Machine Learning Modeling of Intraoperative Hemodynamic Predictors of 30-day Mortality and Major In-hospital Morbidity After Noncardiac Surgery: a Retrospective Population Cohort Study
Conditions: Perioperative/Postoperative Complications, Morbidity, Multiple, Surgery, Anesthesia, Death
Sponsor: Janny Xue Chen Ke

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Machine learning modeling of intraoperative hemodynamic 
predictors of 30-day mortality and major in-hospital morbidity after 
noncardiac surgery: a retrospective population cohort study 
 
Updated protocol (May 2020) 
 
Principal Investigator: 
Xue Chen (Janny) Ke, MD, Anesthesiology Resident, Dalhousie University; M.Sc. 
Epidemiology Candidate, T.H. Chan of Public Health, Harvard University 
 
Supervising Investigators: 
• E. Francis Cook, PhD, Professor of Epidemiology, T.H. Chan of Public Health, Harvard 
University (and designate)  
• David B. MacDonald, MD, FRCPC, Assistant Professor, Department of Anesthesia, Pain 
Management & Perioperative Medicine, Dalhousie University 
• Ronald B. George, MD, FRCPC, Professor, Department of Anesthesia, Pain Management 
& Perioperative Medicine, Dalhousie University; Professor and Chief of Obstetric 
Anesthesia, University of California, San Francisco 
 
Co-investigators: 
• Paula Branco, PhD. Post-doctoral Fellow, Faculty of Computer Science, Dalhousie 
University 
• W. Scott Beattie, MD, PhD, FRCPC, Professor, Department of Anesthesia, University of 
Toronto; Clinical Director, Perioperative Interactive Education, and R. Fraser Elliott 
Chair of Cardiac Anesthesia, Toronto General Hospital, University Health Network 
• Stan Matwin, PhD, Professor and Director, Institute for Big Data Analytics, Faculty of 
Computer Science, Dalhousie University 
• Daniel I. McIsaac MD, MPH, FRCPC, Assistant Professor, Departments of 
Anesthesiology & Pain Medicine, The Ottawa Hospital and University of Ottawa 
• Luis Torgo, PhD, Professor, Faculty of Computer Science, Dalhousie University 
• Robin Urquhart, PhD, Assistant Professor and Ramia Scientist in the Department of 
Surgery, Department of Community Health, and Epidemiology and Division of Medical 
Education; Senior Scientist, Beatrice Hunter Cancer Research Institute; Affiliate 
Scientist, Nova Scotia Health Authority 
 
Research Administration: Jillian Banfield, PhD, Project Coordinator, Department of 
Anesthesia, Pain Management & Perioperative Medicine, Dalhousie University  
 
Innovian Database Management: George Campanis and Paul Brousseau, Department of 
Anesthesia, Pain Management & Perioperative Medicine, Dalhousie University  
 
Data Analyst Support: Lynn Lethbridge, Department of Surgery, Dalhousie University  

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Lay summary  
 
Introduction: The World Health Organization estimates that 270-360 million operations are 
performed every year worldwide. Death and complications after surgery are a big challenge. In 
Canada, out of every 1000 major surgeries, 16 patients die in hospital after surgery. In the United 
States, for every 1000 operations, 67 patients unexpectedly need life support in the Intensive 
Care Unit. With population aging and limited resources, strategies to improve health after 
surgery are ever more important.  
Vital signs, such as blood pressure and heart rate, show how the body is doing. Vital 
signs change during surgery because of patient, surgical, and anesthetic factors. 
Anesthesiologists can change vital signs with medications. However, we are only starting to 
understand which, and what ranges of, vital signs under anesthesia are associated with better 
health. Machine learning is a tool that can provide new ways to understand data. With better 
understanding, we can work to improve outcomes after surgery.  
 
Objective: We will analyze vital signs during surgeries for their links to death, complications 
(heart, lung, kidney, brain, infection), Intensive Care Unit admission, length of hospital stay, and 
hospital readmission. We will determine which, and what levels of, vital signs may be harmful. 
We predict that blood pressure, heart rate, oxygen level, carbon dioxide level, and the need for 
medications to change blood pressure will interact to be associated with death after surgery.  
 
Methods: After obtaining Research Ethics Board approval, we will analyze data from all 
patients who are at least 45 years old and had an operation (with the exception of heart surgery) 
with an overnight stay at the QEII health centre from January 1, 2013 to December 1, 2017. 
There are approximately eligible 35,000 patients. We will use machine learning to model the 
data and test how well our model explains outcomes after surgery.  
 
Significance: Our use of machine learning in a large, broad surgery population dataset could 
detect new relationships and strategies that may inform current practice, and generate ideas for 
future research. A better understanding of the impact of vital signs during surgeries may unveil 
methods to improve outcomes and resource allocation after surgery. The results may suggest 
ways to identify high-risk patients who should be monitored more closely after surgery. If our 
model performs well, it may motivate other researchers to use machine learning in health data 
research. The model we plan to develop will be based on information at the QEII, so it may be 
relevant to the care of Nova Scotians and beyond.  
 
 
 
 
 

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Protocol  
 
Introduction 
Surgical rates are increasing worldwide, with the World Health Organization estimating 270-360 
million operations globally in 2012 (1). Surgical mortality and morbidity remain a significant 
concern. Worldwide, more than 4.2 million patients die within 30 days of surgery every year, 
accounting for 7.7% of the global deaths (2). In Canada 2016-2017, for every 1000 major 
surgeries performed, 16 patients die in hospital after surgery (3). Unplanned ICU admission 
occurred after 6.7% of non-cardiac surgeries in a large American cohort study (4). During the 
intraoperative period, there may be severe hemodynamic (vital sign) derangements (e.g. 
abnormalities in blood pressure, heart rate, and oxygen level) that could play a significant role in 
postoperative mortality. However, due to the quantity and complexity of perioperative data, the 
threshold for harm of potentially modifiable intraoperative hemodynamic factors remain 
incompletely understood. Using machine learning techniques, this study will investigate the 
impact of multiple hemodynamic variables in the perioperative context. 
 
Background and significance 
Postoperative outcomes result from a complex interplay of patient, surgical, and anesthetic 
factors. Several validated preoperative risk stratification scores exist, including the Revised 
Cardiac Risk Index (RCRI), Portsmouth-Physiology and Operative Severity Score for the 
enUmeration of Mortality (POSSUM), Surgical Risk Scale, and National Surgical Quality 
Improvement Program (NSQIP) (5–7). However, the derivation process of these models did not 
include intraoperative hemodynamic variables and thus may not be responsive to the impact of 
intraoperative hemodynamic derangements.  
The Surgical Apgar Score (SAS) is a simple 10-point score involving intraoperative 
variables of estimated blood loss, blood pressure, and heart rate for composite mortality and 
morbidity. The SAS has varying predictive accuracy across surgical specialties (8,9) and its 
addition has not significantly improved preoperative risk prediction (10). Since the initial 
publication of the SAS in 2007 (11), there has been increasing interest in elucidating the role of 
intraoperative hemodynamics on mortality and morbidity, including hypotension (12,13), heart 
rate (14,15), anesthetic depth (16), end-tidal carbon dioxide (17) and oxygen saturation (18). A 
recent systematic review on hypotension found that due to heterogeneity and methodological 
limitations, one cannot conclude based on available evidence that intraoperative hypotension 
causes adverse outcomes (12). The impact of blood pressure perioperatively is physiologically 
complex, with factors including autoregulation, microvascular dysfunction, and limitations of 
measurement (19).  
Our understanding of the impact of intraoperative hemodynamics remains limited as they 
have been mostly evaluated in isolation. The interactions between intraoperative hemodynamic 
variables have not been systematically evaluated for association with postoperative outcomes. 
Hemodynamics are a reflection of physiology, surgical stress, and medications. If found to be 
predictive of outcomes, automatically recorded hemodynamic variables could provide robust, 
objective risk stratification within intraoperative anesthesia information systems.   
We would like to leverage machine learning to elucidate hemodynamic contributors in 
the perioperative surgical, physiologic and pharmacologic milieu in the non-cardiac surgery 
population. Machine learning refers to a set of advanced statistical techniques to evaluate the 
correlations and associations within large data sets (20). This includes logistic regression with 

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variable selection, where the most important predictors are chosen from a set of predictors to 
build a parsimonious model (21). Another machine learning technique is Principal Component 
Analysis (PCA), which finds groups of correlated predictors amongst a large set of potential 
predictors (i.e. dimensionality reduction) (21). The most important groups of predictors could 
then be placed in a model. Other modeling techniques we will use include classification trees, 
which predict outcomes based on input variables through classification, random forest to show 
which predictor has the highest importance, and association rule learning to help profiles of 
linked predictors (21).   
Foundational papers on machine learning of intraoperative data have reported 
inconsistent results. Prasad et al. analyzed data from 101 patients undergoing liver 
transplantation and showed that intraoperative hemodynamic data improved the prediction of 
mortality and acute renal failure compared to preoperative information alone (22). On the 
contrary, Lee et al. found that a deep neural network model predicted in-hospital mortality, 
though not better than conventional models such as the Risk Stratification Index (23). It is 
important for clinicians to understand the factors and processes by which the machine learning 
algorithm built the model, to help with clinical decision making and hypothesis generation for 
future research. Thus, we have chosen machine learning techniques that generate interpretable 
models. Our use of a large population database and advanced machine learning methods may 
help improve our understanding of the crucial relationships amongst hemodynamic variables and 
complex perioperative data.   
The results of this study may help synthesize complex intraoperative clinical information 
and unveil novel therapeutic strategies. Derived from data from the QEII Hospitals, the results 
would be directly applicable to the care of Nova Scotians. The models may lead to the creation 
of objective risk stratification scores calculated at the end of surgery, to identify high risk 
patients for increased postoperative follow-up and monitoring. Moreover, if models created 
using machine learning perform well, machine learning may be used more frequently to reveal 
the patterns within complex, large perioperative datasets. Hemodynamics are potentially 
modifiable risk factors. Future research includes prospective studies on whether targeted 
hemodynamic goals, increased postoperative follow-up of high-risk patients, and real-time 
machine learning precision medicine could improve outcomes after surgeries.  
Specific aims 
1. To use machine learning techniques to investigate systematically intraoperative 
hemodynamic predictors of postoperative 30-day all-cause mortality (primary outcome) 
and major in-hospital morbidity. Hemodynamic predictors to be studied are blood 
pressure, heart rate, oxygen saturation, end-tidal carbon dioxide, and medications to 
adjust blood pressure. Please see “Outcomes” section below for full list of secondary 
outcomes.  
2. To evaluate performance of machine learning models created and compare the 
performance of the best model to the Surgical Apgar Score.  
 
Hypothesis 
Controlling for other predictors, the durations of mean arterial pressure (MAP) <65mmHg, heart 
rate <60 or >100 beats per minute (BPM), hemodynamic medications use, oxygen saturation 
(SpO2) <88%, and end-tidal carbon dioxide (EtCO2) <30 or >45 will be associated with 
postoperative 30-day all-cause mortality (primary hypothesis) and 30-day major in-hospital 
morbidity (secondary hypothesis). Note that blood pressure, heart rate, vasopressors and 

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inotropic medications, oxygen saturation, and end-tidal are considered key indicators of 
hemodynamics. We have chosen conventional ranges commonly used in practice, and will 
perform sensitivity analysis of different definitions of each hemodynamic variable. 
In addition, the model for hemodynamic predictors of postoperative mortality developed 
from machine learning will perform better in terms of discrimination (C-statistics), calibration 
(Hosmer-Lemeshow test), and risk reclassification (Yates slope and integrated discrimination 
improvement) than the Surgical Apgar Score. 
 
 
 
 

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Methodology  
Study Design Overview 
This is a retrospective population cohort study. Following approval from the Research Ethics 
Board, we will analyze de-identified records of our study population. Since there are many 
machine modelling techniques each with its benefits and drawbacks, we will create models 
using interpretable machine learning techniques we believe will work the best for this study, 
and test the performances of these models through internal validation. The models will be 
compared to the Surgical Apgar Score in terms of discrimination, calibration, and risk 
reclassification. The trial will be registered on ClinicalTrials.gov prior to receiving research 
data and performing analysis. Transparent reporting of a multivariable prediction model for 
individual prognosis or diagnosis (TRIPOD) and Developing and Reporting Machine Learning 
Predictive Models (24) guidelines will be followed.  
 
