Development and Internal Validation of Models Involving Vital Signs to Predict Troponin Level and Myocardial Injury After Non-cardiac Surgery: a Single-centre Retrospective Cohort Study
Conditions: Myocardial Injury, Non-cardiac Surgery, Complication,Postoperative, High-sensitivity Troponin
Sponsor: University of British Columbia

 
 
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Development and internal validation of models involving vital signs to predict troponin 
level and myocardial injury after noncardiac surgery: a single-centre retrospective 
cohort study 
 
Protocol  
 
Principal Investigator: 
Dr. Su-Yin MacDonell, MD, MSc, FRCPC 
Clinical Assistant Professor 
Department of Anesthesia, St. Paul's Hospital 
Department of Anesthesiology, Pharmacology & 
Therapeutics, University of British Columbia 
 
Dr. Xue Chen (Janny) Ke, MD, FRCPC 
Anesthesiologist, Department of   
            Anesthesia, St. Paul's Hospital 
Department of Anesthesiology, Pharmacology & 
Therapeutics, University of British Columbia 
Adjunct, Department of Anesthesia, Pain Management 
& Perioperative Medicine, Dalhousie University 
 
 
Co-Investigators: 
 
Dr. Ryan Best, MD 
Resident, Department of Anesthesia, St. Paul's Hospital 
Department of Anesthesiology, Pharmacology & 
Therapeutics, University of British Columbia 
 
Dr. Paula Branco, PhD 
Assistant Professor, School of Electrical Engineering 
and Computer Science, University of Ottawa  
 
Anusha Kambhampati  
Master’s student, School of Electrical Engineering and 
Computer Science, University of Ottawa  
 
Jayalakshmi Iyer 
Master’s student, School of Electrical Engineering and 
Computer Science, University of Ottawa  
 
Sihaoyu (Sherry) Gao, PhD Candidate 
Department of Statistics, Faculty of Science, UBC 
 
Dr. P.J. Devereaux, MD, FRCPC, PhD 
Professor 
Department of Clinical Epidemiology and Biostatistics 
and Department of Medicine, McMaster University 

 
 
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Population Health Research Institute, Hamilton Health 
Sciences and McMaster University 
 
Dr. Alana Flexman, MD, FRCPC 
Clinical Associate Professor 
Department of Anesthesia, St. Paul's Hospital 
Department of Anesthesiology, Pharmacology & 
Therapeutics, University of British Columbia 
 
Dr. Ron Ree, MD, FRCSC 
Clinical Associate Professor 
Department of Anesthesia, St Paul’s Hospital 
Department of Anesthesiology, Pharmacology, and 
Therapeutics, University of British Columbia 
 
Dr. Charles Lo, MD, MBA, FRCPC 
Associate Chief Medical Information Officer, 
Providence Health 
Department of Anesthesia, St Paul’s Hospital 
Department of Anesthesiology, Pharmacology, and 
Therapeutics, University of British Columbia 
 
Matthias Görges, PhD 
Assistant Prof, Department of Anesthesiology, 
Pharmacology & Therapeutics, University of British 
Columbia; Scientist, Research Institute, BC Children’s 
Hospital 
 
Dr. Lang Wu, PhD 
Professor, Department of Statistics, Faculty of Science, UBC 
 
 
 
 
 
Primary Contact:  
 
 
Dr. Xue Chen (Janny) Ke 
jannyke@alumni.ubc.ca 
 
 
 

 
 
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Development and internal validation of models involving vital signs to predict troponin 
level and myocardial injury after noncardiac surgery: a single-centre retrospective 
cohort study 
  
Introduction 
The incidence of myocardial injury after noncardiac surgery (MINS) is approximately 
12-15% and is associated with an increased risk of 30-day mortality (1), 1-year mortality (2), 
and 2-year major vascular events (3).  Since more than 90% of patients with MINS are 
asymptomatic, routine troponin monitoring is required for detection (4). The postoperative 
days 0, 1, and 2 accounts for approximately 40%, 40%, and 10% of MINS, respectively (4). 
Presently, the Canadian Cardiovascular Society (CCS) perioperative guidelines recommend 
patients identified to be at risk according to the Revised Cardiac Risk Index (RCRI) and 
BNP/NT-proB-type Natriuretic Peptide (NT-proBNP) receive daily postoperative troponin 
monitoring for three days to identify MINS events (5). European and American societies have 
similar recommendations for troponin monitoring to detect MINS (6). 
Current risk stratification models have multiple limitations (4). Most importantly, they 
predict an elevated risk over the postoperative period but cannot pinpoint when MINS may 
happen in the postoperative course given the patients’ changing condition. Moreover, 
prescription of troponin monitoring is not universal (7), infrequent and inconsistent troponin 
monitoring may lead to delayed detection and management, and the rise of troponin is 
delayed by 3-4 hours from the time of injury (4).  
Retrospective cohort studies show association amongst intraoperative and 
postoperative derangements in vital signs and MINS (4). Vital signs are routinely available 
within the electronic medical record, and may serve as objective predictors (i.e. as opposed to 
free text and disease names that have higher risks of misclassification and errors). Using both 
traditional longitudinal analysis techniques and novel methods in machine learning, we will 
investigate whether intraoperative and postoperative vital signs can enhance MINS 
surveillance by providing temporal prediction of MINS events.  
 
