Cohort Study of Intraoperative Lung Protective Ventilation Trends in a Large Canadian Health Authority
Conditions: Lung Protective Ventilation, Intraoperative Ventilation, COVID, COVID - 19, Non-cardiac Surgery
Sponsor: Perseus Missirlis

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Study protocol 
 
Impact of COVID on patterns and predictors of lung 
protective ventilation in a large Canadian health authority  
 
Principal investigator: 
Perseus Missirlis1,2, MSc, MD, FRCPC 
Co-investigators: 
 
Connie Drewbrook1, MD 
Susan M Lee1,2, MAS, MD, FRCPC  
Edmond Li1,2, MHSc, MD, FRCPC, CIP 
 
Affiliations 
1. Department of Anesthesiology, Pharmacology and Therapeutics, University of British 
Columbia, Vancouver, BC  
2. Department of Anesthesiology and Perioperative Medicine, Fraser Health Authority, 
New Westminster, BC 
Table of contents 
1. Introduction and background 
2 
2. Purpose and Justification 
2 
3. Research question 
3 
4. Research objectives and hypothesis 
3 
5. Methods 
4 
6. Data collection 
4 
7. Statistical plan 
7 
8. Ethical considerations 
10 
9. Data management plan 
10 
10. References 
12 
11. Appendix 
15 

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This protocol has been compiled in accordance with sections 6.1 and 6.2 of the Guidance notes 
for new applications for ethical review by the Fraser Health research ethics board 
1. Introduction and background 
Postoperative pulmonary complications (PPCs) affect 5 to 33% of patients undergoing non-
cardiothoracic surgery, and are a major cause of postoperative morbidity and mortality (1-4). 
PPCs include respiratory infection, respiratory failure, pleural effusion, atelectasis, 
pneumothorax, bronchospasm, aspiration pneumonitis, and acute respiratory distress syndrome 
(ARDS) (5). In addition, patients who develop PPCs have more admissions to the intensive care 
unit (ICU), an increased length of hospital stay (LOS), and increased 30-day mortality rate of up 
to 20%1. One proven method of reducing PCC’s is to use intraoperative lung protective 
ventilation (LPV), with a number needed to treat of 24 for post-operative pneumonia and 38 for 
post-operative ventilatory support in a Cochrane systematic review including over 1000 patients 
(6).  LPV comprises using a tidal volume of 6-8 mL/kg ideal body weight (IBW) and positive 
end-expiratory pressure (PEEP) of 5 cm H2O for initial ventilator settings (7).   
For the past two decades, adoption of a similar strategy of ventilation in the intensive care unit 
for patients with acute respiratory distress syndrome have yielded decreased mortality (8-10).  
However, uptake of LPV usage in the operating room has been slower, with less than 50% 
compliance (11, 12).  Factors that influence compliance in some centers include extremes of 
patient IBW, unoptimized default ventilator settings, and clinician misconceptions surrounding 
LPV guidelines (12-14).  While adherence to LPV was higher for COVID positive patients 
during the start of the COVID pandemic with evidence for lower mortality in the intensive care 
unit (15, 16), it is unknown whether an increase in overall LPV compliance occurred outside of 
COVID positive patients, and if so, whether such a shift in practice was sustained. In addition, 
most studies querying LPV compliance occurred in centers outside of Canada (12-14), with 
differences in medical education and practice patterns compared to Canadian centers.  To fill 
these gaps in the literature, we therefore aim to explore patterns of LPV compliance in a large 
Canadian health authority in a retrospective manner, and to elucidate predictors influencing LPV 
compliance.  We will also explore trends of LPV compliance throughout the COVID pandemic, 
into the post-pandemic era. 
2. Purpose and Justification 
The purpose of this study is to investigate patterns of LPV compliance, elucidate predictors of 
LPV compliance, and gauge the effects of COVID on LPV compliance in Fraser Health 
Authority.  Although the use of nonprotective ventilation has decreased over time, a large 
proportion of patients continue to be ventilated with high tidal volumes and little PEEP(13, 17, 
18). While the use of LPV strategies has been improved, patients that are short-stature, obese, or 
female have an increased tendency to be ventilated with larger tidal volumes in non-Canadian 
studies (11, 19). Contributing to this shortfall may be lack of patient height documentation as 
well as lack of awareness by providers regarding the significant impact of height (rather than 
patient weight) in determining LPV ventilation parameters. Compliance with LPV has been 
shown to be increased following implementing education about LPV, feedback regarding 
developmental performance, departmental LPV policies, and modification of default ventilator 

