Association of Anesthesia Technique With Morbidity and Mortality in Patients With COVID-19 and Surgery for Hip Fracture: a Retrospective Population Cohort Study
Conditions: Hip Fractures, COVID-19, General Anesthesia, Spinal Anesthesia
Sponsor: University of British Columbia

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Association of anesthesia technique with morbidity and mortality in patients with 
COVID-19 and surgery for hip fracture: a retrospective population cohort study 
 
Protocol  
 
 
Nilufer Nourouzpour MD MSc1,2, Parker Jobin MD PhD1, Christopher Prabhakar MD 
FRCPC,2,3 Tim Ting Han Jen MD FRCPC,2,3 Alana M. Flexman MD FRCPC,2,3 Jonathan 
Bailey MD MSc FRCPC4, Jason Sutherland5, Chun-man (Germaine) Ho BSc 1,  Janny Xue 
Chen Ke MD FRCPC2,3,4 
1 Faculty of Medicine, University of British Columbia, Vancouver, Canada 
2 Department of Anesthesiology, Pharmacology & Therapeutics, University of British Colum-
bia, Vancouver, Canada 
3 Department of Anesthesia, St. Paul's Hospital, Providence Health Care, Vancouver, Canada 
Columbia, Vancouver, Canada 
4 Department of Anesthesiology, Pain Management, and Perioperative Medicine, Dalhousie 
University, Halifax, Canada 
5 School of Population and Public Health, University of British Columbia, Vancouver, Can-
ada 
 
*Corresponding author:  
Janny Xue Chen Ke, E-mail: jannyke@alumni.ubc.ca 
 
 
 

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Association of anesthesia technique with morbidity and mortality in patients with 
COVID-19 and surgery for hip fracture: a retrospective population cohort study 
 
Introduction  
 
Background 
Patients with hip fracture have poor outcomes (1, 2), attributed to risk factors that in-
clude advanced age and higher rates of underlying chronic comorbidities (3). COVID-19 in-
fection is an independent risk factor for increased mortality in hip fracture patients in the peri-
operative period (4–11). A recent meta-analysis demonstrates COVID-19 infection is associ-
ated with higher than seven-fold increase in risk of mortality (12). Recommended management 
of hip fracture includes timely surgical repair, multimodal pain control, and multidisciplinary 
follow-up, to facilitate return to mobility and independent function (13, 14). 
Anesthesia for hip fracture surgery can be achieved by either general anesthesia (GA) 
or spinal anesthesia (SA). The potential advantages of SA include opioid-sparing effects, 
lessened impacts on the respiratory and gastrointestinal systems, and reduction in rates of ad-
verse outcomes such as pneumonia, mechanical ventilation, intensive care unit (ICU) admis-
sion, venous thromboembolism (VTE), myocardial infarction (MI), stroke, transfusion, read-
mission, and prolonged postoperative length of stay (15, 16). However, a recent randomized 
control trial found no difference between SA and GA for older adults undergoing hip fracture 
surgery for the primary outcome of survival and recovery of ambulation at 60 days (17). 
While emerging evidence shows COVID-19 infection increases mortality after hip 
surgery, there is a lack of research examining whether the choice of anesthetic technique 
modifies the postoperative mortality and morbidity of hip fracture patients with COVID-19 
infection. This is particularly important due to the high mortality (35% in COVID-positive 
patients, vs. 2% in patients without COVID) (12), with the potential for SA to modify this 
risk by circumventing the need for airway interventions. SA may also offer superiority over 
general anesthesia for limiting aerosol generation and exposure of operating room staff (18) 
during the pandemic. While SA may reduce the risk of pulmonary morbidity by reducing the 
need for airway interventions, its motor block on accessory muscles and the need for sedation 
may adversely impact ventilation.  
In this study, our goal is to evaluate the adjusted association between anesthesia tech-
nique and mortality and morbidity after hip fracture surgery for patients who tested positive 
for COVID-19. Our primary objective is to determine for patients undergoing hip surgery 
with COVID-19 infection, whether SA, as compared to GA, is associated with a lower rate of 
mortality 30 days postoperatively. Our secondary objective is to determine whether SA, as 
compared to GA, is associated with a lower rate of morbidity 30 days postoperatively. To 
provide context for interpretation, we will describe the epidemiology of the following rates 
during versus before the 2020 COVID-19 pandemic (January to December 2021, compared 
to 2017 to 2019): 1) SA versus GA utilization for hip fracture surgery, and 2) mortality and 
morbidity for hip surgery patients without COVID-19 infection. Finally, we will quantify the 
mortality and morbidity for patients with versus without COVID-19 infection undergoing hip 
fracture surgery, stratified by SA and GA.   
 