Study Population 
We will include all patients ages ≥ 45 receiving their index (i.e. first) non-cardiac surgery with 
an overnight stay at the Nova Scotia Health Authority Queen Elizabeth II (QEII) hospitals 
(Victoria General and Halifax Infirmary) Halifax, Canada, from the past five years (i.e. January 
1, 2013 to December 1, 2017). For patients who had multiple surgeries, only the first non-cardiac 
surgery with an overnight stay at QEII will be included to avoid confounding from previous 
surgical admissions (i.e. one surgical admission per patient). We chose December 1, 2017, as the 
end date to allow for complete data 30 days after surgery: for data analysis in summer 2019, we 
only have access to mortality data up to December 31, 2017  
We will exclude patients with no intraoperative anesthetic records. Cardiac surgery 
patients (identified by procedure anatomy “Heart” or “Cardiac” within Innovian) are excluded 
since they have a unique set of considerations and complications due to having surgery directly 
in the heart. To include the full spectrum of non-cardiac surgical patients, no specific surgeries 
will be excluded except for deceased organ donation (“organ retrieval” in Innovian procedure 
name, and American Society of Anesthesiologists classification for neurologically-deceased 
organ donors, ASA=VI).  
Preliminary analysis of our intraoperative database estimates approximately 35,000 
patients in this cohort.  For patients with multiple procedures, the date of the first surgical 
procedure with an overnight stay will be used to identify the specific surgical admission of 
interest. 
 
Data Sources 
The de-identified dataset will consist of databases from Innovian, Horizon Surgical Manager 
(HSM), and Health Data Nova Scotia (HDNS). Innovian is the intraoperative anesthesia 
electronic information management system used at the QEII, containing automated recordings of 
intraoperative standard monitors, time-stamped anesthesiology entries of medications and 
interventions, and perioperative laboratory tests. HSM contains vital signs and administrative 
data at the anesthetic preoperative clinic, in the preoperative area prior to the surgery, and in the 
recovery room after surgery.  
The specific HDNS databases to which access is being requested are the Canadian 
Institute for Health Information (CIHI) Discharge Abstract Database (DAD) and Vital Statistics. 
DAD is a repository of clinical and administrative data from each hospitalization, including 
preoperative diagnoses, surgery performed, postoperative complications, and investigations. 

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DAD includes the 25 most relevant diagnoses preoperatively and postoperatively. Nova Scotia 
physician billings data will not provide any additional relevant information and are not necessary 
for this project. The Vital Statistics database records all deaths within Nova Scotia. Innovian and 
HSM data will be linked together by health card number and surgical date by the Innovian Data 
Manager (Innovian-HSM dataset). The health card numbers will be sent to Medavie for 
encryption. HDNS will link the Innovian-HSM dataset with the HDNS variables by encrypted 
study IDs and surgery date. National and provincial privacy and data security policies will be 
strictly followed. 
 
Data linkage:   
 
Step 1: Innovian 
a. Include patients with surgery dates 2013 Jan 1 – 2017 Dec 1 inclusive 
b. Include patients with age on date of surgery  45 
c. Exclude patients with procedure name that includes “organ retrieval” or ASA = VI, as 
well as procedure anatomy “heart” or “cardiac”.  
d. Eligible patients will be linked to HSM by health card number for the required variables 
described in the Appendix.  
 
Step 2: HDNS: CIHI DAD 
a. Health card numbers of patients identified in Step 1 will be sent to Medavie for 
encryption. Medavie will provide HDNS with encrypted health card numbers. HDNS 
obtains Study ID and Surgery date from the Innovian-HSM dataset to perform linkage via 
Study ID, encrypted health card number and procedure date (to ensure the data is from 
the same surgical admission). The maximum allowable mismatch for the surgery dates in 
Innovian and CIHI is +/- 1 day. The Innovian surgery date will be used as the gold 
standard, since the record is done in real time and thus the most accurate.  
b. Exclude patients with Length of Stay (LOS)  1. If a patient had multiple surgeries, the 
first surgery with a LOS >1 (i.e. at least overnight stay) will be included. 
c. HDNS provides Innovian-HSM the final list of included patients (patient ID and surgery 
date). Innovian-HSM will then send data for relevant variables to HDNS for linkage.   
 
Data quality: Data reabstraction studies performed by the CIHI show high data quality, ranging 
from 76 to 96% for coding consistency of interventions reported, significant diagnoses, and most 
responsible diagnosis (25). Since intraoperative hemodynamic variables and laboratory are 
automatically recorded into Innovian, no validation is necessary. It would not be possible to 
retrospectively validate the anesthesiology physician entries into Innovian (e.g. use of 
vasopressor medications) as most are not recorded elsewhere, but with medical-legal 
requirements and more than 10 years of experience with Innovian at QEII, the error rates will 
likely not significantly affect results.  
 
 
Variables 

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Please see Appendix for a detailed table of all variables.  
 
I. 
Main Predictors (“Exposures”) 
At the time of surgery, anesthesiologists document the timing of key clinic information 
(induction, intubation, start and end of surgery, emergence, extubation, and exit of the operating 
room), medications and fluids administered, estimated blood loss, and type of anesthesia into the 
intraoperative record. Hemodynamic factors are automatically recorded into the database every 
minute. The following intraoperative hemodynamic factors will be included in model building: 
 
Table 1. Exposures 
Blood pressure  
Since there is no universal definition of low blood pressure under 
anesthesia, we will use several different variables to determine the most 
significant exposure by threshold and duration. Both Systolic Blood 
Pressure (SBP) and Mean arterial pressure (MAP) have been used in 
blood pressure research (12). In a recent study (26), different blood 
pressure modeling techniques on the same dataset led to different 
conclusions on the impact of hypotension on postoperative myocardial 
infarction and kidney injury. Methods with the largest odds ratios were 
absolute maximum decrease in MAP and mean episode area under 
threshold. However, since the area under threshold is less interpretable 
and difficult to calculate in everyday clinical practice, the duration under 
threshold will be used.  
 
SBP 
1. Maximum change from preoperative SBP, in a) absolute change 
(mmHg), and b) relative change (%)(emergency and elective cases 
analyzed separately due to the lack of preoperative blood pressure 
in the emergency group) 
2. Cumulative duration (minutes) 20% below preoperative SBP 
3. Longest single episode (minutes) below a) 80, b) 90, and c)100 
mmHg 
4. Cumulative duration (minutes) below a) 80, b) 90, and c)100 
mmHg 
 
MAP 
1. Maximum change from preoperative MAP, in a) absolute change 
(mmHg), and b) relative change (%) (emergency and elective 
cases analyzed separately due to the lack of preoperative blood 
pressure in the emergency group. Note that since the HSM 
database only contains SBP and DBP, not MAP, MAP will be 
calculated using MAP = 1/3*SBP + 2/3*DBP) 
2. Cumulative duration (minutes) 20% below preoperative MAP 
3. Longest single episode (minutes) below a) 60, b) 65, c) 70, and d) 
80mmHg 
4. Cumulative duration (minutes) below a) 60, b) 65, c) 70, and d) 
80mmHg 

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Heart rate 
Since both fast and slow heart rate may be harmful and there is no 
universal definition of abnormal heart rate under anesthesia, we will 
model using a variety of methods. 
1. Maximum change (BPM) from preoperative heart rate (positive 
and negative) 
2. Relative change (%) from preoperative heart rate (positive and 
negative) 
3. Maximum pulse variation (maximum heart rate minus minimum 
heart rate)  
4. Longest single episode (minutes) a) below 60, and b) above 
100BPM 
5. Cumulative duration (minutes) a) below 60, and b) above 
100BPM  
Use of 
hemodynamic 
medications (i.e. 
special 
medications for 
blood pressure) 
1. Vasopressor/inotrope use (yes vs. no): phenylephrine, 
norepinephrine, epinephrine, vasopressin, dobutamine, or 
milrinone 
2. Infusion of any vasopressor/inotropes above (yes vs. no) 
(identified by unit of weight over time) 
3. Phenylephrine/ephedrine bolus (yes vs. no) (identified by unit of 
weight only)  
4. Vasodilator use (yes vs. no): labetalol, esmolol, nitroglycerin, 
nitroprusside    
5. Infusion of any vasodilator above (yes vs. no) (identified by unit 
of weight over time)  
Oxygen 
saturation by 
pulse oximetry: 
SpO2 
1. Longest single episode (minutes) below a) 88, and b) 90% 
2. Cumulative duration (minutes) below a) 88, and b) 90%  
 
End-tidal (i.e. 
exhaled) Carbon 
dioxide: EtCO2 
 
1. Longest single episode (minutes) a) below 30, and b) above 
45mmHg   
2. Cumulative duration (minutes) a) below 30, and b) above 
45mmHg  
 
 
 
 

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II. 
Outcomes  
Primary: 30-day all-cause postoperative mortality (binary) 
 
Secondary:  
1. Major 30-day in-hospital morbidity, both in terms of the individual category (yes/no), and 
an overall composite (i.e. yes for any of the 6 categories vs. no). We have included major 
morbidity outcomes where increased monitoring would be beneficial, using International 
Statistical Classification of Diseases (ICD) 10 codes and based on published protocol on 
patient safety indicators (27). 
1. Cardiac: composite of acute myocardial infarction, cardiac arrest, ventricular 
tachycardia, congestive heart failure, pulmonary edema, complete heart block, 
shock excluding septic shock 
2. Respiratory: composite of pneumonia, pulmonary embolism, acute respiratory 
failure, respiratory arrest, Mechanical Ventilation  96 hours 
3. Acute Kidney Injury  
4. Cerebrovascular: composite of strokes and transient ischemic attacks 
5. Delirium 
6. Septic Shock  
2. Postoperative ICU admission (yes/no) 
3. Prolonged Postoperative Length of Stay (LOS) (greater than vs. less than or equal to 
CIHI Expected Length of Stay (ELOS) as assigned by the Case Mix Grouping) [note that 
the LOS includes the day of surgery] 
4. Hospital readmission within 30 days (yes/no) 
5. Intraoperative mortality (yes/no)  
6. Days alive and out of hospital at 30 days postoperatively (28)  
 
 
III. 
Other predictors included into model   
The following potential perioperative predictors will be evaluated as covariates during model 
building. Statistically significant predictors will be retained in the model.  
 
 
 

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Table 2. Other predictors  
Preoperative 
1. Demographics: age on date of surgery, sex, obesity (body mass 
index>30) 
2. Indicators of preoperative comorbidities: since no comorbidity score 
has perfect performance, a variety of models will be evaluated and 
the most significant predictive score will be retained in the model.  
• American Society of Anesthesiologist class 
• RCRI score(29), and specific components via ICD codes (history 
of ischemic heart disease, congestive heart failure, 
cerebrovascular disease, diabetes, chronic kidney disease, and 
CCI and Case Mix Group codes of suprainguinal vascular, 
intraperitoneal, or intrathoracic surgery) using previously 
published methods (30)(31) 
• Elixhauser comorbidity index (32) and Charleson Comorbidity 
Index (33): calculated using diagnoses from the previous three 
years according to standard algorithm by HDNS. Reported as 
both total score and individual categories  
• Hospital Frailty Risk Score (34): based on ICD codes 
• Preoperative blood pressure: ICD diagnosis of hypertension and 
by measured blood pressure  
• Preoperative heart rate: Innovian and HSM   
• Chronic Obstructive Lung Disease (ICD code): since it may 
affect the interpretation of results of SpO2 and EtCO2 
      3.    Indicators of surgical complexity  
• CIHI DAD Case Mix Group (CMG) class (also contains 
categories that include medical complexity) 
• Main Surgical Service 
• Procedural Index for Mortality Risk (PIMR) (35): according to 
CCI 
• Procedure: CCI codes 
• Preoperative ICU admission  
• Emergency surgery: as determined by DAD Method of Entry = 
emergency department or transferred from another institution) 
  
 
Intraoperative  
• Anesthetic factors  
o Type of anesthesia: General vs. regional vs. neuraxial vs. 
sedation (multiple concurrent types possible)   
o Measures of anesthetic depth 
▪ Age-adjusted inspired Minimal Alveolar 
Concentration (MAC) – summed from all volatile 
anesthetics used (36). Since MAC is available for 
every 15 seconds, the average MAC for each case (i.e. 
time-weighted) will be used.    