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settings (12, 14, 19).  Therefore, identifying patterns and predictors of LPV compliance in a 
Canadian context through our study may help inform the design of educational efforts and 
systemic changes to tackle areas of deficiency. 
3. Research question 
Primary research question: In patients undergoing general anesthesia with an endotracheal 
tube for non-cardiac surgery, what is the compliance rate to LPV in Fraser Health Authority, and 
predictors of such compliance?  
Secondary research questions: What are time trends and shifts in LPV compliance from the 
pre-COVID to the post-COVID era?  Are these trends sustained into the “new normal” era? 
4. Research objectives and hypothesis 
The objectives for this study are to: 
1. Identify rates of compliance with LPV guidelines in the Fraser Health Authority in the 
Province of British Columbia, Canada. Compliance with ventilation guidelines is defined 
as meeting two criteria. The first is that their average tidal volume is 6-8 mL/kg IBW and 
the second is that the average positive end expiratory pressure (PEEP) is at least 5 cm 
H2O. Patients ventilated according to one or neither of the above criteria will not be 
considered to have been ventilated according to the guidelines.  
2. To elucidate predictors of compliance with LPV practice among providers.  These 
predictors may help inform the design of educational efforts and systemic changes to 
tackle areas of deficiency. 
3. To elucidate time trends and shifts in LPV compliance from the pre-COVID pandemic to 
post-pandemic era. 
4. To evaluate whether time trends and shifts in LPV compliance through the pandemic are 
sustained. 
The primary hypothesis is that not all patients will be ventilated according to the recommended 
LPV strategy.  We further hypothesize that patient height will be a major predictor in 
determining compliance to LPV,  as our clinical experience suggests that while IBW used to set 
tidal volumes is calculated based on height, many anesthesiologists set tidal volumes by actual 
weight rather than IBW.  Therefore, this practice by may result in tidal volumes outside of a 
patient’s acceptable tidal volume as would be recommended by LPV guidelines.  Our other 
hypothesized predictors to LPV compliance include predictors that indicate respiratory 
dysfunction (e.g. severe COPD, CHF) or severe illness (American Society of Anesthesiologists 
[ASA] classification, sepsis), as anesthesiologists may be more cognizant of the need for LPV in 
these patients.  Lastly, we hypothesize that the COVID pandemic will introduce a shift towards 
increased LPV compliance, but this shift may not be sustained in the post-pandemic era as 
precautions waned. 
 