Table 1. Preoperative factors studied in literature associated with MINS and/or major 
myocardial complication and mortality 
Variable  
References  
Age  
Goldman 1977 (8), Lee 1999 (9), Kheterpal 2009 (10), 
Botto 2014 (11), House 2016 (12) 
Congestive heart failure  
Lee 1999, Botto 2014 
Coronary artery disease  
Lee 1999, Kheterpal 2009, Botto 2014 
Cerebrovascular disease  
Lee 1999, Kheterpal 2009, Botto 2014 

 
 
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Peripheral vascular disease Botto 2014 
Diabetes 
Lee 1999 (Insulin-dependent), Botto 2014 (though not 
found to be predictive in Kheterpal 2009) 
RCRI 
Lee 1999 
Hypertension  
Kheterpal 2009 (hypertension requiring medication), Botto 
2014, Ruetzler 2020 (4) 
Kidney function 
(laboratory) 
Lee 1999, Botto 2014 (though not found to be predictive in 
Kheterpal 2009) 
BNP / NT-proBNP 
Choi 2010 (13), Rodseth 2013 (14) and 2014 (15) 
metanalyses, Malhotra 2016 (16), Duceppe 2020 (17) 
Anemia  
Feng 2017 (18) 
Heart rate  
Abbott 2016 (>96) (19), Laitio 2004 (increased variability) 
(20), Devereaux 2011 (every increase by 10 from baseline) 
(21) 
American Society of 
Anesthesiologist physical 
status 
House 2016 
 
Table 2. Intraoperative factors studied in literature associated with MINS and/or major 
myocardial complication and mortality 
Variable  
References  
Type and risk of surgery 
(various definitions) 
Lee 1999, Boersma 2005 (22), Davenport 2007 (23) 
Not found to be predictive in Kheterpal 2009 likely due to 
different definitions and study populations. 
Emergency surgery  
  
Detsky 1986 (24), Boersma 2005, Kheterpal 2009, Botto 
2014, House 2016, Zhao 2017 (25), House 2016 
Emergency surgery patients excluded in RCRI (Lee 1999) 
Red blood cell (RBC) 
transfusion 
Kheterpal 2009, Whitlock 2015 (26), Wu 2010 (27), 
Glance 2011 (28), Devereaux 2011 

 
 
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Hypotension (mmHg) 
Wesselink 2018 (29), Sessler 2018 (30) 
Tachycardia (beats) 
House 2016 (>100 beats for >59 minutes), Abbott 2018 
(>100) 
 
Table 3. Postoperative factors studied in literature associated with MINS and/or major 
myocardial complication and mortality 
Variable  
References  
Postoperative hypotension  
  
van Lier 2018 (quartiles, 3 days postoperatively) (31), 
Sessler 2018 (<90mmHg, 4 days postoperatively) 
Liem 2020 (duration MAP <75, 24 hours postoperatively) 
(32) 
Acute kidney injury  
Zhao 2017  
Vasopressor use 24 hours 
after surgery 
Zhao 2017 
 
 
Objectives 
1. To develop and internally validate a model that uses the duration and degree of 
intraoperative and postoperative hypotension to predict the daily maximum troponin 
level from postoperative days zero to two, in a high risk population where troponin 
monitoring was ordered.  
2. To develop and internally validate a model that uses the duration and degree of 
intraoperative and postoperative hypotension to predict daily probability of MINS or 
death (binary outcome, according to the 2021 American Heart Association (AHA) 
definitions (6) from postoperative days zero to two.  
3. To evaluate how different definitions of hypotension affect the primary and secondary 
models above, and analyze the intraoperative and postoperative hypotension 
separately to determine whether intraoperative or postoperative hypotension alone are 
sufficient for prediction.  
4. To explore whether other intraoperative and postoperative vital sign information 
(heart rate, oxygen saturation, and end-tidal carbon dioxide derangements at various 
definitions) add predictive value to the primary and secondary models above.  
5. To use machine learning methods to perform exploratory analysis to determine 1) 
optimal methods for imputation for time series data; 2) visualization of time series 
data in the setting of prediction; 3) development and internal validation of machine 
learning models to use the time series data to predict troponin levels; and 4) to 
determine how many hours earlier than a binary MINS diagnosis were vital signs able 

 
 
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to predict a MINS diagnosis (as determined by when in the time series prior to MINS 
diagnosis that the model achieved various thresholds of predicted probability). 
 
Methods 
This is a single-center, retrospective cohort study on the development and internal validation 
of a prediction model. 
 
Population  
This study will include patients aged 45 years and older undergoing inpatient noncardiac 
surgery, who had MINS protocol ordered for postoperative high sensitivity troponin 
monitoring based on the CCS guidelines (5) from January 2020 to June 2021 (estimated 
sample size of 750, event rate of ~75).  
Patients will be excluded if they do not have intraoperative and postoperative vital 
signs, or any postoperative troponin measurements. Patients who underwent repeat surgery or 
were readmitted within the first 72 hours postoperatively will be excluded, since patients who 
are discharged and readmitted will not have vital measurements as outpatients. If a patient 
had multiple admissions where the MINS protocol was ordered, only the first inpatient 
admission will be included. Patients who had positive troponin on postoperative day 4 will be 
excluded, since vital signs are only included up to postoperative day 3 for consistency.  
 
 
Data sources  
Following research ethics board approval, data will be extracted from Cerner Surginet 
Anesthesia (intraoperative electronic records) and Cerner PowerChart (preoperative and 
postoperative electronic records). The extracted data will consist of cohort characteristics 
(patient, anesthetic, surgical variables relevant to MINS (see Data Dictionary below), and 
patient vital signs with time stamps up to postoperative day 3. The time series time stamps 
will be transposed to have the start of surgery as 00:00, Jan 1, 2020. A manual chart review 
will also be performed to extract data not automatically retrieved from Cerner and Surginet 
(e.g. diagnoses of morbidities). We will extract relevant preoperative comorbidities, including 
RCRI and Charleson Comorbidity Index categories that are available to the Cerner team via 
CIHI with no date limit to obtain all preoperative comorbidities (please see Appendix). The 
Cerner, Surginet, and chart review data will then be linked by de-identified study ID and full 
surgery date.  
 
Duration of data collection   
Preoperative laboratory values are collected within 30 days before surgery. Preoperative 
vitals are collected within 24 hours before surgery. The total duration of subsequent data 
collection will be from the time of surgery up to postoperative day 3 (since the MINS 
protocol monitors for 3 days) or hospital discharge or death, whichever occurs first. Since 
90% of MINS happen between postoperative days zero and two (4) and the frequency of 
monitoring would likely decrease by postoperative day 3, we will model troponin from 
postoperative day 0 to postoperative day 2 for the primary analysis to balance utility of the 
temporal model and prediction data quality.  
 