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▪ Bi-spectral Index (BIS): duration in minutes for BIS 
<46 (37) 
o Temperature: duration (minutes) a) < 36C, and b) > 38C 
(38)  
o Crystalloid administration >1L: volume in mL 
• Surgical factors  
o Duration 
o Laparoscopy  
o Bleeding: Estimated blood loss (mL), lowest Hemoglobin day 
within 2 days after surgery (g/L) (including day of surgery), 
Red Blood Cell transfusion (mL) 
 
 
 
 

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Analysis plan 
Statistics will be analyzed directly on the HDNS Citadel secure server using SAS Software 9.4 
(SAS Institute, Inc., NC) and R version 3.5.3 (https://www.r-project.org/).  
 
Cohort characteristics 
Descriptive statistics will be performed on all proposed predictors and outcome variables listed 
above. Continuous variables will be listed as mean with standard deviation, and categorical 
variables as frequency counts and percentages. Mortality will also be described in terms of 
causes, location (in- vs. out-of-hospital), and timing (intraoperative vs. postoperative, and by 
postoperative day).  
 
Data processing and artifact removal 
New variables will be created through calculation or dichotomization, as described in the 
variables section above and Appendix 1. Patients with age >120 or  45 will be flagged for 
further investigation of data validity and potential removal. Artifact removal from hemodynamic 
variables will be performed by the Innovian Database Manager in SQL, prior to creation of 
exposure variables (Table 1) and data linkage. Input of hemodynamic variables of interest (blood 
pressure, heart rate, EtCO2, and SpO2) as well as BIS and end tidal volatile from intraoperative 
monitors were automatically recorded every 15 seconds into Innovian. Continuous values were 
captured every 15 seconds as medians of 8 values captured every 2 seconds, while discrete 
values (e.g. NIBP) were captured as is at the moment.  The maximum incidence of artifact was 
found to be 0.1% for heart rate, 0.4% for SpO2, 2.9% for noninvasive blood pressure, and 15% 
for invasive blood pressure in a previous study (39), and this is likely an overestimate due to the 
strict definition of artifacts. A systematic review and cohort study showed that while a variety of 
different artefact algorithms affected the defined incidence of intraoperative hypotension, it did 
not significantly affect the model between hypotension and outcome (40).  Importantly, duration-
based hypotension definitions were less affected by artefact filtering algorithms than depth 
thresholds (40).  
 
To reduce artifacts outside of anesthesia time (e.g. arterial line is plugged in prior to patient 
arrival), the variables below will only be taken during a defined “Surgery Duration”. This 
Duration starts at the first valid recorded SpO2  (that is subsequently valid for 1 minute), and ends 
after the last valid SpO2  (that has been valid for 1 minute).  
 
 
• Blood pressure 
1. Step 1, removal of individual artifacts: Note that non-invasive (NIBP) and invasive 
blood pressure values (IABP) will be treated separately. Potential artifacts will be 
removed (changed to missing data) in the following order: 
1. Missing any of SBP, DBP, or MAP, or where any of SBP, DBP, and MAP 
are equal to each other 
2. Apply 3 different artifact algorithms simultaneously to identify artifacts: 
• Multi-Centre Perioperative Outcomes Group (MPOG) algorithm  
• For IABP only: MAP value greater than 50% from the values 
before and after will be deleted, as previously described by Sun et 
al (41). 

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•  The following blood pressure artifact removal rules as described 
by Salmasi et al (41) will also be applied: “SBP less than or equal 
to DBP + 5 mmHg”, and “abrupt changes defined by SBP change 
greater than or equal to 80 mmHg within 1 min in either direction 
or abrupt SBP changes greater than or equal to 40 mmHg within 
2 min in both directions.” 
3. To remove artifacts prior to arterial line insertion, for IABP data only, the 
first MAP readings must be >40, and the first SBP reading must be >60.  
4. For each BP measurement, if any of SBP or MAP has been labelled as an 
artifact, then remove the entire measurement 
5. If an entire case has been identified as null after artifact removal (i.e. no 
valid BP for the entire case), the entire case and studyID will be deleted.  
2. Step 2. Concatenation. The final blood pressure will be taken from IABP or NIBP, 
whichever is available after artifact removal. If both non-invasive (NIBP) and invasive 
blood pressure values (IABP) are available for the same 15 second interval, the higher 
reading will be taken. Otherwise, the only blood pressure that is valid will be used.  
3. Clarification re. “Baseline” blood pressure: The HSM and first Innovian BP will be 
modeled separately during analysis, i.e. each patient will have two “baseline” 
measurements, coded by the variables “MAP_first_Innovian” and “MAP_preop_HSM”.  
1. All patients will have the variable of a first Innovian BP (after artifact 
clean up). The first Innovian BP is used since the second BP may be 
postinduction. This has the limitation that the the first intraoperative BP 
may not reflect the true baseline (42); however, practically this would be 
the blood pressure that would be available in similar clinical situations.  
2. Only some patients will have HSM BP (i.e. at preoperative clinic or in the 
preadmission area). Since the HSM does not contain MAP, MAP will be 
calculated using MAP = (2/3 DBP) + (1/3 SBP).  
• If a patient does not have a HSM BP, the variable value will be 
coded as missing (not zero).  
• Heart rate 
1. Artifact removal: 
1. The heart rate will be collected from the pulse oximeter instead of ECG, to 
reduce risk of electric cautery artifact and non-transmitted conduction.  
2. Heart rate > 170 or < 30 BPM will be removed. To reduce the influence 
from outlier artifacts (e.g. temporary disconnection or adjustment of a 
monitor), single episodes of deviation in any variable greater than 50% 
above or below the preceding value will be flagged. If the flagged heart 
rate from the pulse oximeter and ECG (and/or arterial line) differ by >10, 
the heart rate will be removed as artifact.  
2. Clarification about “Baseline” heart rate: similar to BP above, the HSM and first 
Innovian HR will be modeled separately during analysis, i.e. each patient will have two 
“baseline” HR. All patients will have the variable of a first Innovian HR (after artifact 
clean up). Only some patients will have HSM HR (i.e. preoperative clinic or in the 
preadmission area). If a patient does not have a HSM HR, the variable value will be 
coded as missing (not zero).  

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• EtCO2 will only be analyzed in cases involving general anesthesia. Values > 120 or < 5 
mmHg will be removed as artifact.  
• SpO2  > 100% or < 50% will be removed as artifact.  
• Temperature < 30 or > 43 degrees Celsius will be removed as artifact.  
• End tidal volatile concentrations (including nitrous oxide) will be converted to summed 
age-adjusted MAC (36). The total age-adjusted MAC < 0.2 and >2.5, at each 15 second 
interval, will be removed as artifact prior to calculation of the time-weighted average (15 
second time intervals).  
 
For each automatically recorded variable from Innovian, at least 3 cases will be reviewed to 
ensure correct artifact labeling. We will quantify the percentages of artifacts flagged and 
removed per case for each variable monitored. 
 
 
Artifact Removal Sub-study: Derivation of a machine learning algorithm for artifact 
removal for invasive blood pressure data  
Rationale 
While putting together the Innovian dataset for our study, we found that existing artifact (noise) 
removal algorithm has limitations at removing invasive arterial blood pressure (IABP) artifacts. 
Current algorithms are based on clinician-generated rules (40). We plan to use machine learning 
algorithms to improve artifact detection.  
Objective 
Our goal is to derive an artifact removal algorithm for IABP using machine learning of 
hemodynamic data.  
Methodology 
For this sub-study, we will include patients with IABP recorded. There are approximately 6000 
patients with IABP recordings. We will exclude patients who do not have any recording of the 
required hemodynamic variables: IABP systolic, IABP mean, IABP diastolic, non-invasive 
blood pressure (NIBP) systolic, NIBP mean, NIBP diastolic, IABP heart rate, ECG heart rate, 
SpO2 heart rate, and SpO2 (oxygen saturation). 
To label the dataset with “gold-standard” answers, we will use a random sample of 60 cases 
(chosen by random number generator) for which the artifacts (anomalies) will be manually 
labeled by anesthesiologists on the study team, blinded to the final artifact removal algorithm. 
The problem of artifact detection (anomaly detection) will be addressed for two target variables: 
IABP mean and IABP systolic. We will use the same methodology to conduct the analysis for 
artifacts detection in these two target variables. 

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For each target variable the problem will be tackled in two approaches: as a univariate time 
series and as a multivariate time series. In the univariate series approach, we will only take into 
consideration the data of the time series representing either the IABP mean or the IABP 
systolic artifacts. For the multivariate approach, we will include the information of the following 
variables IABP systolic, IABP mean, IABP diastolic, NIBP systolic, NIBP mean, NIBP diastolic, 
IABP heart rate, ECG heart rate, SpO2 heart rate, and SpO2 (oxygen saturation).  
In the univariate case, the anomaly detection will be carried out using several statistical 
approaches, and machine learning approaches. 
Methods to apply for anomaly detection in the univariate time series case: 
1) Statistical-based approaches: ARIMA model, simple, double and triple Exponential 
Smoothing and Outlier Detection using Prediction Confidence Interval (PCI). Anomalies will be 
detected by evaluating the deviation of the predicted point to the observed one. 
2) ML-based approaches: DBSCAN, LOF, isolation Forest, One-Class Support Vector Machine, 
XGBoost and neural networks. 
Regarding the anomaly detection in the multivariate time series case we will apply several 
machine learning-based techniques such as isolation forest, One-Class Support Vector Machine, 
XGBoost and different neural networks. 
Regarding the performance evaluation, we will split the data set into train/test (70%) and 
validation (30%). This means we will use 42 cases for train and test and 18 cases for 
validation. The performance evaluation in the train/test part of the data will be carried out using 
100 repetitions of bootstrapping method and we will assess the performance on following 
metrics: F1, precision, recall, specificity, Negative Predicted Value (NPV), False Discovery Rate 
(FDR), G-Mean, AUC-ROC, AUC-PR. 
The derived algorithm will be applied to the full sample (~6000 excluding the 60 cases used 
above), and the percentage of hypotension defined will be quantified according to published 
comparisons (1). This will also be compared to the results of the MPOG algorithm.  
 
Analysis will be performed in Python.  
 
Research Ethics Considerations  
 
This sub-study will use the existing Innovian dataset, but using only the following completely 
de-identified variables:  
 
1. Study ID 
2. Time stamps for each hemodynamic variable (dates will be removed, and placeholders will be 
used to denote surgery times that happened overnight that bridges two dates) 
3. Required timestamped hemodynamic variables for modelling: 
IABP systolic  

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IABP mean 
IABP diastolic 
NIBP systolic  
NIBP mean 
NIBP diastolic 
IABP heart rate  
ECG heart rate 
SpO2 heart rate 
SpO2 (oxygen saturation) 
 
Data analysis will be performed by Dr. Paula Branco (member of the current study team as a 
post-doc, currently faculty member at University of Ottawa) and her Co-op student Mengqi Wu 
will receive the de-identified, limited Innovian dataset securely and directly from our Innovian 
manager using the NSHA encrypted institutional email (SENDNS). The files will be stored in a 
password-protected computer and permanently deleted according to NSHA policy once analysis 
is complete (estimated to be end of September 2020). 
 
 
 
Missing data 
Since intraoperative hemodynamics are automatically recorded and mortality reporting is 
mandatory, we do not expect significant missing data for the key hemodynamic predictors and 
primary outcomes. For disease conditions, patients without ICD codes are assumed to not have 
the disease; the same applies for medications. For the other variables of interest, if there is less 
than 5% missing data, no processing will be performed. Between 5-20%, missing data will be 
imputed using group mean (43) for continuous variables. At greater than 20% missing data, the 
variable will be removed from analysis with potential causes evaluated and reported.  
 