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5. Methods 
Study design   
Our study is a retrospective cohort study investigating the correlation between hypothesized 
predictors of LPV compliance and actual LPV practice in a large multi-center health authority. 
The study population is all patients who underwent who underwent a general anesthetic for any 
procedure at a Fraser Health Authority hospital from January 2014 – December 2023. See 
Section 7 for more details regarding inclusion and exclusion criteria.  
Sampling and sample description 
We aim to use convenience sampling by including all patients who meet eligibility criteria. All 
procedures conducted under general anesthesia within the Fraser Health Authority with an 
endotracheal tube for over 30 minutes and within the date range specified that meet the inclusion 
criteria are eligible for inclusion in this study. This is a multicentre study and includes all 
hospitals within the Fraser Health Authority using Centricity Perioperative Anesthesia (CPA) 
system, which collects intraoperative data. The American College of Surgeons National Surgical 
Quality Improvement Program (NSQIP) is an outcomes-based program to measure and improve 
the quality of surgical care by tracking patient data preoperatively through 30-days 
postoperatively; NSQIP data will be used for perioperative variables such as comorbidities.  . 
Procedures must be followed by NSQIP and currently within the CPA database to be included in 
our study (see inclusion and exclusion criteria). Based on historical data and the surgical volume 
contributing to the Fraser Health CPA system we expect there to be approximately 100,000 
cases. 
Inclusion and exclusion criteria 
Inclusion criteria for this retrospective study include any patient that is over the age of 18 and 
underwent general anesthesia with endotracheal tube placement for a procedure lasting ≥30 
minutes. Patients must be included in the NSQIP database and linked to their anesthetic dataset 
within CPA by either their name, date of birth, medical record number (MRN, i.e. RC number), 
Personal Health Number (PHN), and/or date of surgery.  Exclusion criteria for this retrospective 
study include: any patient that underwent cardiac or thoracic surgery or received an anesthetic 
that did not utilize an endotracheal tube, ASA 6 (organ donor), Hyperthermic intraperitoneal 
chemotherapy, transplant cases, surgical procedure related to an occurrence or complication of 
prior procedure during the same admission/within 30 days, multiple National Surgical Quality 
Improvement Program (NSQIP) assessed cases within 30 days only for same patient, cases in 
CPA that are unable to be linked to NSQIP data or vice versa. 
6. Data collection 
Data sources 
Predictors and demographic data: The American College of Surgeons National Surgical Quality 
Improvement Program is a validated, risk adjusted, outcome-based program that measures the 

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quality of surgical care internationally and in Canada. NSQIP data contains pre-operative, intra-
operative and 30-day postoperative variables derived from operative reports, medical records and 
patient interviews to provide a comprehensive reflection of 30-day surgical outcomes. Data uses 
standard definitions and is provided in real-time to participating hospitals(20). The data extracted 
from this source are listed below (see Data collection section). 
Intraoperative data: Centricity Perioperative Anesthesia (CPA), an Anesthesia Information 
Management System (AIMS), is an electronic record used by anesthesiologists to automate data 
capture from the operating room. Information is collected using physiologic monitoring devices 
and includes clinical interventions such as placement of intraoperative monitors, derived 
measurements and pharmacologic interventions. The data obtained is automatically collected, 
reliable and can be stored and subsequently used for analysis, typically for quality assurance and 
research purposes. CPA at our institution includes only the basic component of an automated 
anesthesia record, rather than information from the pre-operative, post-operative and ancillary 
areas in the hospital available at other centers. Therefore, to obtain these data, data obtained from 
CPA must be linked to patients’ NSQIP data using with their name, date of birth, medical record 
number (MRN, i.e. RC number), Personal Health Number (PHN), and/or date of surgery. 
Data collection procedures 
This is a retrospective study sampling data from all patients who underwent general anesthesia 
for non-cardiac surgery at a Fraser Health Authority institution from January 2014 – December 
2023 meeting our inclusion criteria. This dataset includes clinical and administrative data from 
all hospitals within the Fraser Health medical system using CPA. There will be no prospective 
recruitment or sampling of patients, and thus a waiver of consent will not be requested in 
accordance with article 3.7A of the Tri-Council Policy Statement: Ethical Conduct for Research 
Involving Humans. 
Predictors and demographic data: Data will be obtained from the NSQIP database include 
patient characteristics such as age, medical record number, sex, age, weight, height, procedure, 
inpatient status, hospital where procedure occurred, elective surgery status, principal anesthesia 
technique, surgical specialty, attending staff surgeon, diabetes mellitus, current smoker within 1 
year, dyspnea, functional heath status, ventilator dependent, history of severe chronic obstructive 
pulmonary disease (COPD), ascites w/in 30 days prior to surgery, congestive heart failure within 
30 days prior to surgery, hypertension requiring medication, acute renal failure, currently 
requiring or on dialysis, disseminated cancer, open wound with or without infection, 
steroid/immunosuppressant use for chronic condition, >10% loss of body weight in the 6 months 
prior to surgery, bleeding disorder, preoperative transfusions (packed red blood cells within 72 
hrs prior to surgery start time), sepsis (SIRS/sepsis/septic shock within 48h postoperatively), 
emergency case, duration of surgical procedure (in minutes), ASA classification, postoperative 
pneumonia, postoperative unplanned intubation, postoperative ventilation > 48 hours, 
intraoperative or postoperative transfusion, death within 30 days of the procedure, and hospital 
length of stay from surgery to discharge. Preoperative bloodwork values include serum sodium, 
serum sodium date, BUN, BUN date, serum creatinine, serum creatinine date, albumin, albumin 
date, total bilirubin, total bilirubin date, AST/SGOT, AST/SGOT date, alkaline phosphatase, 
alkaline phosphatase date, WBC, WBC date, hematocrit, hematocrit date, platelet count, platelet 

