4%.
●
pre-dialysis and post-dialysis weight
●
pre-dialysis, intradialytic and post dialysis BP
●
pulse rate
●
number and type(s) of BP medications
●
dialysis access pressures
●
dialysis flow rate and blood flow rate
●
dialysis temperature
●
routine haemodialysis monthly haematology (e.g. haemoglobin) and
blood chemistry [renal profile (e.g. sodium, potassium), bone profile (e.g.
calcium, phosphate)] data
5.1.2 Assessment of efficacy
The primary and secondary endpoint data will be listed and tabulated or
displayed graphically.
The primary endpoint will be to evaluate the validity and reproducibility of the
machine learning algorithm in estimating fluid status when compared to
bioimpedance data. The primary endpoint will be assessed by comparison of the
fluid status output of the algorithm versus the fluid status determined by
bioimpedance.
5.2
Safety
It is not anticipated that any safety issues will arise from use of the dry weight
estimation model or bioimpedance assessment. Issues regarding abnormal BP
readings will be addressed by the nephrology department at Beaumont
Hospital.
Adverse events will be recorded and assessed. Adverse events include, but are
not limited to:
●
muscle cramps, symptomatic hypotension, access-related complications,
hypertension-related symptoms, congestive heart failure, pulmonary
edema, requirement for additional unscheduled dialysis, or need for
hospitalisation
●
requirement for nursing intervention such as stopping dialysis or
administering fluid bolus
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Any adverse events observed with medical treatments should be reported to the
manufacturers or suppliers of those treatments.
5.3
Other variables
Demographic data [e.g. date of birth, gender, medical cause of haemodialysis,
time since first ever dialysis, vascular access route for haemodialysis,
medication(s) prescribed for hypertension].
5.4
Appropriateness of measurements
The primary efficacy endpoint variables are measurements which are important
and accepted measures of fluid status and renal function in haemodialysis
patients.
6
Investigational plan
Patients who participate in the study will follow their usual haemodialysis
regimen for approximately eight weeks. The machine learning estimation model
will be used to estimate fluid status of patients during the study. In addition to
usual care, participating patients will have fluid status measured by
bioimpedance on five occasions.
6.1
Visit schedule
The total observation period will be approximately eight weeks. It is anticipated
that patients will attend the clinic for haemodialysis three times per week on
average. No additional patient visits are required for this study.
6.2. Patient recruitment process
Approximately 300 patients attend for dialysis under the governance of
Beaumont Hospital. Eligible individuals will be invited to participate. It is
estimated that twenty (20) people will be enrolled in this study.
The study centre will publicise the study to their potential patient population.
When potential patients who are interested in the study attend for a usual care
haemodialysis visit, the research staff will discuss the study (face-to-face) with
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the patient. If the person wishes to participate, they will give written informed
consent. The patient will receive a paper and electronic copy of the patient
information document.
The  study observation period can start after written informed consent.
6.3
Details of trial procedures
The trial procedures at each visit are summarized in the flow chart on page 6.
6.3.1 Baseline clinic visit (start of study)
At a planned usual care haemodialysis visit, the study centre research team will
discuss the study with the patient and confirm the patient's understanding of
the study processes and their willingness to participate. The patient information
document will be given to the patient. The patient will have the opportunity to
ask questions on the study processes and will be asked to give their written
informed consent before any study-specific procedures.
Demographic data, vital signs, medical history and concomitant therapy for renal
and other relevant conditions will be recorded.
At the baseline visit and at each haemodialysis visit during the study the
following usual care parameters will be assessed and recorded:
●
total fluid removal (each haemodialysis session)
●
IDWG
●
proportion of haemodialysis sessions in which IDWG is ≤ 4%.
●
pre-dialysis and post-dialysis weight
●
pre-dialysis, intradialytic and post-dialysis BP
●
pulse rate
●
dialysis access pressures
●
dialysis flow rate and blood flow rate
●
dialysis temperature
●
requirement for additional nursing intervention (e.g.stopping dialysis,
administering fluid bolus)
●
adverse events related to the patient’s underlying condition (e.g.
access-related complications, hypertension-related symptoms, congestive
heart failure, pulmonary edema)
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●
requirement for additional unscheduled dialysis, or need for
hospitalisation
●
number and type(s) of BP medications
In addition, fluid status will be assessed by bioimpedance immediately before
haemodialysis.
Patient symptoms related to volume status (questions described above in
section 5.1.1.2) will be recorded before haemodialysis.
The research team will estimate fluid status using the machine learning
estimation model.
6.3.2
Haemodialysis visits during study observation period
The patient will attend for haemodialysis as required for an observation  period
of approximately three weeks.
The measurements related to haemodialysis described in section 6.3.1 (usual
care haemodialysis parameters) will be recorded at each haemodialysis visit
during the study observation period.
6.3.3 Week 2, Week 4 and Week 6 clinic visits
The measurements related to haemodialysis described in section 6.3.1 (usual
care haemodialysis parameters) will be recorded.
Symptoms (related to fluid status) will be assessed before haemodialysis.
In addition, fluid status will be assessed by bioimpedance immediately before
haemodialysis.
The research team will estimate fluid status using the machine learning
estimation model.
6.3.4 Week 8 visit (end of study)
The measurements related to haemodialysis described in section 6.3.1 (usual
care haemodialysis parameters) will be recorded.
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Symptoms (related to fluid status) will be assessed before haemodialysis.
In addition, fluid status will be assessed by bioimpedance immediately before
haemodialysis.
The research team will estimate fluid status using the machine learning
estimation model.
The study procedures are concluded at this visit.
7.