 
 
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Outcome 
In the primary model, we will model high-sensitivity troponin (hsTnT) level as a continuous 
outcome, such that the model will remain useful if the troponin threshold definitions for 
MINS evolve.  
 
In the secondary model, we will use the American Heart Association (AHA) definitions (6) 
for MINS, briefly: 
1. Postoperative hsTnT 20 to <65 ng/L with an absolute increase of ≥5 ng/L amongst 
any postoperative troponins, or ≥65 ng/L, “attributable to a presumed ischemic 
mechanism (ie, supply-demand mismatch or atherothrombosis) in the absence of 
an overt precipitating nonischemic cause (eg, pulmonary embolism)”. The 
timestamp for laboratory diagnosis will be set at the time that the sample of 
elevated troponin was drawn.  
a. Nonischemic causes that may be temporally associated (around the time 
prior to troponin elevation) were individually chart reviewed, and include 
PE/DVT, sepsis, rapid atrial fibrillation, cardioversion within 24 hours as 
per the VISION trial (33). Rapid atrial fibrillation will be established as 
the presence of atrial fibrillation on ECG with a ventricular rate >100 bpm. 
Sepsis will be defined as the presence of infection and a systemic 
inflammatory response (SIRS) and per the Third International Consensus 
Definitions for Sepsis and Septic Shock (Sepsis-3). SIRS requires 2 or 
more of the following features: core temperature > 38 C or < 36 C; heart 
rate > 90 beats per minute; respiratory rate > 20 breaths per minute; WBC 
count > 12 x 109/L or < 4 x 109/L (34).  
2. In patients with abnormal baseline troponin values (>14 ng/L) that is considered 
chronic and stable, postoperative myocardial injury is considered acute if there is 
postoperatively a ≥20% rise in high-sensitivity cTnT, or an absolute increase in 
high-sensitivity cTnT of ≥14 ng/L above preoperative values. If a patient was 
diagnosed as having preoperative acute myocardial infarction, there must be a 
decrease in preoperative hsTnT to be considered chronic elevation for this rule to 
apply. Manual physician adjudication will be performed as a double check.  
 
Note that MINS includes myocardial infarction in the AHA definitions. If a patient died 
before MINS diagnosis within 3 days postoperatively, this will be considered an event - i.e. 
the outcome is specifically MINS or death, to avoid competing risk bias due to death. 
However, we anticipate death within 3 days after surgery to be very rare.  
  
Only the first positive MINS event is included, and hemodynamic data will be 
truncated up to that point. The MINS “diagnosis time” of MINS vs. no MINS is the first 
troponin in the serial troponin that fits the MINS criteria, with the start time defined as since 
the start of the surgery. 
 
The exploratory modeling objectives will model both troponin level and MINS 
definitions.  
 
Vital sign candidate features (predictors) 
- 
Vital signs time series (SBP, MAP, DBP, HR, SpO2): taken from Surginet 
(intraoperative) and Cerner (preoperatively on the day of surgery, and postoperative) 
- 
These will be modeled using time series data from 1) intraoperative only, 2) 
postoperative only, and 3) both intraoperative and postoperative periods, using 
the following approaches: 

 
 
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- 
Summary predictor variables as defined by clinicians based on 
literature (e.g. duration MAP<75, duration HR >100) - different 
thresholds will be explored  
- 
For the primary model, duration MAP<75 will be used 
(Liem et al.) 
- 
Since vitals on the surgical ward may not be measured as 
frequently, episodes will be defined with the first low value as 
the start of the event and the first next measurement that is 
normal as the end of the event.   
- 
Univariate time series (machine learning) 
- 
Multivariate time series (machine learning) 
 
 
Table 4. Examples of summary variables for vital signs.  
These will be modeled based on time stamps as: 1) intraoperative only, 2) postoperative 
only, and 3) both intraoperative and postoperative periods 
  
Blood pressure 
SBP 
1.  Maximum decrease from preoperative (defined as median 
past 24h throughout this table) SBP, as a) absolute change 
(mmHg), and b) relative change (%) 
2.  Cumulative duration (minutes) 20% below preoperative 
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 decrease from preoperative MAP, as a) absolute 
change (mmHg), and b) relative change (%) 
2.  Cumulative duration (minutes) 20% below preoperative 
MAP 
3.  Longest single episode (minutes) below a) 60, b) 65, c) 70, 
d) 75, e) 80, and f) 85mmHg 
4.  Cumulative duration (minutes) below a) 50, b) 60, c) 65, d) 
70, e) 75, f) 80, and g) 85mmHg 

 
 
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Heart rate 
1. Maximum decrease from preoperative heart rate, as a) 
absolute change (beats per minute, BPM), and b) relative 
change (%) 
2. Maximum increase from preoperative heart rate, as a) 
absolute change (BPM), and b) relative change (%) 
3. Maximum pulse variation (maximum heart rate minus 
minimum heart rate) 
4.  Longest single episode (minutes) below 40, 50, or 60, and 
above 100, 110, or 120 BPM 
5.  Cumulative duration (minutes) a) below 60, and b) above 
100BPM 
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% 
3.   New postoperative requirement for supplementary oxygen  
  
 
Other candidate features 
For generalizability and to decrease bias, we will select features that are routinely collected. 
Non-vital sign candidate features include age, sex, urgent/emergent procedure (for the index 
surgery), postoperative GFR, and comorbidities within the RCRI categories.   
 
A priori features in the primary model 
This a priori list of predictors will be used for the primary modeling in Objective 1: duration 
hypotension (summarized as a percentage of duration of MAP<75 mmHg in the duration of 
time modeled, e.g. postoperative day 0, 1, and 2), age, sex, emergency surgery, RCRI, and 
postoperative GFR, as well as the postoperative day and the interaction between 
postoperative day and % duration MAP<75mmHg as detailed below.      
 