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count date, INR, INR date, PTT, PTT date. Post-operative complications will also be obtained 
from the NSQIP database.  These include respiratory complications (pneumonia, re-intubation, 
ventilator use present either before or after surgery) and if the patient died within 30 days post-
operatively. 
Physiologic and therapy data: Data obtained from the CPA data warehouse will include all data 
from the anesthesia monitor (e.g., heart rate, oxygen saturation, and MAP) and ventilator (e.g.: 
tidal volume, end tidal carbon dioxide (EtCO2), and end tidal inhalational gas concentrations) as 
well as any data entered manually (medications, procedures). The primary variables of interest 
include respiratory rate, set and exhaled tidal volume, peak pressure, PEEP, non-invasive and 
invasive blood pressures including systolic, diastolic and mean arterial pressures, heart rate, 
inspired oxygen, oxygen saturation, and end-tidal CO2 and volatile gas measurements.  All data 
will be extracted as averages during “total surgical monitoring time” (see Appendix for details).  
FiO2 values will be restricted to values between 21-100% and SpO2 values will be restricted to 
values between 70-100% prior to calculating averages.  PEEP data will be limited to values 
between 0-15 mmHg.  If data is missing at a given timestamp it will be excluded from the 
analysis.  Measured average tidal volume will be compared to LPV tidal volume based on IBW 
as calculated by the patient’s height. Patients will be deemed to have been ventilated according 
to a lung protective strategy if their average tidal volume was 6-8 mL/kg IBW and their average 
PEEP throughout the case was as least 5 cm H2O. Date of surgery will also be extracted to 
facilitate plotting of the Shewhart chart, and determination of pre-COVID/COVID era status (see 
section 7 Statistical Plan).  Time of surgery start will be extracted to determination of 
night/weekend shift status. 
Measures 
Baseline demographics for all study patients include age, sex, height, weight, smoking status in 
the last year, and ASA classification. Data relating to the surgery includes the date of surgery, 
hospital admission date, hospital discharge date, if the surgery was elective or emergency, a 
common procedural code used to identify the type of surgery, and duration of the procedure. 
To determine “compliance to LPV”, the response variable of our prediction model, tidal volume 
per unit of body weight in kilograms will be calculated and compared to the guidelines. Average 
PEEP will be calculated in cm H2O and compared to the guidelines. The average tidal volume 
will be calculated in mL throughout each case. IBWs will be calculated using height according to 
the Devine formula29: 
Ideal body weight (IBW) (men) = 50 kg + 2.3 kg x (height (inches) – 60) 
Ideal body weight (IBW) (women) = 45.5 kg + 2.3 kg x (height (inches) – 60) 
 
 
 
 
 