Statistical methods
This a pilot-scale study to determine the validity and utility of the machine
learning estimation model to assess fluid status model in haemodialysis
patients.
7.1
Statistical design and model
This is a prospective, open-label, single-arm observational study.
All patients will attend for haemodialysis according to their usual regimen during
the study.
7.2
Null and alternative hypotheses
Not relevant.
7.3
Planned analyses
Results will be collected and summarized for descriptive statistical display.
7.3.1 Primary analyses
The objective of this pilot-scale study is to assess how the machine learning
estimation model functions when used in a range of haemodialysis patients of
varying age, gender, body habitus and volume status.
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The primary endpoint will be assessed by comparison of the fluid status
determined using the machine learning estimation model with those observed
via bioimpedance and haemodialysis machine data.
7.3.2 Secondary analyses
Patient-reported symptoms related to haemodialysis and their underlying
medical condition will be recorded before haemodialysis. Symptom data will be
assessed and compared with fluid status data.
An additional objective of this observational study is to assess the acceptability
and utility of the dry weight estimation model (from the healthcare professional
perspective).
7.3.3 Safety analyses
Patient-reported symptoms and adverse events will be tabulated and displayed.
7.3.4 Interim analyses
None planned.
7.3.5 Health economic analyses
Any additional healthcare resource utilisation (e.g. additional unplanned
haemodialysis sessions, hospitalisation) will be tabulated and displayed.
7.4
Handling of missing data
No imputations of missing data will be made.
7.5. Randomisation
Not relevant. All patients will be allocated to the same observation sequence.
7.6
Determination of sample size
This is a pilot-scale study. The sample size (n = 20) was chosen arbitrarily.
8.
Informed consent, data quality and storage
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8.1
Study approval, patient information and informed consent
The study will be approved by the relevant ethics committee(s) for the
participating centre.
The study will be discussed with each patient and they will be provided with a
written document describing the study conditions and procedures.
All patients will give written informed consent before enrollment and any
study-specific procedures.
8.2
Data quality assurance
All endpoint data will be stored on a central database for analysis. The data as
reported by the patients will not be queried before descriptive statistical analysis
tables are prepared.
8.3
Records
8.3.1 Source documents
The original electronic data and relevant medical records will be the source
documents.
8.3.2 Direct access to source data and documents
The research team will ensure that all data entered to the central database is a
true record of events. Source data verification will not be performed.
8.3.3 Storage of records
Medical data relating to patient care will be stored in the medical records
according to the usual procedures of the treatment site(s).
The endpoint data, haemodialysis parameters (including bioimpedance data)
and symptoms data will be stored on a central database managed by
patientMpower Ltd. These data will be pseudonymised and will be shared with
all partners in the HOPE project i.e. Beaumont Hospital research team, Design to
Value Ltd. and patientMpower Ltd.
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8.4
Statement of confidentiality
Patients will only be identified by a unique identification number on the study
database (i.e. data will be pseudonymised). All data will be treated as
confidential. Each patient’s data will be linked to their unique identification
number.
8.5
Completion of trial
The trial will be complete when 20 patients have completed the 8-week
observation period.
If it appears to be unlikely that the target number of patients can be achieved
(e.g. because of slow recruitment) a lower target will be set (after discussion and
agreement with the investigator).
8.6
Protocol violations
All data will be analysed on an intention-to-treat basis without regard to protocol
violations.
8.7
Compensation available in the event of study-related injury
It is not anticipated that any study-related injury will occur.
9.
References
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mortality among patients receiving hemodialysis. N Engl J Med 2011; 365:
1099-1107.
2. Collins AJ, Foley RN, Herzog C, et al. Excerpts from the US Renal Data
System 2009 Annual Data Report. Am J Kidney Dis 2010; 55: S1-420,
A426-427.
3. Burton J, Jefferies H, Selby N, McIntyre C. Hemodialysis-induced cardiac
injury: determinants and associated outcomes. Clin J Am Soc Nephrol
2009; 4: 914-20
4. Shoji T, Tsubakihara Y, Fujii M, Imai E. Hemodialysis-associated
hypotension as an independent risk factor for two-year mortality in
hemodialysis patients. Kidney International 2004; 66: 1212-20.
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5. Flythe JE, Chang TI, Gallagher MP, Lindley E, Madero M, Sarafidis PA, et al.
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10.Wang Y-F, Hu T-M, Wu C-C, Yu F-C, Fu C-M, Lin S-H, et al. Prediction of
target range of intact parathyroid hormone in hemodialysis patients with
artificial neural network. Comput Methods Programs Biomed. 2006;83:
111–119.
11.Lee H, Yun D, Yoo J, Yoo K, Kim YC, Kim DK, et al. Deep Learning Model for
Real-Time Prediction of Intradialytic Hypotension. Clin J Am Soc Nephrol.
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12.Maduell F, Arias M, Massó E, Fontseré N, Carrera M, Vera M, et al.
Sensitivity of blood volume monitoring for fluid status assessment in
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improved cardiovascular management: the necessity of combining blood
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14.Niel O, Bastard P, Boussard C, Hogan J, Kwon T, Deschênes G. Artificial
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1799–1803.
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15. Kim HR, Bae HJ, Jeon JW, Ham YR, Na KR, Lee KW, et al. A novel approach
to dry weight adjustments for dialysis patients using machine learning.
PLoS One. 2021;16: e0250467.
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doi:10.1155/2021/6627650
10. Appendices
Appendix 10.1 Cointerventions protocol
All nephrologists working at Beaumont Hospital will be asked not to alter the
patients’ usual fluid balance management during the study
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