Statistical analysis 
Missing data 
We will exclude patients with no intraoperative or postoperative vital signs and no troponin 
measurements from the cohort. Complete case analysis will be performed for vital sign 
features, with each complete case defined as a patient with >=1 vital sign measurement(s) and 
>=1 troponin measurement(s). Since the frequencies of measurements are different, missing 
vital signs are not considered missing values for inclusion/exclusion purposes as long as 
patients meet the complete case criteria. No imputation will be performed for vital sign data.  
  
For features that are not vital signs (e.g. age, sex), we anticipate minimal missing data 
(as these variables are routinely collected), and assume missing at random. Due to the 
longitudinal data structure with correlated observations, we opted for a simpler method of 
imputation for the primary analysis. For features missing <1%, complete case analysis will be 

 
 
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performed; between 1-<10%, multiple imputation; above 10%, the feature would be excluded 
from the model and the reason for missing data will be investigated.  
 
Data quality 
The data quality of the CIHI vs. chart review results of the comorbidity information will be 
assessed by Pearson correlation, with chart review as the gold standard.  
 
Cohort characteristics 
Continuous data will be presented in mean (SD) if normal, and median (IQR) if non-normally 
distributed. Categorical data will be presented as frequency (%). The following 
characteristics will be presented as overall group, and stratified by MINS vs. no MINS:  
 
 Age  
 Gender 
 American Society of Anesthesiologists’ physical status classification (ASA) 
 Type of anesthesia (general, neuraxial, peripheral, and/or sedation)   
 RCRI score and categories: pre-existing TIA/CVA, insulin dependent diabetes, serum 
creatinine >177 mmol/L, coronary artery disease, congestive heart failure, and high 
risk surgery including intrathoracic, intraabdominal or suprainguinal vascular surgery  
 Charlson Comorbidity Index (CCI) score and categories: history of MI, CHF, PVD, 
CVA/TIA, dementia, chronic pulmonary disease, connective tissue/rheumatic disease, 
peptic ulcer disease, liver disease, diabetes mellitus, hemiplegia/paraplegia, moderate 
to severe CKD, local/metastatic malignancy, AIDS/HIV 
 Surgical service  
 Duration of surgery  
 Surgical priority: emergent/urgent vs. elective 
 ICU admission preoperatively 
 Cardiovascular medications preoperatively: Angiotensin II receptor blockers (ARBs), 
angiotensin-converting enzyme (ACE) inhibitor, and beta blockers (based on 
available names extracted from Cerner; may be missing medications that have been 
held and not restarted in hospital). 
 Estimated blood loss 
 Use of intraoperative/postoperative vasopressors/inotropes 
Yes/no (received anything at all) 
Small amount of intraoperative use (phenylephrine (<200 mcg) or ephedrine (<10 mg) 
Significant only (i.e. exclude if small doses of phenylephrine (<200 mcg) or ephedrine 
(<10 mg) 
 Laboratory preoperatively (30 days before surgery): NT-proBNP, hemoglobin, 
creatinine/GFR, troponin  
 Respiratory rate 
 Postoperative disposition (ward vs. HAU vs. ICU) - multiple entries possible; time of 
moving in/out of each unit  
 Descriptive statistics of troponin as a continuous variable by postoperative days  

 
 
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 Diagnosis of myocardial infarction, MINS, and postoperative myocardial injury 
 MINS troponin monitoring  
 Duration (operative day to the last day of troponin measurement)  
 Numbers of troponin testing done and frequency  
 Time of MINS diagnosis (rule in vs. rule out), hours after end of operation as 
defined by the event of patient leaves the operating room    
 Non-MINS causes of troponin elevation  
 PE/DVT  
 Sepsis  
 Rapid atrial fibrillation  
 Cardioversion < 24 hours  
 Chronic elevation (excluded)  
 Vitals monitoring  
 Frequency (plot frequency vs. POD)  
 Duration within POD3  
 30 Day in-hospital mortality 
 
Also, descriptive summaries of the vital statistics as listed in Table 4 will be presented.  
 
Data processing 
The Cerner, Surginet, and chart review data will then be linked by de-identified Cerner study 
ID. The time series time stamps will be shifted to have the start of surgery as 00:00, Jan 1, 
2020, in order to standardize the times for longitudinal modeling. Data will then be processed 
for removal of vital sign artefacts per the Multicentre Perioperative Outcomes Group protocol 
(35). To generate a final blood pressure dataset, if noninvasive blood pressure and invasive 
blood pressure are available during the same minute, then the measurement that is 1) not 
marked as artefact and 2) has a higher systolic blood pressure will be taken. Data will also be 
processed for calculation of RCRI, determination of the timing of MINS diagnosis, truncation 
of vital sign time series to the appropriate time stamp (see below), and calculation of duration 
under threshold of the time series to create vital sign summary variables as described in Table 
4 for each postoperative day (0-<24h, 24-<48. 48-<72, and <72-96h after surgery end time). 
A formal chart review will also be performed to extract data not automatically retrieved from 
Cerner and Surginet, and verify data quality of automatically extracted comorbidities.  
  
For modeling troponin as a continuous outcome, the vital sign time series will be 
truncated at the last troponin measurement within 3 days of surgery. For modeling the binary 
MINS outcome definitions the data will be truncated at the time of troponin laboratory 
diagnosis of the outcome event, postoperative day 3 (since the MINS protocol monitors for 3 
days) or hospital discharge or death, whichever occurs first. All time measurements will be 
standardized as above in relation to the surgery start time.  
 
 

 
 
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Predictive Modeling Approaches  
Approach I. Analysis using conventional longitudinal modeling (primary objective) 
 
Data visualization  
The initial exploratory analysis of the vital signs and troponin data involves time series 
visualizations and summaries to explore any potential patterns present in the data that may be 
associated with troponin quantiles or MINS or myocardial injury diagnosis. Spaghetti plots of 
the longitudinal data will be created for the outcome and vital signs variables, with lines 
colour-coded by troponin quantiles or MINS or myocardial injury diagnosis. Other relevant 
visualizations (scatterplots, boxplots, etc.) examining the associations between the troponin 
outcome and the vital signs variables will also be created. 
 