Creating the models  
We will divide the cohort temporally (approximately 80:20 ratio) into two groups: 
derivation/training group (approx. January 1, 2013 – Dec 31, 2016), and validation group 
(approx. Jan 1, 2017 – December 1, 2017). This temporal approach to the model building vs. 
validation groups will also mirror the real-life application of machine learning, where data from 
the past is used to predict future outcomes. Splines will be used for the hemodynamic predictors 
involving time as a unit.  
In the derivation/training set, we will create models for the primary outcome using 
machine learning techniques, including logistic regression with variable selection, classification 
trees, and Principal Component Analysis (PCA)(21). We have chosen these techniques to obtain 
interpretable results, i.e. being able to understand the process by which the algorithm decision 
making occurred. Also, these techniques demonstrate different approaches and perspectives to 
better understand the relationships amongst the variables.  
Logistic regression with variable selection (e.g. LASSO, Elastic Net) identifies the most 
important predictors to create a parsimonious model. Classification trees predict outcomes 
based on input variables through classification. Random forest is an ensemble method of 
classification trees; while it is not directly interpretable, it can show which predictor has the 
highest importance. Association rule learning will be used to find new patterns. PCA is used for 

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dimensionality reduction and is particularly helpful in this study to better understand profiles of 
correlated variables, and to narrow down on what the most important predictors are when a 
multitude of predictors exist. PCA reveals components (i.e. groups of correlated predictors), 
with the most important components accounting for the most variation within the data. 
Components found to account for the most variances (Eigenvalue 1) in a scree plot represent 
the most important sets of predictors. They will be further analyzed using Cronbach Alpha to 
assess for internal consistency within components. Note that since PCA is a method of 
unsupervised learning where the outcome is not defined, in the first PCA model, all variables 
(including both primary and secondary outcomes) will be inputted into PCA with the significant 
components reported. This is to generate profiles of correlated predictors and outcomes. 
Structural equation modeling will be used. In an unrelated, second PCA model, PCA will be 
used to reduce dimensionality of predictor variables (i.e. exposures and covariates only), and 
the most correlated predictors within key components will be entered into a logistic regression 
the primary outcome.  
 
Class Imbalance 
Since patients with the primary outcome of mortality only accounts for an estimated 1.7% of the 
sample, significant class imbalance exists (i.e. not a 1:1 ratio between patients with vs. without 
mortality). This results in the machine learning to be more focused on the majority class (i.e. 
patients without mortality) and impacts the predictive power for mortality.  
Several techniques exist to make the learning algorithms to focus on the important class 
(44). Among these, pre-processing techniques are a powerful tool enabling the use of any 
standard learning algorithm by modifying the training set. The goal is to obtain a more balanced 
training set through weighting, undersampling and/or oversampling. We will explore different 
resampling techniques to rebalance the training set. Regarding undersampling, we will explore 
both random and informed undersampling (remove examples near the decision border through 
nearest neighbours computation and use a clustering algorithm to guide the selection of 
examples). For oversampling, we will explore the introduction of replicas (randomly or by 
weighting) and the generation of synthetic data through the introduction of Gaussian Noise, 
SMOTE, and other SMOTE-based techniques. We will also examine the combination of these 
techniques.  All of these techniques are only applied on the training set used to learn the model, 
and they are never applied on the test set. Thus, the model, although learned in a more balanced 
scenario is always evaluated only in real data and in a real imbalanced scenario. 
We will also apply cost-sensitive learning. The assignment of higher costs to type II 
errors during learning has the potential to improve the results, reducing this type of error. 
The effect of class rebalancing (at a variety of ratios and trough a variety of techniques) on 
model performance will be examined, and the impact of these adjustments on model 
performance will be reported. 
 
Evaluating model performance 
Using the derivation/testing set, the performances of machine learning models generated will be 
evaluated by cross validation. To account for potential temporal effects and concept drift, 
Monte Carlo and prequential evaluation will be used (45). In addition, each model will be 
evaluated in terms of discrimination, calibration, and risk reclassification (21,46). Discrimination 
will be calculated through C-statistics using area under curve (AUC) of receiver operating 
characteristic curve (ROC). Calibration will be represented graphically using observed vs. 

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expected event rates in risk estimate deciles, and by the Hosmer-Lemeshow statistic. Brier score 
will be computed for discrimination and calibration. Risk reclassification will be quantified 
through Yates slope and integrated discrimination improvement. R-squared will be reported.  
The performance of all models created will be evaluated in the validation set as part of 
internal validation using the same metrics of discrimination, calibration, and risk 
reclassification, and compared to the Surgical Apgar Score. Since there will likely be significant 
class imbalance and ROC curves may overestimate model performance, for each model, 
precision and recall curves (PRC), F score, average precision, and AUC will also be reported 
and compared. The best performing model will also be used to analyze for predictors for 
secondary outcomes. Sensitivity (recall, true positive rate), specificity, positive predictive value 
(precision), G-mean, negative predictive values, False Discovery Rates (FDR), optimism, and 
measures of association of hemodynamic thresholds for primary and secondary outcomes will be 
computed. Bayesian network causal inference analysis may be performed, to help increase 
interpretability. 
 
Sensitivity and subgroup analysis 
Sensitivity analysis will be performed for different definitions of hemodynamic 
derangements. Subgroup analysis will be performed based on age, sex, high preoperative risk 
(most significant indicator of preoperative comorbidity in Table 2, based on final model), RCRI, 
frailty (HFRS), preoperative hypertension, a select list of intermediate-high risk, gender-neutral 
elective surgery (please see Appendix – RCRI Protocol Table 1), emergency surgery, and type of 
anesthesia.   
 
Power calculation 
The 2016-2017 in-hospital mortality after major surgery in Nova Scotia is 1.7% (47). Using the 
Events Per Variable criterion (EPV) of at least 10 outcomes per predictor in the sample, the 1.7% 
mortality (408) out of an estimated sample size of 24,000 for the derivation/training group means 
that our model would be valid for up to 41 predictors for the primary outcome. However, the 
EPV may over or underestimate  the limitations of the EPV criterion (48), square root of the 
mean squared prediction error (rMPSE) and mean absolute prediction error (MAPE) will be 
calculated based on the number of predictors, sample size and events fraction (49) with the 
caveat that this model has not been externally validated . Full power calculation will be 
performed once we receive the final sample size. 
 
Strengths 
This study involves a large, recent population dataset with nearly complete follow up for the 
primary outcome. There has been more than 10 years of experience with intraoperative 
anesthetic electronic recording system, with a high likelihood of provider proficiency with 
anesthetic intraoperative electronic documentation. This dataset is unique in North America for 
its degree of intraoperative details, pairing of clinical and administrative data, and robust data 
quality. Our use of machine learning could detect new network relationships and strategies that 
may inform current practice as well as future research.  
 
Limitations 
This study involves two academic provincial adult non-obstetric tertiary care centres in one 
hospital system, which may represent a sicker population. Validation in other centres would 

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increase generalizability. The retrospective nature of this database study makes it susceptible to 
random error, bias, and confounding. There may be cluster effects from health providers and 
hospitals, however the large sample size allows for robust power and minimizes random error. 
There is a potential for misclassification, measurement, and ascertainment bias. Due to the lack 
of universal screening for morbidity (e.g. troponin biomarker for myocardial injury and 
infarction, and Brain-Natriuretic Peptide for preoperative cardiac risk stratification), only 
mortality will be included as primary outcome. The lack of postoperative hemodynamic data 
creates non-informative censoring (i.e. unbiased since the data is missing for all patients). 
Numerous other predictors have been adjusted for through multivariate logistic regression but 
unknown confounders and confounding by indication may remain. The retrospective data reflects 
a snapshot of evolving practice, though secular effects may remain limited over the five-year 
study period. Future prospective validation of the prediction model will increase external 
validity.  
 
Future directions 
Future research includes prospective multicentre validation of our findings. Based on this model, 
a real-time risk prediction tool could be incorporated into electronic anesthesia management 
systems, while a simple score could be developed for clinicians. In addition, randomized studies 
could shed light on whether targeted hemodynamics, increased postoperative follow-up based on 
risk stratification, and real-time machine learning precision medicine could improve mortality, 
morbidity, and patient-reported outcomes after surgeries. 
 
 
 

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Feasibility 
 
Budget overview 
 
Budget Item 
Amount 
Details 
A. Personnel/third party service 
providers 
$800 
Mentorship from data analyst Lynn 
Lethbridge.   
B. Equipment 
$0 
SAS and R software will be provided by 
HDNS.   
C. Materials, supplies and 
administrative services 
$5895 
 
 
 
 
$75 
Health Data Nova Scotia data request and 
linkage with Innovian and HSM.  
 
Medavie patient ID encryption (required by 
HDNS) 
D. Knowledge translation and 
dissemination  
$1600 
Poster presentation at a conference 
Total 
$8370 
 
 
 
Additional budget details 
Budget Item A: Lynn Lethbridge is a data analyst within the Department of Surgery with 
extensive experience with HDNS. She has been contracted to mentor Dr Ke’s data analysis for 
16 hours at $50/hour ($800).  
 
Budget Item C: We will request Discharge Abstracts Database and Vital Statistics data from 
Health Data Nova Scotia (HDNS) (please see confirmation of feasibility and quote attached). 
HDNS will link this dataset with our institutional Innovian (intraoperative anesthesia electronic 
record) by MSI and provide us with a complete de-identified dataset. No server purchase is 
necessary since the HDNS data will be provided on Citadel. Details from HDNS regarding 
Citadel: "Analyses will be carried out remotely on the HDNS Windows server "Citadel". 
Connection to Citadel occurs through Remote Desktop Connection (RDC). Citadel only accepts 
connections from approved DAL NetIDs to access specific project data housed in our Hadoop 
based research cluster. Other external connections such as the internet or USB devices are 
disabled, and no data may be transferred between the local PC and the remote session."     
 
Budget Item D: Presentation at a conference: Poster printing $200, flights and accommodation 
$1000, registration fee $400.  
 
Funding: $5000 Nova Scotia Health Authority Research Fund (awarded), the rest to be covered 
by the Dalhousie Department of Anesthesia  
 
 
 
 

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Benefits  
This interdisciplinary study brings together investigators from diverse disciplines and locations, 
with expertise from Dalhousie anesthesiology, epidemiology, and Big Data Institute, as well as 
Harvard and University of Toronto. The research project is locally-based and relevant, with 
findings that may spark further research and inform anesthesiology perioperative practice.  
The QEII hospitals are tertiary referral centres taking care of some of the sickest patients 
of the province. Better understanding of intraoperative predictors may unveil strategies to 
improve outcome and improve resource allocation after surgery. Moreover, this model may be 
generalizable to other hospitals, placing the Nova Scotia Health Authority and Dalhousie 
University as a leading innovator in this area, as large population-level outcomes research will 
likely continue to be at the forefront of health research.  
 
 
Research team roles and responsibilities 
Dr. Ke is completing the Harvard T.H. Chan School of Public Health Summer-Only Master of 
Science in Epidemiology. This consists of coursework over three summers (2018-2020) and 
online. The courses focus on epidemiology and advanced statistics, and with mentorship from 
Harvard and Dalhousie faculty Dr. Ke has been learning the theoretical knowledge and practical 
coding skills to independently analyze this project. She has protected research time to complete 
this project.  
  
This proposed thesis project is co-supervised by Dalhousie and Harvard faculty. Dr. E. 
Francis Cook at the Harvard School of Public Health for his expertise in epidemiology, risk 
prediction, and data mining. Dr. George has extensive research experience in anesthesiology and 
has been building capacity in Big Data anesthesiology research at Dalhousie. Dr. David 
MacDonald at Dalhousie anesthesiology has expertise in perioperative medicine and will bring 
clinical and research knowledge. We also draw on the guidance of Dr. William Scott Beattie 
(anesthesiology professor at University of Toronto) who has published extensively in database 
studies involving perioperative outcomes, Dr. Robin Urquhart (Community Health & 
Epidemiology) for her experiences with Health Data Nova Scotia (HDNS) and epidemiology, 
Dr. Stan Matwin (Director at the Dalhousie Big Data Institute) for his expertise in machine 
learning and Big Data, Dr. Paula Branco (Post-doctoral fellow at Dalhousie Computer Science) 
for her specialty in machine learning and class imbalance, Dr. Luis Torgo (Professor at 
Dalhousie Computer Science) for his expertise in analytics of spacio-temporal data, Dr. Dan 
McIssac from Ottawa for his specialty in perioperative database research, George Campanis and 
Paul Brousseau for their experience with Innovian and medical informatics, and Lynn Lethbridge 
(Department of Surgery) for HDNS data analysis mentorship.  
Knowledge translation and dissemination 
The models derived from this project will undergo further prospective internal and external 
validation prior to being incorporated into clinical practice. Based on an externally validated 
model, a real-time risk prediction tool could be incorporated into electronic anesthesia 
management systems, while a simple score could be developed for clinicians. This project will 
be presented locally at the Dalhousie Anesthesia Research Day, as well as at a national or 
international anesthesiology conference. In addition, the methodology of optimization of class 
imbalance will be published in a machine learning journal, and the overall project will be 
published in an anesthesiology journal. Once published, a Visual Abstract and a patient-focused 
infographic of results will be disseminated on social media.  