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7. Statistical plan 
Software 
Microsoft Excel (Microsoft Corporation, Redmond, WA), R Console (R Foundation for 
Statistical Computing, Vienna, Austria), Anaconda Navigator Distribution (python), and SAS 
Base 9.4 statistical analysis software will be used for analysis.   
Part I: Patterns of compliance to LPV and relationship with default ventilator settings 
• Descriptive statistics for proportion compliance to LPV will be calculated for the entire 
dataset 
• Among the non-compliance cases, the % attributable to the following will be calculated.  
This analysis is useful for determining areas of intervention/education required: 
o Low PEEP 
o Inappropriate (both too high or too low) tidal volume 
o Too low tidal volume 
o Too high tidal volume 
o Both low PEEP and inappropriate tidal volume 
• Using the entire data set, for tidal volumes from 400-600mL, we will plot the % of 
patients in whom that tidal volume satisfies LPV criteria.  This will inform any required 
change to the default tidal volume ventilator setting to maximize the number of patients 
satisfying LPV criteria if the tidal volume setting were left unchanged. 
• We will model improvements in LPV compliance if the default PEEP setting on 
anesthesia machines were changed from PEEP = off to PEEP = 5. 
Part II: Investigation of time trends to compliance 
 
A Shewhart control chart (p chart) of LPV compliance each month will be presented to 
investigate time trends of LPV compliance as correlated to the COVID pandemic, and to identify 
special-cause variation in LPV compliance throughout the pandemic.  The following rules to 
look for trends and shifts will be employed (5): 
• 1 point is outside the control limits. 
• 2 out of 3 consecutive points are more than 2 sigmas from the center line in the same 
direction. 
• 4 out of 5 consecutive points are more than 1 sigma from the center line in the same 
direction. 
• 8/9 points on the same size of the center line. 
• 6 consecutive points are steadily increasing or decreasing. 
• 14 consecutive points are alternating up and down. 
• 15 consecutive points are within 1 sigma of the center line. 
• 8 consecutive points on either side of the center line with not within 1 sigma. 
 
 

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Part III: Predictors of compliance to LPV 
• Our list of hypothesized predictors to LPV use include: 
o Age 
o sex 
o height 
o obesity (body mass index > 30) 
o elective/emergency 
o ASA Class 
o sepsis 
o functional health status 
o anesthesia duration 
o dyspnea 
o severe COPD 
o CHF 
o smoker 
o surgical specialty 
o ventilatory dependent 
o Pre-COVID era vs. COVID era 
o Night or weekend shift (1) 
o Hospital site 
o Use of neuromuscular blockade 
o Presence of epidural anesthesia 
o Inpatient vs. outpatients 
o Laparoscopic surgery 
o Transfusion of blood products 
o Dialysis 
o Ascites 
o NB: We considered before January 2020 as the pre-COVID era, and after June 
2020 as the COVID era.  The former cutoff was chosen as January 2020 
contained the first COVID case in BC.  The latter cutoff was chosen as it 
constitutes three months after March 2020, when the World Health Organization 
announced COVID’s pandemic status, and BC announced a state of emergency 
with the start of isolation policies.  We hypothesized that any impact of the 
COVID pandemic on LPV compliance patterns would unlikely to have occurred 
prior COVID’s arrival in BC, but would likely be in full swing three months after 
initiation of widespread social isolation, a very palpable event in society. 
o NB2: parameters such as oxygen saturation, end-tidal carbon dioxide, and peak 
inspiratory pressures may be affected by use of LPV, and therefore will not be 
included as candidate predictors due to high likelihood of reverse causation (2).  
Mode of ventilation has been shown to correlate with LPV compliance(2), but this 
is unfortunately not recorded in our dataset. 
• To screen for inclusion in the multivariate logistic regression model, the association of 
each hypothesized predictor to LPV use will be first explored using bivariate analyses.  