Primary Modeling 
The primary analysis will involve building a linear mixed effects model (with random 
intercepts) to predict the maximum daily troponin level between postoperative days 0 to 2 as 
the continuous outcome. The postoperative days 0, 1, and 2 accounts for approximately 40%, 
40%, and 10% of MINS, respectively (4). Due to the limitations in retrospective data and the 
daily troponin monitoring, we will use day as a unit for time rather than hour to avoid 
overestimation of precision by readers.  
  
For the primary and secondary models described in Objectives 1 and 2, an a 
priori list of predictors will be used: duration hypotension (summarized as a percentage 
of duration of MAP<75 mmHg in the duration of time modeled, e.g. postoperative day 
1) age, sex, emergency surgery, RCRI, and postoperative GFR, as well as the 
postoperative day and the interaction between postoperative day and % duration 
MAP<75 mmHg as detailed below.      
  
For longitudinal modeling, the vital signs data and troponin data will be aggregated to 
the day level, so that the model can predict at the daily temporal level. If there are multiple 
troponin measurements on the same day, the maximum will be taken. This aggregation of the 
data is necessary to account for the fact that the vital signs and troponin measurements are 
measured at irregular intervals (both across patients and data types), often due to the acting 
physician’s response to the patient’s condition. Additionally, due to this characteristic of the 
raw data, the vital signs predictors will be created such that they are standardized to be robust 
to the number of daily measurements performed (for example, by taking the percentage of 
day duration, NOT the total duration, of blood pressure below a specific threshold across all 
measurements during that day). A categorical time variable (representing the day of the 
measurement) will be included as a predictor within the model. 
  
Due to the longitudinal nature of the data, we need to include random effects to adjust 
for the dependence between within-patient daily observations. Thus, at minimum, we intend 
to include a random intercept corresponding to each patient within the model. However, we 
may consider including random slopes based on what is observed in the exploratory analysis 
of the vital signs data, though this would be limited by sample size. Since the effect of a vital 
signs covariate may be different depending on the day of monitoring, we will investigate 
interactions between the time predictor and vital signs predictors. This will be performed for 
only a small number of key time-varying predictors to avoid overfitting, given that the total 

 
 
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number of terms within the model will increase by at least 3 (if the interacting vital signs 
predictor is continuous or binary) for each additional interaction term included. 
Visualizations of the effect of each predictor on the response will also be generated, to further 
investigate their relationships within the context of the model.  
  
To summarize, a simplified example of the primary linear mixed effects model would 
be: Troponin = intercept + b1BP_day0 + b2day1 + b3day2 + b4BP_day1*day1 + 
b5BP_day2*day2 + error (plus other non-BP fixed-effect covariates as listed above). An 
example interpretation would be b1 will correspond to the effect of BP on Day 0, b4 will 
correspond to the additional effect of BP on Day 1 (with respect to Day 0) and b5 will 
correspond to the additional effect of BP on Day 2 (with respect to Day 0). However, we will 
perform exploratory analysis to explore other forms of longitudinal modeling to select the 
best model based on discrimination and calibration.   
 
  
Survival analysis will not be employed due to the small variance in the days for the 
outcome (days 0-2). Also, right censoring is assumed in survival analysis, which does not 
apply in our case of troponin rise postoperatively as the majority of patients will not have a 
troponin increase eventually.  
 
Secondary, Sensitivity, and Exploratory Analyses 
In addition to the above outlined primary statistical analysis, we intend to conduct additional 
secondary, sensitivity and exploratory analyses to address Objectives 2 to 4 to examine how 
well the model performs given the following changes: 
 Introduction of transformations or natural cubic splines on continuous predictors or 
outcomes 
 Modelling using a generalized linear mixed effects model for binary definitions of 
troponin increase (per AHA, as described above).  
o Different truncations of vital sign data stream prior to MINS diagnosis (e.g. no 
truncation, 12h before diagnosis) 
o If sample size allows: subgroup analysis involving only patients with 
preoperative troponin; subgroup analysis for patients who are in the intensive 
care unit perioperatively, as this represents a sicker population. 
 Intraoperative only, postoperative only  
 Further inclusion of additional predictors (e.g. % duration of HR > 100) from the 
available vital signs data 
 Exploration of the incremental value of vital signs in addition to the RCRI or NT-
proBNP in predicting MINS or myocardial injury 
 Inclusion of information from postoperative day 3 
 Delta troponin (change from preoperative troponin) using linear contrast  
Model evaluation  
To validate the performance of the linear mixed effects model specified in the primary 
statistical analysis plan, given the longitudinal nature of the data, a repeated patient-based 5-
fold cross-validation will be performed with 100 repetitions (doi: 
10.1093/gigascience/gix019). Patient-based record was chosen as record-wise cross 
validation tends to overestimate prediction accuracy compared to subject-wise cross 
validation. Note that when using a linear mixed effects model to predict troponin for “new” 

 
 
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patients (such as those in a held-out validation set), there are no unique random effects 
corresponding to those patients and so we are assuming the mean response for these “new” 
patients. A “patient-based” approach for the cross-validation approach is performed to avoid 
data leakage from the training set into the validation set (i.e. a patient’s measurements could 
potentially be present in both the training and validation sets). To quantify predictive 
performance, the root mean square error (RMSE) will be computed for each repetition, and 
the corresponding average and standard deviation of those performance measures will be 
reported. Marginal (i.e. fixed effects only) and conditional (i.e. fixed and random effects) R-
squared (Nakagawa and Schielzeth R-squared, doi.org/10.1111/j.2041-210x.2012.00261.x) 
will be calculated. For assessment of calibration, the predicted values will be averaged across 
all repetitions (for each actual observation), and a calibration plot will be generated along 
with measures of the calibration intercept and slope.  
For the models involving the binary outcomes of MINS diagnosis, a similar validation 
approach will be conducted. To assess discrimination, receiver operating characteristic curves 
(ROCs) will be generated and their area under the curves (AUCs) will be computed for each 
repetition, forming a distribution of AUCs. Precision-recall curves will also be considered 
due to the severe class imbalance present in the dataset. Pseudo R-squared will be computed. 
To assess calibration, the mean predicted class probability will be taken across all repetitions 
for each observation and then a decile calibration plot will be created, with corresponding 
calibration intercept and slope.  
Decision curve analysis will be done to determine the net benefit of the various binary 
outcome models across various thresholds, as compared to ordering or not ordering troponin 
on everyone.   
 