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Ethical considerations 
Use of Personal Health Information (PHI)  
This is a minimal-risk study. De-identified patient data meeting the inclusion and exclusion 
criteria will be obtained from Innovian and Health Data Nova Scotia. This project satisfies all of 
the requirements for waiver of consent under the Tri-Council Policy Statement (TCPS 2) and 
Nova Scotia Personal Health Information Act (please see Request for Waiver of Consent 
appendix). The population size is estimated to be 35,000, which would be impracticable since 
this is an incredibly large population to contact for consent. Also, the cohort of interest includes 
patients aged 45 and older who have undergone surgery, and it is therefore expected that some 
patients would have died. Obtaining consent for these individuals would not only result in the re-
identification of these patients, but also their next-of-kin who would give consent on their behalf. 
We have submitted a request for waiver of consent to the REB.  
Measures will be in place to minimize the risk of breach in privacy and confidentiality (please 
see below). Due to the large sample size and the collection of common perioperative outcomes, 
accidental identification of a specific person from de-identified data is unlikely. There is no other 
adverse event, harm, or risk anticipated. 
Health Data Nova Scotia will create a database of linked data using the Provincial Health Card 
number and date of surgery. The database we receive will be de-identified. PHI collected include 
the following and will be used in the most de-identified form: 
1. Date of surgery (for patients with multiple surgeries, information from only the first 
surgery will be collected): in order to divide the cohort into derivation, training, and 
validation groups by date of surgery 
2. Age on date of surgery, Sex: potential covariates in the model  
3. Procedures (Current Procedural Terminology (CPT) codes: a potential covariate in the 
model   
 
 
Measures Taken to Protect Personal Health Information and Study Data 
• Cohort creation: The linked dataset will be created by HDNS through experienced 
personnel and established process, and follow Tri-Council guidelines. The dataset we 
receive for analysis will not contain any direct identifiers such as health card number 
and date of birth.  
• Controlled access: Only authorized researchers in Nova Scotia will have access to 
de-identified person-level data. Aggregate data will be used as early as possible. 
While Dr. Ke is in Boston, if needed, Health Data Nova Scotia will perform data 
analysis on patient-level data using SAS protocol from team and send the rest of the 
team aggregate data for interpretation. The rest of the team will only have access to 
aggregate results after statistical analysis, with no identifiable information. All de-
identified person-level electronic data will be securely stored in the HDNS secure 
server “Citadel”, where analysis will be performed without needing to download data 
from the secure server.  

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o Details from HDNS regarding Citadel: "Analyses will be carried out remotely 
on the HDNS Windows server "Citadel". Connection to Citadel occurs 
through Remote Desktop Connection (RDC). Citadel only accepts 
connections from approved DAL NetIDs to access specific project data 
housed in our Hadoop based research cluster. Other external connections such 
as the internet or USB devices are disabled, and no data may be transferred 
between the local PC and the remote session."  
• Removal of all personal identifiers in electronic data: This will be performed by 
the HDNS prior to the release of the analytic dataset to the project team. 
• The results of the study (prototypes, publications, posters, presentations) will not 
contain any personal data: Only aggregate data of cell counts greater than 5 will be 
reported. As per HDNS policy, manuscripts will be submitted to HDNS for approval 
prior to publication to ensure privacy and confidentiality.  
Storage and Retention of Personal Health Information and Study Data 
De-identified electronic person-level data will be securely stored in Citadel, the HDNS server. 
(please see above). Paper material will be stored in a locked cabinet in the Halifax Infirmary 
Anesthesiology Research Office. Regulatory documents (no-PHI) will be stored in the secure 
(locked; access limited) anesthesia research office. Electronic records will be kept in password 
protected files on a password protected computer on the NSHealth network. 
After study completion, according to HDNS protocol, researchers will no longer have 
access to Citadel but the data will be kept within HDNS for a minimum of seven years. Paper 
records will be securely shredded in accordance to NSHA policy. All electronic files will be 
permanently erased by the  according to NSHA IT policy at the end of the retention period (7 
years). 
 
How do benefits outweigh harm 
 
The results of this study may help synthesize complex intraoperative clinical information and 
unveil novel therapeutic strategies. Derived from data from the QEII Hospitals, the results would 
be directly applicable to the care of Nova Scotians. The QEII hospitals are tertiary referral 
centres taking care of some of the sickest patients of the province. Better understanding of 
intraoperative predictors may unveil strategies to improve outcome and improve resource 
allocation after surgery.  
The models may lead to the creation of objective risk stratification scores calculated at the end of 
surgery, to identify high risk patients for increased postoperative follow-up and monitoring. 
Moreover, if models created using machine learning perform well, machine learning may be 
used more frequently to reveal the patterns within complex, large population perioperative 
datasets. Hemodynamics are potentially modifiable risk factors. Future research includes 
prospective studies on whether targeted hemodynamic goals, increased postoperative follow-up 
of high-risk patients, and real-time machine learning precision medicine could improve outcomes 
after surgeries.  
With any database analysis project, a potential harm would be a breach in confidentiality and 
privacy. With robust measures detailed above to protect privacy and confidentiality strictly 

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followed, the risk of breaches is minimal. The many benefits of this study outweigh the unlikely 
occurrence of potential harms.  

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Anesthesiol. 2015 Oct 1;123(4):775–85.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Appendix 1. Summary of Variables 
 
 

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33
Source 
Dataset 
 
Variable 
 
Level of 
Identific
ation 
Time Span 
Why is this element required in the 
analysis? 
 
Information for inclusion and data linkage 
Innovian  Date of 
surgery 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Part of HDNS data linkage with 
Innovian to ensure only variables from 
the surgical admission of interest (first 
surgery admission with LOS>1) is 
included. This Date of Surgery will be 
used to confirm linkage with 
“PDATE” from CIHI DAD to ensure 
that data from the correct surgical 
admission is included The maximum 
allowable mismatch for the surgery 
dates in Innovian and CIHI is +/- 1 
day. The Innovian surgery date will be 
used as the gold standard, since the 
record is done in real time and thus the 
most accurate.  
  
The date of surgery will also be used 
to divide the cohort temporally into 
the derivation/training group (January 
1, 2013 – November 30, 2016), and 
validation group (December 1, 2016 – 
December 1, 2017)  
Innovian 
Patient_ID: 
encrypted 
health card 
number 
 
Patient 
Jan 1, 2013 
to Dec 1, 
2017 
HDNS data linkage with Innovian. 
The dataset given to us will not 
contain Patient_ID, but rather a Study 
ID.  
Innovian  Age: on date 
of surgery  
Patient 
Jan 1, 2013 
to Dec 1, 
2017 
Age ≥45 part of inclusion criteria. 
Also, as age may impact outcomes, 
this will be included as a covariate in 
our models 
Innovian  Organ 
donation: 
Procedure 
name 
Patient 
Jan 1, 2013 
to Dec 1, 
2017 
Patients with procedure name that 
includes “organ donor” or “organ 
donation” will be excluded, since they 
are already declared brain dead and 

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34
containing 
the word 
“Organ 
donor” or 
“Organ 
donation” or  
“DCD”  
[Derived by 
Innovian]  
will not reflect the usual surgical 
population.  
Innovian  Cardiac 
surgery 
[Derived by 
Innovian]  
 
Patient 
Jan 1, 2013 
to Dec 1, 
2017 
As defined by ProcedureAnatomy = 
“heart” or “cardiac”. Cardiac surgery 
patients are excluded since they are 
receiving surgery directly on the heart 
and have a unique set of complications 
that may not reflect the usual surgical 
population. 
Innovian   ASA: 
American 
Society of 
Anesthesiolo
gist class (I to 
VI) 
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Patients with ASA VI (i.e. patient with 
neurologic brain death for organ 
donation) will be excluded. Also, as 
this may affect outcomes, it will be 
included as a covariate in our models.  
CIHI 
DAD 
PATIENT_I
D: encrypted 
health card 
number 
For HDNS 
internal use 
Patient 
Jan 1, 2013 
to Dec 1, 
2017 
HDNS data linkage with Innovian-
HSM. The dataset given to us will not 
contain Patient_ID, but rather a Study 
ID. 
Vital 
Statistics 
PATIENT_I
D: encrypted 
health card 
number 
For HDNS 
internal use 
Patient 
Jan 1, 2013 
to Dec 1, 
2017 
HDNS data linkage with Vital 
Statistics. The dataset given to us will 
not contain Patient_ID, but rather a 
Study ID. 
CIHI 
DAD  
PDATE[1-
n]: procedure 
date 
 
 
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
HDNS data linkage with Innovian 
surgery date, to ensure that only the 
admission corresponding to the 
surgery admission of interest (first 
surgery admission with LOS>1) is 
included. 

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35
 
The PDATE matching the date of 
procedure (+/- 1 day) from Innovian 
will be used. The corresponding 
number (e.g. PDATE1) will be used 
for all procedure-related codes (e.g. 
PCODE1, PDSERV1). 
 
Note that except for preoperative 
comorbidity data, where we would 
need data for 3 years prior to PDATE, 
for all other variables we only need 
data within 30 days after PDATE.  
CIHI 
DAD 
LOS: Length 
of Stay (after 
surgery) 
 
Derived by 
HDNS: 
discharge 
date minus 
PDATE + 1 
(includes day 
of surgery) 
 
  
Patient 
Jan 1, 2013 
to Dec 1, 
2018 (to 
account for 
prolonged 
LOS postop 
from the end 
of inclusion 
date) 
Part of inclusion criteria: if a patient 
had multiple surgeries, the first 
surgery with a LOS >1 (i.e. at least 
overnight stay). To calculate ratio of 
LOS to Expected Length of Stay 
(ELOS) as secondary outcome.  
We have chosen to standardize LOS to 
only include duration of stay after 
surgery, since there are many factors 
that may prolong a patient’s stay prior 
to surgery.  
 
Additional Demographic Information 
CIHI 
DAD 
SEX:   
Patient sex  
 
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
As sex may affect outcomes, this will 
be included as a covariate in our 
models 
M = Male  
F = Female 
 
HSM  
BMI: Body 
Mass Index  
[Derived by 
Innovian] 
 
Patient  
Jan 1, 2010 
to Dec 1, 
2017 
Dichotomized according to WHO 
definition for obesity: BMI>30. This 
will be combined with ICD code to 
create a binary Obesity variable 
(please see below).  

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CIHI 
DAD  
Obesity  
[Derived by 
HDNS]  
  
Patient  
Jan 1, 2010 
to Dec 1, 
2017 
Binary variable (yes/no), “yes” 
defined by: BMI >30, and if BMI 
missing, obesity ICD code 
(DXCODE[1-n] = E66). As BMI may 
affect outcomes, this will be included 
as a covariate in our models. The BMI 
data from HSM-Innovian will take 
precedent over DXCODE: i.e. first 
identify patients BMI>30 (coded as 
obesity = yes), BMI <=30, and 
BMI=missing data. For patients with 
BMI=missing, patients with ICD E66 
will also be coded as obesity = yes.  
 