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Chi squared test will be used for categorical hypothesized predictors (or Fisher’s exact 
test if a covariate pattern results in < 5 cases).  Bivariate logistic regression (including 
only the predictor and LPV) will be used for continuous hypothesized predictors.   
• We will use a multivariate logistic regression model to determine the independent 
predictors of LPV use.  Predictors with a p < 0.2 in the bivariate analysis will be 
incorporated into model construction.  A conservatively high cut-off for the p-value was 
chosen to minimize type II error (exclusion of a predictor that would have become 
significant if adjusted for other predictors in the multivariate model).  For the same 
reason, we will not adjust the p-value for multiple comparisons during the predictor 
screening phase.  
• For face validity, we will include age, sex, height, obesity status, night or weekend shift, 
hospital site, laparoscopic surgery, and pre-COVID era vs. COVID era in the model 
regardless of bivariate analysis results. 
• We will add candidate predictors that passed the bivariate phase in a forward selection 
method, minimizing the Akaike Information Criterion (3), which rewards improved 
goodness of fit while simultaneously penalizes over-fitting.   
• Pairwise interaction among included terms will be investigated via adding an interaction 
term for each pair, and comparing the model with and without the interaction term via a 
likelihood ratio test.  A p < 0.05 will result in inclusion of that interaction term. As 
before, to minimize type II error, the p value will not be adjusted for multiple comparison 
at this stage. 
• Finally, the odds ratio of each predictor for LPV use in the final model will be presented 
with and without Bon Ferroni adjustment for multiple comparisons.  A separate result 
will be presented for interactions, if any are detected. 
Sensitivity analyses, subgroup analyses, and other supplemental analyses 
• Part I: none planned 
• Part II: 
o Subgroup analysis for: academic hospital sites, laparoscopic surgery. 
o Subgroup analysis for patients who would not have met LPV criteria if ventilated 
by default ventilator settings for tidal volume.  This analysis explores whether 
clinicians became more likely to adjust the ventilator settings intentionally to 
achieve LPV.   
• Part III: 
o We will adjust the date cut-offs for pre-COVID era vs. COVID era based on 
Shewhart chart results of Part II (i.e. the dates that the Shewhart chart indicated 
shifts in practice pattern) 
o Subgroup analysis for academic hospital sites only. 
 
 

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8. Ethical considerations 
This data set includes clinical and administrative data from all hospitals within the Fraser Health 
medical system using CPA. There will be no prospective recruitment or sampling of patients, and 
thus a waiver of consent will not be requested in accordance with article 3.7A of the Tri-Council 
Policy Statement: Ethical Conduct for Research Involving Humans. 
9. Data management plan 
Access to medical records (A.2) 
CPA is an AIMS database within FHA that stores details on intra-operative patient management. 
CPA is accessed through a dedicated subset of database views stored in a Microsoft SQL Server 
2012 controlled by the Fraser Health Authority. After REB approval, this data will be queried by 
Dr. Perseus Missirlis and data analysts within FHA. 
Type of data to be collected (A.3) 
From CPA, all data for patients that have undergone surgical procedures will be accessed. Data 
describing the previously mentioned monitoring data will be collected. All relevant NSQIP 
variables (as described above) will be collected. Due to the size of the data, NSQIP data may be 
provided as a database flat file. Only data linked to CPA based on the inclusion and exclusion 
criteria of this study this file will be used for the analysis and the flat file will be destroyed after 
data linking has occurred. 
Number of records required (A.4) 
All records will be included that meet the inclusion criteria in Section 7. We expect this to be 
approximately 100,000 cases. 
Personal information (A.5)  
The following identifiable information will be required during data collection and/or analysis:  
CPA database: MRN, PHN, sex, date of birth, date and time of surgery. 
NSQIP: MRN, PHN, age at time of surgery, sex, date of birth, date and time of surgery 
Risks associated with disclosure of data (A.7) 
Disclosure may expose patients and their families to risk of identifying them as having 
undergone a general anesthetic and surgery at an FHA hospital. 
 