Parameter Count Based on Sample Size and Event Rate 
To mitigate potential overfitting of the model onto the available data, we will aim to restrict 
the number of coefficient terms included within our linear mixed effects model to be at most 
15 (i.e. 50 parameters per patient), and 8 variables for the generalized linear mixed effects 
models (i.e. 10 parameters per outcome event). As there is limited guidance for longitudinal 
prediction models, this is a choice justified based on a combination of the anticipated sample 
size, the presence of repeated measures, the modelling of a continuous outcome, the interest 
in examining interactions between vital signs predictors and the time predictor, and lastly to 
minimize uncertainty in the predictions.  
 
 
Approach II. Machine learning exploratory analyses  
 
Aim 1:  
We will evaluate several strategies to determine the optimal integration of workflow for 
imputation and internal bootstrap validation for small medical datasets involving longitudinal 
data, including modeling vital signs as a time series vs. summary variable. The best 
performing workflow will be written as a function available on GitHub.  
 
 

 
 
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Aim 2:  
We will evaluate unsupervised machine learning techniques to describe and cluster patterns 
of time series data. This would help with classification and prediction, visualization, and 
interpretability. Then, we will use supervised machine learning techniques to evaluate what 
patterns in the time series drove predictions in the models, and whether we can determine 
how much sooner the machine learning models were able to predict myocardial injury events 
prior to laboratory detection. The visualization codes will be wrapped into a Python package 
for visualization.  
 
Aim 3:  
To determine whether the predictive performance can be improved by using machine learning 
over traditional models, we will develop and internally validate machine learning models 
(including boosted trees and neural networks) to predict outcomes as described above, using 
time series or summary vital sign variables. The modeling is limited by the small sample size 
and class imbalance, and specific techniques will be explored including temporal-based 
resampling in the derivation set, one-class learners, and autoencoders.  
 
 
 
 

 
 
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Participant Confidentiality and Privacy 
The dataset contains de-identified perioperative patients. Only those listed on this protocol 
will have access to the de-identified dataset for the duration of this project. The dataset 
created for the study will be saved in a password-protected file after the completion of the 
study or, if applicable, after the publication of study results (whichever happens later). At the 
end of 5 years, the data will be deleted permanently in accordance to UBC IT policy. 
  
Potential for Risk and/or Harm 
This study involves the secondary use of existing de-identified data, and no additional data 
will be collected. There is no foreseeable risk of the possible disclosure of any identifiable 
data because the current research will involve de-identified secondary data only. 
  
Report of Findings 
The findings from this research will be presented to the St. Paul’s Hospital Anesthesia 
Department through a grand rounds presentation. In addition, there may be the opportunity to 
present this at an anesthesiology conference and produce a peer-reviewed publication from 
this data. 
 
Data Dictionary 
 
Source: Surginet extraction  
 
Variable name 
Legend 
Reason for request 
IDN 
ID (deidentified) 
To deidentify patients in the 
dataset 
Anesthesia 
Principal Anesthesia Technique 
Epidural 
General 
Local 
Monitored Anesthesia 
care (MAC) / IV Sedation 
None 
Regional 
Spinal 
Other 
Unknown 
  
Cohort characteristics 
 
SBP 
Systolic blood pressure with time 
stamp 
Potential predictors: 
univariate vs. multivariate 
time series; clinician-defined 

 
 
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predictor variables.  
MAP 
Mean arterial blood pressure 
with time stamp 
Potential predictors: 
univariate vs. multivariate 
time series; clinician-defined 
predictor variables.  
HR 
Heart rate/pulse rate with time 
stamp 
Potential predictors: 
univariate vs. multivariate 
time series; clinician-defined 
predictor variables.  
RR 
Respiratory rate with time stamp 
Potential predictors: 
univariate vs. multivariate 
time series; clinician-defined 
predictor variables.  
SpO2 
Oxygen saturation by pulse 
oximetry with time stamp 
Potential predictors: 
univariate vs. multivariate 
time series; clinician-defined 
predictor variables.  
Intraoperative 
Phenylephrine (>200  
mcg) 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
Intraoperative 
Ephedrine (>10 mg) 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
Intraoperative 
Norepinephrine 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
Intraoperative 
Vasopressin 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
Intraoperative 
Epinephrine 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
Intraoperative 
Dobutamine 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
Intraoperative Milrinone 1/0 
Yes/No 
Potential predictor 
Potential confounder 
Time of patient entering 
room  
Time/date 
To identify preoperative 
vitals  

 
 
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Time of patient leaving 
room  
Time/date 
To identify postoperative 
vitals 
 
 
Source: Cerner extraction (please see appendix for details of the CIHI codes) 
 
Variable name 
Legend/ICD-10 code 
Reason for request 
IDN 
ID (deidentified) 
To deidentify patients in the 
dataset 
MRN 
ID 
Required for manual chart 
review of variables that 
cannot be extracted 
automatically 
Age 
Age 
Cohort characteristics 
Gender 
Male vs. Female 
Cohort characteristics 
Surgical priority 
E0: STAT 
E1: <1 hour 
E2: <4 hours 
E2OB: <2 hours 
E3: <8 - 12 hours 
E4: <48 hours 
Elective 
 