Additional Measures of Perioperative Comorbidity 
CIHI 
DAD 
DXCODE 
[1-n]: 
diagnosis 
code  
 
Patient 
Jan 1, 2010 
to Dec 31, 
2017 (3 years 
before and 
including 30 
days after 
date of 
surgery) 
Diagnosis codes are required for the 
following purposes: 
• HDNS will use DXCODE 
(with “DXTYPE[1-n] = 1”, 
OR “DXTYPRE[1-n] = 2 
AND DXPRE[1-n] = 5”, OR 
“DXTYPE[1-n] = 3 AND 
DXPRE[1-n] = 5”, OR 
“DXTYPE[1-n] = M AND 
DXPRE[1-n] = 5”) within 
three years before date of 
surgery to calculate the 
Elixhauser comorbidity 
index, Charleson 
Comorbidity Index, RCRI, 
and Hospital Frailty Risk 
Score (see below). The 
entirety of DXCODES do 
not need to be disclosed to 
the research team since 
HDNS will perform the 
calculations.  
• Specific comorbid conditions 
will be included as covariates 
in the analysis  
• To examine post-operative 
comorbidity 

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37
 
Comorbidity covariates:  
We need the following DXCODE 
(with “DXTYPE[1-n] = 1”, OR 
“DXTYPRE[1-n] = 2 AND DXPRE[1-
n] = 5”, OR “DXTYPE[1-n] = 3 AND 
DXPRE[1-n] = 5”, OR “DXTYPE[1-
n] = M AND DXPRE[1-n] = 5”) 
within 3 years before PDATE: 
Disease categories for RCRI (please 
see “RCRI”); hypertension (I10-I15), 
COPD (I27.8, I27.9, J40.x–J47.x, 
J60.x–J67.x, J68.4, J70.1, J70.3), 
Obesity (E66) 
Postoperative outcomes:  
We need the following DXCODE 
(with “DXTYPE [1-n]=2” Excluding 
“DXTYPE [1-n]=2 AND DXPRE [1-
n]=5”, OR “DXTYPE [1-n]=3” 
excluding “DXTYPE [1-n]=3 AND 
DXPRE [1-n]=5”,  OR “DXTYPE[1-
n] = M AND DXPRE[1-n] = 6”) to 
include into model as individual, 
categorical, and composite secondary 
outcome: 
 
Cardiac: acute myocardial infarction 
(I21 to I22, I24), cardiac arrest (I46.0, 
146.1, I46.9), ventricular tachycardia 
(I47.2), shock (R57, T81.1) excluding 
septic shock (R57.2), congestive heart 
failure (I50.0, I50.1, I50.9), pulmonary 
edema (J81), complete heart block 
(I44.2) 
Respiratory: pneumonia (J13 to J18, 
J69.0, J69.8, J95.4), pulmonary 
embolism (I26), acute respiratory 
failure (J95.1, J95.2, J96.0), 
respiratory arrest (R9.2)  

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Cerebrovascular: strokes and 
transient ischemic attacks (I60 to I69, 
G45) 
Delirium (F5)  
Acute kidney injury (N17)  
Septic shock (R57.2)  
 
CIHI 
DAD 
DXPRE [1-
n]: 
diagnosis 
prefix 
Patient  
Jan 1, 2010 
to Dec 31, 
2017 (3 years 
before and 
including 30 
days after 
date of 
surgery) 
To categorize the type of the 
corresponding DXCODE (please see 
above):  
 
C = Cause of death 
Q = Query Diagnosis/Etiology 
5 = Comorbidity arose before 
qualifying intervention 
6 = Comorbidity arose during or after 
qualifying intervention  
8 = Palliative Care  
CIHI 
DAD 
DXTYPE 
[1-n]: 
diagnosis 
type 
Patient  
Jan 1, 2010 
to Dec 31, 
2017 (3 years 
before and 
including 30 
days after 
date of 
surgery) 
To categorize the type of the 
corresponding DXCODE (please see 
above):  
 
M =  Most Responsible Diagnosis 
1 = Pre-admit comorbidity 
2 = Post-admit comorbidity 
3 = Secondary Diagnosis 
CIHI-
DAD 
Admit-date 
 
Patient 
1Jan2010 to 
31Dec2017 
 
For 3-year look-back window with: 
either admission date or discharge date 
is between surgery date (B) and 3 
years before B (A), assuming that 
A<B. 
 
For 30-day follow-up window with: 
Either admission date or discharge 
date is between surgery date (B) and 
30 days after B (C), assuming that 
B<C. 
 
CIHI-
DAD 
Discharge_da
te 
 
Patient 
1Jan2010 to 
31Dec2017 
For 3-year look-back window with: 
either admission date or discharge date 
is between surgery date (B) and 3 
years before B (A), assuming that 
A<B. 

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39
 
For 30-day follow-up window with: 
Either admission date or discharge 
date is between surgery date (B) and 
30 days after B (C), assuming that 
B<C. 
CIHI 
DAD 
ECI: 
Elixhauser 
comorbidity 
index  
 
[Derived by 
HDNS] 
 
Patient  
Jan 1, 2010 
to Dec 1, 
2017 (3 years 
before and 
including 
date of 
surgery)  
To be computed by the HDNS 
according to protocol (32) using 
DXCODES (please see above), to be 
included into model as covariate. 
Please report both total score, and 
any of the following individual 
categories: 
 
Congestive heart failure 
Cardiac arrhythmias 
Valvular disease 
Pulmonary circulation disorders 
Peripheral vascular disorders 
Hypertension, uncomplicated 
Hypertension, complicated 
Paralysis 
Other neurological disorders 
Chronic pulmonary disease 
Diabetes, uncomplicated 
Diabetes, complicated 
Hypothyroidism  
Renal failure 
Liver disease 
Peptic Ulcer Disease Excluding 
Bleeding 
AIDS/HIV 
Lymphoma 
Metastatic cancer 
Solid tumor without metastasis 
Rheumatoid arthritis/ collagen 
vascular diseases 
Coagulopathy 
Obesity  
Weight loss 
Fluid and electrolyte disorders 
Blood loss anemia  
Deficiency anemia  
Alcohol abuse 
Drug abuse  
Psychoses  

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Depression 
 
CIHI 
DAD 
CCI: 
Charleson 
Comorbidity 
Index 
 
[Derived by 
HDNS] 
 
Patient 
Jan 1, 2010 
to Dec 31, 
2017 (3 years 
before and 
including 30 
days after 
date of 
surgery) 
To be computed by the HDNS 
according to protocol (33) using 
DXCODES (please see above), to be 
included into model as covariate. 
 
Please report both total score, and 
any of the following individual 
categories: 
 
Myocardial infarction  
Congestive heart failure 
Peripheral vascular disease 
Cerebrovascular disease  
Dementia 
Chronic pulmonary disease  
Rheumatic disease 
Mild liver disease 
Moderate or severe liver disease 
Diabetes without chronic 
complication 
Diabetes with chronic complication 
Hemiplegia or paraplegia  
Renal disease 
Any malignancy, including 
lymphoma and leukemia, except 
malignant neoplasm of skin 
Metastatic solid tumor  
Peptic Ulcer Disease Excluding 
Bleeding 
AIDS/HIV 
CIHI 
DAD  
RCRI: 
Revised 
Cardiac Risk 
Index   
 
[Derived by 
HDNS] 
 
 
Patient  
Jan 1, 2010 
to Dec 1, 
2017 (3 years 
before and 
including 
date of 
surgery)  
As RCRI has been shown to affect 
outcomes (29), it will be included as a 
covariate. This is based on DXCODE 
ICD codes (identified above) of 
history of ischemic heart disease, 
congestive heart failure, 
cerebrovascular disease, diabetes on 
insulin, chronic kidney disease 
creatinine >176.8 µmol/L, and a list of 
Canadian Classification of Health 
Interventions (CCI codes) of 

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41
suprainguinal vascular, intraperitoneal, 
or intrathoracic surgery (30).  
 
Detailed protocol will be sent to the 
HDNS. Please provide both the names 
of the categories and the total score 
(i.e. for each patient, positive for any 
of the categories of cerebrovascular 
disease, ischemic heart disease, 
congestive heart failure, chronic 
kidney disease, diabetes, and diabetes 
on insulin; as well as the total RCRI 
score (1 point for each positive 
category). 
CIHI 
DAD 
High risk 
surgery as 
per RCRI: 
Suprainguina
l vascular, 
intraperitonea
l, or 
intrathoracic 
surgery 
 
[Flag - 
Derived by 
HDNS] 
Patient  
3 years 
before and 
including 
date of 
surgery (i.e. 
Jan 1, 2010 
to Dec 1, 
2017) 
Binary variable (yes/no)  
As this may affect outcomes, it will be 
included as a covariate by itself and as 
part of the Revised Cardiac Risk Index 
(RCRI) score calculation.  
HSM  
Preoperative 
insulin use 
(yes/no)  
 
[Derived by 
Innovian: yes 
= any insulin 
use in the 
preoperative 
medication 
list] 
Patient 
3 years 
before and 
including 
date of 
surgery (i.e. 
Jan 1, 2010 
to Dec 1, 
2017) 
Due to the difficulty of obtaining this 
data from ICD codes (the code for 
this, Z79.4, only came to existence in 
2018), this will help identify patients 
on insulin for the RCRI score.  
Innovian 
(from 
linked 
lab 
database)  
Preoperative 
Creatinine 
(µmol/L) 
Patient 
3 years 
before and 
including 
date of 
surgery (i.e. 
This will help identify patients who 
satisfy the chronic kidney disease 
category (Pre-operative 
creatinine >176.8 µmol/L) to calculate 
the RCRI score.  

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42
Jan 1, 2010 
to Dec 1, 
2017) 
 
 
CIHI 
DAD 
HFRS: 
Hospital 
Frailty Risk 
Score  
 
[Derived by 
HDNS] 
Patient  
3 years 
before and 
including 
date of 
surgery (i.e. 
Jan 1, 2010 
to Dec 1, 
2017) 
As the HFRS has been shown to affect 
outcomes, it will be included as a 
covariate. HDNS will calculate using 
DXCODE according to a published 
protocol (34).  
Detailed protocol will be sent to the 
HDNS. 
Innovian 
and 
HSM  
Preoperative 
mean arterial 
pressure 
(MAP)  
 
[Derived by 
Innovian] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
As preoperative high blood pressure 
may affect the interpretation of the 
threshold of harm for intraoperative 
blood pressure (12), preoperative 
MAP will be used in the calculation of 
change in MAP (absolute and %). 
 
HSM contains blood pressure of 
elective surgery patients at the 
anesthetic clinic and prior to entering 
the operating room, while Innovian 
contains the first blood pressure of all 
patients in the operative room. The 
lowest blood pressure from either 
Innovian or HSM will be used.  
HSM   
Preoperative 
systolic blood 
pressure 
(SBP)  
 
[Derived by 
Innovian] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
As preoperative high blood pressure 
may affect the interpretation of the 
threshold of harm for intraoperative 
blood pressure (12), preoperative SBP 
will be used in the calculation of 
change in SBP (absolute and %). 
 
HSM contains blood pressure of 
elective surgery patients at the 
anesthetic clinic and prior to entering 
the operating room. The first Innovian 
blood pressure will be used for 
emergency surgery patients, since 
HSM pressure will not be available.  