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Protecting identity of participants (A.8)  
For analysis, patients will be identified using a unique study code (see below). Study data for 
analysis will be password protected and kept on M: drive in the FHA corporate intranet. 
Additionally, de-identified data will only be transferred using the Fraser Health secure file 
sharing tool ‘Cerberus’. 
Unique Study Code 
Participating patients will be assigned a random number once variables of interest have been 
collected and linked between the 2 datasets.  
UBC REBs require the use of a unique study code not derived from or related to the information 
about the individual i.e. name, SIN, PHN, hospital number, DOB, or unique characteristic. 
Access to study data (A.9)  
Only Dr. Perseus Missirlis and Fraser Health data analysts will have access to the key and raw 
data, which will be kept on password-protected file on an FHA computer. The data linking will 
be performed with the assistance of an FHA data analyst on an FHA corporate computer. Other 
investigators and the FHA statistician will only have access to the data file (on FHA or Cerberus 
secure drives), once it has been stripped of all identifying information for the purposes of 
statistical analysis and manuscript preparation. 
Data storage (A.10)  
Study documents containing identifiable information will be stored as files that are both 
password protected and encrypted on the principal investigator’s corporate FHA account. De-
identified information for the purposes of statistical analysis and manuscript preparation will be 
stored in FHA encrypted network drives. 
Confidentiality and security (A.11) 
Original files, which contain identifiers, will be password protected on an FHA computer only 
accessible by the principal investigator. Access to the de-identified data file will be limited to the 
research team by their FHA log-in credentials. 
End of study procedures (A.12) 
Research data will be kept for 5 years post-publication as outlined in the UBC study data 
retention guidelines. Any data required to be submitted along with a manuscript as a requirement 
for publication in a peer-reviewed scientific journal will be de-identified as per the de-
identification protocol outlined above. 

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Once the project’s data has reached its effective lifespan, hard copies will be securely destroyed 
using the on-site FHA approved secure document shredding service, while digital data will be 
deleted and wiped from the servers as per standard 85 of the UBC information security policy. 
Data Transfer (A.13)  
No data transfer anticipated outside of Fraser Health and UBC or its affiliated hospitals is 
planned. 
Data Linkage (A.14)  
Data linking and anonymization: Data linking between the NSQIP database and the CPA 
database will be done utilizing a similar technique used in a recently REB-approved study (H18-
03578).  In brief, the patients will be matched with their name, date of birth, medical record 
number (MRN, i.e. RC number), Personal Health Number (PHN), and/or date of surgery.  This 
will be performed by the principal investigator or a Fraser Health data analyst on a corporate 
Fraser Health computer only. Date of surgery, PHN, medical record number, sex, date of birth 
and date of surgery may be confirmed internally with data from the Fraser Health Authority 
Meditech or Surgical Information Systems to reconcile data linkage errors between CPA and 
NSQIP.  Once linked, each record’s identifying information (name, date of birth, MRN, PHN, 
date of surgery) will be deleted from the research dataset.  
 
10. References 
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7. 
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11. Appendix 
Total surgical monitoring time – defined as: 
1. Duration between 'Surgical Incision' and 'Surgery End' event texts (annotated by 
anesthesiologist in CPA) if: 
a. both event texts were logged 
b. duration between these 2 event texts is > 10 minutes (this is to omit those cases 
where anesthesiologist forgot to change the observed time when logging the 
events) 
c. surgical incision time in CPA is within 15 min of Meditech surgery start time 
2. If the above conditions are not met, then data will be extracted based on volatile 
anesthetic gas thresholds as surrogate start and end times for surgery. Data is extracted 
once the end tidal volatile gas % exceeds and then drop below the 0.3 MAC threshold of 
a 40 year old30: 
a. End-tidal sevoflurane % >= 0.5  
b. End-tidal desflurane % >= 2.0  
c. End-tidal isoflurane % >= 0.35