A dichotomized variable of 
emergent/urgent vs. elective will 
be created as a predictor in the 
primary model  
Cohort characteristics 
Procedure_code 
Cerner procedure code 
Cohort characteristics  
Procedure_text 
The corresponding free text to 
the Cerner procedure code 
Cohort characteristics  
ASA 
American Society of Anesthesia 
Physical Classification Score 
Score from 1 - 6 assessed by 
consulting anesthesiologist 
Cohort characteristics 

 
 
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SBP 
Systolic blood pressure with time 
stamp (day of surgery to POD3) 
Potential predictors: 
univariate vs. multivariate 
time series; clinician-defined 
predictor variables.  
MAP 
Mean arterial blood pressure 
with time stamp (day of surgery 
to POD3) 
Potential predictors: 
univariate vs. multivariate 
time series; clinician-defined 
predictor variables.  
HR 
Heart rate/pulse rate with time 
stamp (day of surgery to POD3) 
Potential predictors: 
univariate vs. multivariate 
time series; clinician-defined 
predictor variables.  
RR 
Respiratory rate with time stamp 
(day of surgery to POD3) 
Potential predictors: 
univariate vs. multivariate 
time series; clinician-defined 
predictor variables.  
SpO2 
Oxygen saturation by pulse 
oximetry with time stamp (day of 
surgery to POD3) 
Potential predictors: 
univariate vs. multivariate 
time series; clinician-defined 
predictor variables.  
RCRI 
Revised cardiac risk index total 
score 
Potential predictor of MINS 
CCI 
Charlson comorbidity index total 
score 
Potential predictor of MINS 
CVA 
Presence of cerebrovascular 
disease 
CIHI preoperative diagnoses: 
G45.x, G46.x, H34.0, I60.x–
I69.x 
1/0 
Yes/No  
Potential predictor RCRI, 
CCI 
 
IHD 
Presence of ischemic heart 
disease 
CIHI preoperative diagnoses: 
I20.x, I21.x, I22.x, I24.x, I23.x, 
I25.x, Z95.1, Z95.5 
1/0 
Yes/No  
Potential predictor RCRI, 
CCI 

 
 
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CHF 
History of congestive heart 
failure 
CIHI preoperative diagnoses: 
I09.9, I11.0, I13.0, I13.2, I25.5, 
I42.0, I42.5–I42.9, I43.x, I50.x, 
P29.0 
1/0 
Yes/No  
Potential predictor RCRI, 
CCI 
Preoperative creatinine 
Value in μmol/L 
Potential predictor RCRI 
Insulin use 
Insulin (medium or long-acting) 
from medical administrative 
record  
1/0 
Yes/No 
Potential predictor RCRI 
CKD 
CKD stage 4-5 or dialysis 
dependednt 
CIHI preoperative diagnoses: 
N18.3, N18.4, N18.5, Z49.0– 
Z49.2, Z99.2 
1/0 
Yes/No 
Cohort demographics 
Potential predictor RCRI, 
CCI 
PVD 
Presence of peripheral vascular 
disease 
CIHI preoperative diagnoses: 
I70-I79 
1/0 
Yes/No 
Potential predictor of MINS, 
CCI 
Dementia 
History of dementia 
CIHI preoperative diagnoses: 
F00.x-F03.x, F05.1, G30.x, 
G31.1 
1/0 
Yes/No 
Potential predictor CCI 
Chronic pulmonary 
disease 
History of chronic pulmonary 
disease 
CIHI preoperative diagnoses: 
I27.8, I27.9, J40.x–J47.x, J60.x–
J67.x, J68.4, J70.1, J70.3 
1/0 
Potential predictor CCI 

 
 
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Yes/No 
Connective 
tissue/rheumatic disease 
History of connective tissure or 
rheumatic disease 
CIHI preoperative diagnoses: 
M05.x, M06.x, M31.5, M32.x-
M34.x, M35.1, M35.3, M36.0 
1/0 
Yes/No 
Potential predictor CCI 
Peptic ulcer disease 
History of peptic ulcer disease 
CIHI preoperative diagnoses: 
K25.x-K28.x 
1/0 
Yes/No 
Potential predictor CCI 
Mild liver disease 
History of mild liver disease 
CIHI preoperative diagnoses: 
B18.x, K70.0-K70.3, K70.9, 
K71.3-K71.5, K71.7, K73.x, 
K74.x, K76.0, K76.2-K76.4, 
K76.8, K76.9, Z94.4 
1/0 
Yes/No 
Potential predictor CCI 
Severe liver disease 
History of moderate - severe 
liver disease 
CIHI preoperative diagnoses: 
I85.0, I85.9, I86.4, I98.2, K70.4, 
K71.1, K72.1, K72.9, K76.5, 
K76.6, K76.7 
1/0 
Yes/No 
Potential predictor CCI 
Diabetes uncomplicated 
History of diabetes without 
chronic complications 
CIHI preoperative diagnoses: 
E10.0, E10.l, E10.6, E10.8, 
E10.9, E11.0, E11.1, E11.6, 
E11.8, E11.9, E12.0, E12.1, 
E12.6, E12.8, E12.9, E13.0, 
E13.1, E13.6, E13.8, E13.9, 
E14.0, E14.1, E14.6, E14.8, 
E14.9 
1/0 
Potential predictor CCI 

 
 
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Yes/No 
Diabetes complicated 
History of diabetes with chronic 
complications 
CIHI preoperative diagnoses: 
E10.2-E10.5, E10.7, E11.2-
E11.5, E11.7, E12.2-E12.5, 
E12.7, E13.2-E13.5, E13.7, 
E14.2-E14.5, E14.7 
1/0 
Yes/No 
Potential predictor CCI 
Hemiplegia/paraplegia 
History of hemiplegia or 
paraplegia 
CIHI preoperative diagnoses: 
G04.1, G11.4, G80.1, G80.2, 
G81.x, G82.x, G83.0-G83.4, 
G83.9 
1/0 
Yes/No 
Potential predictor CCI 
Localized malignancy 
History of malignancy (including 
lymphoma and leukemia, except 
malignant neoplasm of skin) 
CIHI preoperative diagnoses: 
C00.x-C26.x, C30.x-C34.x, 
C37.x-C41.x, C43.x, C45.x-
C58.x, C60.x-C76.x, C81.x-
C85.x, C88.x, C90.x-C97.x 
1/0 
Yes/No 
Potential predictor CCI 
Metastatic tumour 
History of metastatic solid 
tumour 
CIHI preoperative diagnoses: 
C77.x-C80.x 
1/0 
Yes/No 
Potential predictor CCI 
AIDS/HIV 
History of acquired 
immunodeficiency syndrome or 
human immunodeficiency virus 
CIHI preoperative diagnoses: 
B20.x-B22.x, B24.x 
1/0 
Potential predictor CCI 