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Innovian  First systolic 
blood 
pressure 
(SBP)  
 
[Derived by 
Innovian] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Emergency and elective cases will be 
analyzed separately for the variables 
containing relative SBP changes, 
recognizing the lack of preoperative 
blood pressure in the emergency 
group. For patients with BP recorded 
on HSM (i.e. preoperative clinic or in 
the preadmission area), the lowest of 
the HSM or first Innovian BP will be 
used as baseline. For emergency 
surgery patients without previously 
recorded blood pressure, the first 
Innovian BP will be used (since the 
second BP may be postinduction), 
with the limitation that this may not 
reflect the true baseline (42); however, 
practically this would be the blood 
pressure that would be available in 
similar clinical situations. 
HSM   
Preoperative 
mean arterial 
pressure 
(MAP)  
 
[Derived by 
Innovian] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Since the HSM database only contains 
SBP and DBP, not MAP, MAP will be 
calculated using MAP = 1/3*SBP + 
2/3*DBP 
Innovian  First mean 
arterial 
pressure 
(MAP)  
 
[Derived by 
Innovian] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Emergency and elective cases will be 
analyzed separately for the variables 
containing relative MAP changes, 
recognizing the lack of preoperative 
blood pressure in the emergency 
group. For patients with BP recorded 
on HSM (i.e. preoperative clinic or in 
the preadmission area), the lowest of 
the HSM or first Innovian BP will be 
used as baseline. For emergency 
surgery patients without previously 
recorded blood pressure, the first 
Innovian BP will be used (since the 
second BP may be postinduction), 
with the limitation that this may not 

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44
reflect the true baseline (42); however, 
practically this would be the blood 
pressure that would be available in 
similar clinical situations. 
HSM 
Preoperative 
heart rate 
 
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
As this may affect the level of harm 
from the intraoperative heart rate, it 
will be used in the calculation of 
change in heart rate (absolute and %) 
 
HSM contains heart rate of elective 
surgery patients at the anesthetic clinic 
and prior to entering the operating 
room, while Innovian contains the 
initial heart rate of all patients in the 
operative room. The lowest heart rate 
from either Innovian or HSM will be 
used. 
Innovian  Preoperative 
heart rate 
 
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
As this may affect the level of harm 
from the intraoperative heart rate, it 
will be used in the calculation of 
change in heart rate (absolute and %) 
 
HSM contains heart rate of elective 
surgery patients at the anesthetic clinic 
and prior to entering the operating 
room, while Innovian contains the 
initial heart rate of all patients in the 
operative room. The lowest heart rate 
from either Innovian or HSM will be 
used. 
CIHI 
DAD 
Emergency 
surgery  
 
[Derived by 
HDNS]  
 
ENTRYCO
D: method of 
entry  
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
ENTRYCOD = E (i.e. emergency 
department) and/or ADFROM = [any 
except 085 (QEII) – i.e. transferred 
from another institution] excluding 
elective admission (flag) will be used 
to identify surgeries as emergency 
surgery. As the emergency nature of 
surgery has been shown to affect 
outcomes, it will be included as a 
covariate.  

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ADFROM: 
Institution 
from Number 
CIHI 
DAD 
ADTYPE:  
Admission 
Type   
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
This variable will be used internally 
by HDNS to create a flag for elective 
admission. This will not be included in 
the dataset. This will help identify 
patients who did not have emergency 
surgery.   
 
Indicator of surgical complexity 
CIHI 
DAD 
CMG: Case 
Mix Group 
(“a grouping 
methodology 
developed by 
CIHI that 
categorizes 
acute care 
patients into 
groups based 
on 
similarities of 
diagnosis, 
intervention, 
LOS, and 
resource 
requirements.
”) 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
All CMG will be included into 
modeling to determine the CMG with 
the highest postoperative risk.  
CIHI 
DAD 
PIMR: 
Procedural 
Index for 
Mortality 
Risk  
 
[Derived by 
HDNS] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
As the PIMR has been shown to affect 
outcomes, it will be included as a 
covariate. HDNS will calculate using 
DXCODE according to a published 
protocol (35).  
Detailed protocol will be sent to the 
HDNS. 
CIHI 
DAD 
Subgroup 
analysis flag: 
intermediate 
to high risk, 
gender 
neutral 
elective 
surgeries  
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
To create a list of intermediate to high 
risk, gender neutral elective surgeries 
for subgroup analysis. Detailed list of 
CCI codes will be sent to the HDNS. 

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[Derived by 
HDNS based 
on PCODE] 
CIHI 
DAD 
PCODE[1-
n]: Procedure 
[1-n] Code, 
according to 
Classification 
of Health 
Interventions 
(CCI).  
The one 
PCODE 
correspondin
g to the 
specific 
PDATE will 
be obtained, 
i.e. the 
correspondin
g number 
(e.g. 
PDATE1) 
will be used 
for all 
procedure-
related codes 
(i.e. 
PCODE1) 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Covariate in model 
The PCODE corresponding to the 
included PDATE for each patient will 
be included for modeling as a 
covariate.  
CIHI 
DAD 
PDSERV[1-
n]: Procedure 
[1-n] Doctor 
service  
 
The one 
PDSERV 
correspondin
g to the 
specific 
PDATE will 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
The one PDSERV corresponding to 
the included PDATE allows us to 
determine the surgical service, and 
since the type of surgery may affect 
outcomes, it will be included as a 
covariate. We will include the 
following PDSERV: 
00030 General Surgery 
00031 Cardiac Surgery 
00032 Neurosurgery 
00034 Orthopedic Surgery  
00035 Plastic Surgery 

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be obtained, 
i.e. the 
correspondin
g number 
(e.g. 
PDATE1) 
will be used 
for all 
procedure-
related codes 
(e.g. 
PDSERV1) 
 
 
 
00036 Thoracic Surgery 
00037 Vascular Surgery 
00039 Urology  
00050 Obstetrics and Gynecology  
00059 Colorectal Surgery 
00060 Otolaryngology 
00073 General Surgical Oncology 
 
Intraoperative variables 
Innovian 
SBP: 
Systolic 
blood 
pressure  
 
[Derived by 
Innovian as 
multiple 
variables – 
see right] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Exposure variable to be included into 
modeling. The following variables will 
be derived by Innovian:  
5. Maximum change from 
preoperative SBP, in a) 
absolute change (mmHg), and 
b) relative change 
(%)(emergency and elective 
cases analyzed separately due 
to the lack of preoperative 
blood pressure in the 
emergency group) 
6. Cumulative duration (minutes) 
20% below preoperative SBP 
7. Longest single episode 
(minutes) below a) 80, b) 90, 
and c)100 mmHg 
8. Cumulative duration (minutes) 
below a) 80, b) 90, and c)100 
mmHg 
Innovian 
MAP: Mean 
Arterial 
Pressure  
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Exposure variable to be included into 
modeling. The following variables will 
be derived by Innovian: 
1. Maximum change from 
preoperative MAP, in a) 
absolute change (mmHg), and 

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[Derived by 
Innovian as 
multiple 
variables – 
see right] 
b) relative change (%) 
(emergency and elective cases 
analyzed separately due to the 
lack of preoperative blood 
pressure in the emergency 
group. Note that since the 
HSM database only contains 
SBP and DBP, not MAP, MAP 
will be calculated using MAP 
= 1/3*SBP + 2/3*DBP) 
2. Cumulative duration (minutes) 
20% below preoperative 
MAP 
3. Longest single episode 
(minutes) below a) 60, b) 65, 
c) 70, and d) 80mmHg 
4. Cumulative duration (minutes) 
below a) 60, b) 65, c) 70, and 
d) 80mmHg 
Innovian 
HR: Heart 
rate  
 
[Derived by 
Innovian as 
multiple 
variables – 
see right] 
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Exposure variable to be included into 
modeling. Emergency and elective 
cases analyzed separately due to the 
lack of preoperative heart rate in the 
emergency group. The following 
variables will be derived by Innovian: 
6. Maximum change (BPM) from 
preoperative heart rate 
(positive and negative) 
7. Relative change (%) from 
preoperative heart rate 
(positive and negative) 
8. Maximum pulse variation 
(maximum heart rate minus 
minimum heart rate)  
9. Longest single episode 
(minutes) a) below 60, and b) 
above 100BPM 
10. Cumulative duration (minutes) 
a) below 60, and b) above 
100BPM 
Innovian  Use of 
hemodynami
c 
medications  
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Exposure variable to be included into 
modeling. The following variables will 
be derived by Innovian:  
6. Vasopressor/inotrope use (yes 
vs. no): phenylephrine, 

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[Derived by 
Innovian as 
multiple 
variables – 
see right] 
 
norepinephrine, epinephrine, 
vasopressin, dobutamine, or 
milrinone 
7. Infusion of any 
vasopressor/inotropes above 
(yes vs. no) (identified by unit 
of weight over time) 
8. Phenylephrine/ephedrine bolus 
(yes vs. no) (identified by unit 
of weight only)  
9. Vasodilator use (yes vs. no): 
labetalol, esmolol, 
nitroglycerin, nitroprusside    
10. Infusion of any vasodilator 
above (yes vs. no) (identified 
by unit of weight over time) 
Innovian 
SpO2 : 
Oxygen 
saturation by 
pulse 
oximetry 
[Derived by 
Innovian as 
multiple 
variables – 
see right] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Exposure variable to be included into 
modeling. The following variables will 
be derived by Innovian:    
3. Longest single episode 
(minutes) below a) 88, and b) 
90% 
4. Cumulative duration (minutes) 
below a) 88, and b) 90%  
Innovian 
EtCO2: End-
tidal (i.e. 
exhaled)  
 
[Derived by 
Innovian as 
multiple 
variables – 
see right] 
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Exposure variable to be included into 
modeling. The following variables will 
be derived by Innovian: 
1. Longest single episode 
(minutes) a) <35, and b) >45 
2. Cumulative duration (minutes) 
below a) <35, and b) >45 
 
HSM  
Duration of 
surgery  
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
As this may affect outcomes, it will be 
included as a covariate 
Innovian 
Type of 
anesthesia  
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Categorical variable: general, 
neuraxial, peripheral nerve block, 
and/or managed anesthesia care [i.e. 

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sedation] – multiple concurrent 
possible). As this may affect 
outcomes, it will be included as a 
covariate. 
Innovian will be used instead of the 
CIHI ANATYP (Anaesthetic Type), 
since anesthetic type is a mandatory 
field in Innovian and will allow for 
multiple concurrent anesthetic types.  
Innovian 
Average 
MAC-
adjusted: 
Minimal 
Alveolar 
Concentratio
n adjusted by 
age   
[Derived by 
Innovian] 
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Age-adjusted MAC will be calculated 
by Innovian from the end-tidal % of 
all inspired volatiles according to 
published protocol (36). The MAC, 
available every 15 seconds will be 
averaged (i.e. time-averaged MAC). 
The averaged MAC will be included 
as a covariate. 
Innovian 
BIS : Bi-
spectral 
Index   
 
[Derived by 
Innovian] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Duration in minutes for BIS <46. As 
deep anesthesia (low BIS) may affect 
outcomes (50), it will be included as a 
covariate. 
Innovian  Laparoscopy
-booked 
 
[Derived by 
Innovian] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Binary variable (yes/no), as defined by 
“laparoscope” or “laparoscopic” in 
procedure name.  
DAD   
Laparoscopy 
converted to 
open flag 
STATUS[1-
n] 
 
[Flag by 
HDNS] 
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Flag: STATUS[1-n] = C “Converted 
from endoscopic to open” 
 
Since Innovian procedure names are 
usually from OR booking, procedures 
where laparoscopy was converted to 
open would be included in the 
laparoscopy (booked) variable.  

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DAD   
Laparoscopy
-actual  
[Derived by 
HDNS] 
 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
This is derived from as “laparoscopy-
booked” excluding “laparoscopy 
converted to open flag” 
As this may affect outcomes, it will be 
included as a covariate. 
Innovian 
Temperatur
e  
[Derived by 
Innovian as 
multiple 
variables – 
see right] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
Exposure variable to be included into 
modeling. The following variables will 
be derived by Innovian: duration 
(minutes) a) < 36C, and b) > 38C 
(38) 
Innovian
, HSM   
EBL: 
Estimated 
blood loss 
(mL) 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
As this may affect outcomes, it will be 
included as a covariate. 
Innovian  Crystalloid 
use >1000m
L 
 
[Derived by 
Innovian] 
Patient 
Jan 1, 2013 
to Dec 1, 
2017 
As this may affect outcomes, it will be 
included as a covariate. 
 
Derived by Innovian as a continuous 
variable in mL, if 
Crystalloid >1000mL: 
Crystalloid = Ringer Lactate + Normal 
Saline + Plasmalyte + Normosol 
Note: this includes the total volume 
from infusions marked by mL/hour x 
hour infused 
Innovian  Hemoglobin: 
lowest 
Hemoglobin 
within 2 
days after 
OR (g/L) 
(including 
day of OR) 
[Derived by 
Innovian] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
As this may affect outcomes, it will be 
included as a covariate. 

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Innovian 
PRBC: Red 
Blood Cell 
transfusion 
(mL) 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
As this may affect outcomes, it will be 
included as a covariate. 
Innovian  Surgical 
APGAR 
Score 
[Derived by 
Innovian 
based on 
variables 
already 
requested] 
Patient  
Jan 1, 2013 
to Dec 1, 
2017 
0 points 
1 point 
2 points 
3 points 
4 points
Estimated 
blood loss 
(mL) 
> 1,000 
601−1,00
0 
101−600 
≤ 100 
Lowest mean 
arterial 
pressure 
(mmHg) 
< 40 
40−54 
55−69 
≥ 70 
Lowest heart 
rate (beats/min) > 85 
76−85 
66−75 
56−65 
Surgical score (0-10) = sum of all 
points  
The Surgical APGAR Score is an 
existing, validated method of risk 
stratification at the end of surgery, and 
will be compared to the machine 
learning models.  
 