 
 
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Yes/No 
Hypertension 
Presence of hypertension 
CIHI preoperative diagnoses: 
I10-I15 
1/0 
Yes/No 
Cohort demographics 
PE  
Presence of pulmonary embolism 
within MINS window 
CIHI postoperative diagnoses: 
I26 
 
1/0 
Yes/No  
MINS diagnosis criteria 
Atrial fibrillation 
Presence of atrial fibrillation 
within MINS window 
CIHI postoperative diagnoses: 
I48.x 
1/0 
Yes/No  
MINS diagnosis criteria 
Cardioversion  
Cardioversion within 24 hours 
MINS 
1/0 
Yes/No  
MINS diagnosis criteria 
Sepsis  
Presence of sepsis within MINS 
window 
CIHI postoperative diagnoses: 
R57.2 
1/0 
Yes/No  
MINS diagnosis criteria 
MINS 
Based on MINS diagnostic 
criteria as described (primary 
outcome) 
1/0 
Yes/No  
MINS diagnosis criteria 
MIpost 
Diagnosis of myocardial 
infarction postoperatively  
1/0 
Yes/No 
Third Universal Definition 
or per cardiology note  

 
 
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PMI 
Diagnosis of postoperative 
myocardial injury 
postoperatively 
1/0 
Yes/No 
StEP criteria 
Troponin 
Preoperative and postoperative 
HS troponin values with time 
stamps 
Value in ng/L 
MINS diagnosis criteria 
Time of MINS diagnosis 
Exclusion Criteria 
Patients who had positive 
troponin on postoperative 
day 4 will be excluded, since 
vital signs are only included 
up to postoperative day 3 for 
consistency. 
 
Estimated blood loss 
Intraoperative recorded blood 
loss 
Volume in mL 
Potential predictor 
Surgical Service 
Vascular 
General 
Orthopedics 
ENT 
Gynecology 
Plastics 
Other 
Potential predictor 
High risk surgery 
Suprainguinal vascular, 
intrathoracic, intraperitoneal 
1/0 
Yes/No 
Potential predictor RCRI 
Disposition 
postoperatively 
Ward vs HAU vs ICU 
Cohort Characteristic 
HAU admission start 
time 
Time stamp 
Determine setting of vital 
measurement 
 
HAU admission end 
time 
Time stamp 
 
Determine setting of vital 
measurement 
 
ICU admission start 
time 
Time stamp 
 
Determine setting of vital 
measurement 

 
 
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ICU admission end time Time stamp 
 
Determine setting of vital 
measurement 
 
Preoperative NT-
proBNP 
Value in ng/L 
Potential predictor 
Preoperative 
Hemoglobin 
Value in g/L 
Cohort characteristics 
Potential predictor 
Preoperative GFR 
Value in mL/min 
Cohort characteristics 
Potential predictor 
Preoperative beta 
blocker use 
Preoperative use (metoprolol, 
bisoprolol, carvedilol, atenolol, 
propranolol, labetalol, nadolol) 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
 
Note that in order to obtain 
these variables and due to 
the limited analytic time 
from the Cerner team, all 
medications in the patient's 
medication administration 
record that is noted as 
completed were pulled 
Preoperative 
ACEI/ARB 
Preoperative use (captopril, 
enalapril, ramipril, perindopril, 
lisinopril, candesartan, 
irbesartan, losartan, telmisartan, 
valsartan, sacubitril/valsartan) 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
 
Note that in order to obtain 
these variables and due to 
the limited analytic time 
from the Cerner team, all 
medications in the patient's 
medication administration 
record that is noted as 
completed were pulled 
Postoperative 
Phenylephrine 
(>200mcg) 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
Postoperative Ephedrine 
(>10mg) 
1/0 
Yes/No 
Potential predictor 
Potential confounder 

 
 
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Postoperative 
Norepinephrine 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
Postoperative 
Vasopressin 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
Postoperative 
Epinephrine 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
Postoperative 
Dobutamine 
1/0 
Yes/No 
Potential predictor 
Potential confounder 
Postoperative Milrinone 1/0 
Yes/No 
Potential predictor 
Potential confounder 
In hospital mortality 
1/0 
Yes/No 
Outcome 
Cohort characteristic 
Start of surgery 
(time/date) 
[Enters OR] 
Time/date  
To define the start of the 
time series  
End of surgery 
(time/date) 
[Leaves OR] 
Time/date 
To define the start of the 
postoperative time series 
Length of OR 
Duration in minutes 
Potential predictor 
PACU length of stay 
Duration in minutes (if available, 
otherwise calculated based on the 
following two values)  
Potential Predictor 
Time of PACU 
admission 
Time/date  
For time stamps 
Time of PACU 
discharge  
Time/date  
For time stamps 
Time/date of hospital 
discharge   
Time/date  
To verify inclusion criteria 
Readmission within 3 
days of surgery  
1/0 
Yes/No 
Patients who underwent 
repeat surgery or were 
readmitted within the first 
72 hours postoperatively 
will be excluded. 
Repeat surgery within 3 
1/0 
Patients who underwent 

 
 
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days of surgery  
Yes/No 
repeat surgery or were 
readmitted within the first 
72 hours postoperatively 
will be excluded. 
CIHI – Canadian Institute for Health Information, ICD-10 (International statistical 
classification of diseases and related problems) codes 
 
 

 
 
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