Mortality 
Vital 
Statistics 
Mortality 
(30-day all 
cause) 
 
[Derived by 
HDNS based 
on DOD 
from Vital 
Statistics]  
 
Patient  
Jan 1, 2013 
to Dec 31, 
2017 
Binary outcome (yes/no), derived from 
DOD within 30 days from PDATE. 
This is needed to compute 30-day all-
cause mortality. Primary outcome for 
the study to be used for modeling.   
 
CIHI 
DAD 
OPDEATH: 
operative 
death 
 
Patient 
Jan 1, 2013 
to Dec 31, 
2017 
OPDEATH will be described in 
descriptive statistics. 
1 = Died in Operating Room 
2 = Did Not Die in Operating Room 
Vital 
Statistics  
DOD: Date 
of death  
Patient  
Jan 1, 2013 
to Dec 31, 
2017 
For internal use by HDNS to compute 
30-day all-cause mortality (primary 
outcome), and the Number of 

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postoperative day of death (i.e. Date of 
death minus PDATE).  
CIHI 
DAD 
Number of 
postoperativ
e day of 
death  
 
[Derived by 
HDNS based 
on DOD 
from Vital 
Statistics]  
Patient 
Jan 1, 2013 
to Dec 31, 
2017 
Derived discrete (n = 1, 2, 3… 30) 
variable, of the patients with 30-day 
mortality = yes. This variable is 
defined as DOD [if 30 days after 
PDATE] minus PDATE.   
 
Survival statistics will be performed 
(% vs. postoperative day) 
 
CIHI 
DAD  
 
 
 
 
 
 
 
In-hospital 
death  
[Derived by 
HDNS based 
on 
“DISCHAR
GE: 
Discharge 
disposition”]  
 
 
Patients   
Jan 1, 2013 
to Dec 31, 
2017 
Derived binary variable (yes/no). Of 
those who died within 30 days after 
surgery, descriptive statistics will be 
performed for in-hospital vs. out-of-
hospital mortality (yes/no) 
 
“Yes” if DISCHARGE = 7 or 8:  
7 = Died  
8 = Cadaver donor admitted for 
organ/tissue retrieval 
 
Else = “no”  
Vital 
Statistics 
UCAUSE: 
Underlying 
Cause of 
Death 
 
 
Patient 
who died 
within 30 
days 
after 
surgery  
Jan 1, 2013 
to Dec 31, 
2017 
For patients who died within 30 days 
of surgery, we are interested in the 
cause of death. The UCAUSE will 
only be used internally by HDNS to 
create flags for these causes of death 
as categorized according to ICD codes 
as follows: 
acute myocardial infarction (I21 to 
I22, I24), cardiac arrest (I46.0, 146.1, 
I46.9), ventricular tachycardia (I47.2), 
shock (R57, T81.1) excluding septic 
shock (R57.2), congestive heart failure 
(I50.0, I50.1, I50.9), pulmonary edema 
(J81), complete heart block (I44.2), 
pneumonia (J13 to J18, J69.0, J69.8, 
J95.4), pulmonary embolism (I26), 
acute respiratory failure (J95.1, J95.2, 

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J96.0), respiratory arrest (R9.2), 
strokes and transient ischemic 
attacks (I60 to I69, 
G45), Delirium (F05), Acute kidney 
injury (N17), Septic shock (R57.2)  
 
Causes not fitting the above secondary 
outcomes will be categorized 
according to standard ICD categories:  
Infectious diseases (A00-B99) 
Neoplasm (C00-D48) 
Diseases of the blood and blood-
forming organs and certain disorders 
involving the immune mechanism 
(D50-D89)  
Endocrine, nutritional and metabolic 
diseases (E00-E90) 
Mental and behavioural disorders 
(F00-F99) 
Diseases of the nervous system (G00-
G99) 
Diseases of the circulatory system 
(I00-I99) 
Diseases of the respiratory system 
(J00-J99) 
Diseases of the digestive system (K00-
K93) 
Diseases of the skin and subcutaneous 
tissue (L00-L99) 
Diseases of the musculoskeletal 
system and connective tissue (M00-
M99) 
Diseases of the genitourinary system 
(N00-N99) 
Pregnancy, childbirth and the 
puerperium (O00-O99) 
Congenital malformations, 
deformations and chromosomal 
abnormalities (Q00-Q99) 

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Injury, poisoning and certain other 
consequences of external causes (S00-
T98) 
External causes of morbidity and 
mortality (V01-Y98) 
 
Causes not fitting any of the above 
categories will be coded as UCAUSE 
= other 
 
Descriptive statistics will be 
performed for causes.  
Vital 
Statistics 
All causes of 
death (case 
1-13)  
 
 
Patient 
who died 
within 30 
days 
after 
surgery  
Jan 1, 2013 
to Dec 31, 
2017 
For patients who died within 30 days 
of surgery, we are interested in the 
cause(s) of death. However, since the 
UCAUSE may be missing or not 
reflect the whole picture, we would 
also like to request all causes of death. 
This will only be used internally by 
HDNS to create flag(s) for these 
causes of death as categorized 
according to ICD codes as follows 
(note that each patient may have 
multiple flags):  
acute myocardial infarction (I21 to 
I22, I24), cardiac arrest (I46.0, 146.1, 
I46.9), ventricular tachycardia (I47.2), 
shock (R57, T81.1) excluding septic 
shock (R57.2), congestive heart failure 
(I50.0, I50.1, I50.9), pulmonary edema 
(J81), complete heart block 
(I44.2), pneumonia (J13 to J18, J69.0, 
J69.8, J95.4), pulmonary embolism 
(I26), acute respiratory failure (J95.1, 
J95.2, J96.0), respiratory arrest (R9.2), 
strokes and transient ischemic attacks 
(I60 to I69, 
G45), Delirium (F05), Acute kidney 
injury (N17), Septic shock (R57.2)  
 

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Causes not fitting the above secondary 
outcomes will be categorized 
according to standard ICD categories:  
Infectious diseases (A00-B99) 
Neoplasm (C00-D48) 
Diseases of the blood and blood-
forming organs and certain disorders 
involving the immune mechanism 
(D50-D89)  
Endocrine, nutritional and metabolic 
diseases (E00-E90) 
Mental and behavioural disorders 
(F00-F99) 
Diseases of the nervous system (G00-
G99) 
Diseases of the circulatory system 
(I00-I99) 
Diseases of the respiratory system 
(J00-J99) 
Diseases of the digestive system (K00-
K93) 
Diseases of the skin and subcutaneous 
tissue (L00-L99) 
Diseases of the musculoskeletal 
system and connective tissue (M00-
M99) 
Diseases of the genitourinary system 
(N00-N99) 
Pregnancy, childbirth and the 
puerperium (O00-O99) 
Congenital malformations, 
deformations and chromosomal 
abnormalities (Q00-Q99) 
Injury, poisoning and certain other 
consequences of external causes (S00-
T98) 
External causes of morbidity and 
mortality (V01-Y98) 
 

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Causes not fitting any of the above 
categories will be coded as UCAUSE 
= other 
 
Descriptive statistics will be 
performed for causes.  
 
Additional Postoperative Morbidity (in addition to DXCODE above) 
CIHI 
DAD 
FLAG_HE
ARTRESUS
: Heart 
Resuscitatio
n Flag  
 
 
Patient 
Jan 1, 2013 
to Dec 31, 
2017 
Part of secondary outcomes to be 
included into model. Descriptive 
statistics will be performed for this 
subcategory, within the category of 
cardiac complications, and an overall 
composite measure of each category.  
0 = No 
1 = Yes 
CIHI 
DAD 
FLAG_ME
CHVEN_G
E96: 
Mechanical 
Ventilation  
96 hours  
  
 
Patient 
Jan 1, 2013 
to Dec 31, 
2017 
Part of secondary outcomes to be 
included into model. Descriptive 
statistics will be performed for this 
subcategory, within the category of 
respiratory complications, and an 
overall composite measure of each 
category.  
0 = No 
1 = Yes 
CIHI 
DAD 
Composite 
morbidity  
 
[Derived by 
HDNS]  
 
Patient 
Jan 1, 2013 
to Dec 31, 
2017 
Binary variable (yes/no) derived based 
on any of the following: 
FLAG_HEARTRESUS, 
FLAG_MECHVEN_GE96, or ICD 
code of any of: acute myocardial 
infarction (I21 to I22, I24), cardiac 
arrest (I46.0, 146.1, I46.9), ventricular 
tachycardia (I47.2), shock (R57, 
T81.1) excluding septic shock (R57.2), 
congestive heart failure (I50.0, I50.1, 
I50.9), pulmonary edema (J81), 
complete heart block 
(I44.2), pneumonia (J13 to J18, J69.0, 
J69.8, J95.4), pulmonary embolism 
(I26), acute respiratory failure (J95.1, 
J95.2, J96.0), respiratory arrest (R9.2), 
strokes and transient ischemic attacks 
(I60 to I69, 

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G45), Delirium (F05), Acute kidney 
injury (N17), Septic shock (R57.2)  
 
Part of secondary outcomes to be 
included into model. Descriptive 
statistics will be performed. 
CIHI 
DAD 
ICU 
admission  
[Derived by 
HDNS] 
 
SCUNUM[1
-n]: Special 
Care 
Unit Num 
“The SCU 
Unit Number 
is a code 
identifying 
the type of 
special care 
unit where 
the patient 
receives 
critical 
care.”  
 
[Derived by 
HDNS] 
 
Patient 
Jan 1, 2013 
to Dec 31, 
2017 
Two variables to be derived:  
1. Preoperative ICU (yes/no) 
2. Postoperative ICU (yes/no) 
 
Preoperative ICU admission (yes/no) 
is a covariate to be included into the 
model. Postoperative ICU admission 
(yes/no) is a secondary outcome to be 
included into model. Descriptive 
statistics will be performed.  
 
Derivation process:  
ICU = yes include the following 
SCUNUM:  
10 = Medical Intensive Care Nursing 
Unit 
20 = Surgical Intensive Care Nursing 
Unit 
25 = Trauma Intensive Care Nursing 
Unit 
30 = Combined Medical/Surgical 
Intensive Care Nursing 
Unit 
35 = Burn Intensive Care Nursing Unit 
40 = Cardiac Intensive Care Nursing 
Unit Surgery  
45 = Coronary Intensive Care Nursing 
Unit  
80 = Respirology Intensive Care 
Nursing Unit 
 
Else ICU = no  
 
If the SCU Admit Date is on or before 
PDATE, then it would be defined as 
Preoperative AND postoperative ICU 
admission. Else, postoperative ICU 
admission only.  

July 4, 2019 
Hemodynamic Predictors Protocol - Ke et al. REB#1024251 
  
 
59
CIHI 
DAD 
READMIT: 
Readmission 
Code (within 
30 days after 
PDATE) 
[Derived by 
HDNS] 
Patient 
Jan 1, 2013 
to Dec 31, 
2017 
Readmission within 30 days of 
PDATE (yes/no) is a secondary 
outcome to be included into model. 
Descriptive statistics will be 
performed.   
 
Derived:  
Postoperative readmission = yes 
include the following:  
1 = Planned readmission from 
previous acute care   
2 = Unplanned readmission within 7 
days following discharge from acute 
care  
3 = Unplanned readmission 8 – 28 
days following discharge from acute 
care  
4 = <=7 days, unplanned 
5 = New patient 
9 = None of the above. 
Else Postoperative readmission = no 
CIHI 
DAD 
LOS:ELOS 
Ratio of 
Length of 
Stay (LOS) 
to Expected 
Length of 
Stay (ELOS) 
as assigned 
by the Case 
Mix 
Grouping 
[Derived by 
HDNS]  
Patient 
Jan 1, 2013 
to Dec 31, 
2017 
This will be dichotomized by HDNS: 
LOS:ELOS >1 (yes/no). This is a 
secondary outcome to be included into 
model. Descriptive statistics will be 
performed.