 
 

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Methods 
 
Study Design 
Approval will be gained from the University of British Columbia Providence Health Care Re-
search Ethics Board. The requirement for written informed consent will be waived for use of 
deidentified data. Patient information will be obtained for the retrospective cohort analysis us-
ing the NSQIP® (general dataset linked with the Hip Fracture Procedure Targeted Dataset), a 
prospectively-collected multicentre dataset with more than 150 clinical variables within 30 
days after surgery (19). The study will be pre-registered prior to data analysis (Clinicaltri-
als.gov) and reported according to The Strengthening the Reporting of Observational Studies 
in Epidemiology (STROBE) guidelines. 
 
Setting 
The setting of this study will be patient data obtained from the multicentre generated NSQIP 
Hip Fracture Procedure Targeted Dataset. The period of patient data obtained will include from 
January 2017 through December 2021. We will omit the data from January 2020 to December 
2020 given there was no reporting of COVID status during this period.  Data will only be 
obtained from patients undergoing hip surgery with mortality and morbidity gathered for 30 
days postoperatively. 
 
Participants 
Inclusion 
The study will include all patients 19 years or older who are sampled in the NSQIP Hip Fracture 
Procedure Targeted Dataset from January 2017 through December 2019 and January to De-
cember 2021 undergoing surgical fixation of hip fractures using either general and/or spinal 
anesthesia. In case of reduced Procedure-Targeted data collection during the COVID-19 pan-
demic, we will also create a total open hip fracture cohort using relevant Current Procedural 
Terminology codes (27244, 27245, 27269, 27236, or 27248) (20).  
 
Exclusion  
Patients with the primary or secondary anesthetic technique listed as local anesthesia alone, 
local anesthesia with intravenous sedation, epidural, and those with no reported anesthesia 
technique will be excluded. We will further exclude patients with American Society of Anes-
thesiologists (ASA) Physical Status (PS) V (defined as “5-Moribund”), and patients with ven-
tilator-dependence preoperatively. Patients with platelet counts < 80,000/mm3 within 90 days 
before surgery (21), INR  1.5, or PTT  > 35 seconds were also excluded for the likelihood of 
these patients being considered ineligible for SA based on previous guidelines (22, 23).  
 
COVID-19 cohorts  
The study will be divided into three cohorts: those undergoing hip surgery 1) without COVID-
19 infection January to December 2021, 2) with COVID-19 infection January to December 
2021, and 3) pre-pandemic from January 2017 to December 2019.  
Due to the variable duration of asymptomatic period that can precede symptoms and 
diagnosis, COVID-19 infection status will be classified as follows. In our primary analysis, 
COVID-negative patients will be defined as rows 1 in Table 1, and COVID-positive patients 
will be defined as row 4. In NSQIP, preoperative COVID status denotes within 14 days before 
surgery, and patients with preoperative COVID are always coded “No” for postoperative 
COVID. NSQIP does not have previous history of COVID prior to 14 days, which is a major 
limitation given the increased mortality of patients with recent COVID undergoing surgery 
(24).  

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As patients with postoperative COVID-positive status are difficult to interpret due to 
variable incubation period and the possibility of COVID-19 contraction while in hospital post-
operatively, we will perform sensitivity analysis using alternative definitions for the COVID-
positive cohort, including 1) rows 2, 4, and 6 (i.e. laboratory confirmed preoperatively or post-
operatively), 2) rows 4 and 6 (i.e. laboratory confirmed or symptomatic preoperatively), and 3) 
rows 2, 3, 4, 5, and 6 (i.e. suspected and laboratory confirmed anytime preoperatively or post-
operatively).  
 
Table 1 
Row #  
NSQIP classification  
 
Preoperative COVID 
(within 14 days before surgery) 
Postoperative COVID 
1 
No  
No  
2 
No  
Yes – lab confirmed 
3 
No  
Yes – suspected 
4 
Yes – lab confirmed  
No  
5 
Yes – suspected* 
No 
6 
Yes – suspected 
Yes – lab confirmed 
 
 
 
 
*Suspected denotes COVID-19 infection suspected by patient preoperative pneumonia or 
dyspnea 
 
Outcomes 
All outcomes (please see Appendix 1 for detailed definitions) will be measured within 30 days 
postoperatively, with the exception of length of stay (LOS) which was the total number of days 
from the day of operation to the day of discharge from hospital (19). 
 
Primary outcomes 
The primary outcome is all-cause 30-day mortality following hip fracture surgery.  
 
Secondary outcomes 
Secondary outcomes are MI, stoke or cerebrovascular accident (CVA), postoperative delirium, 
pneumonia, acute renal failure, transfusion, being on ventilator postoperatively (>48 hours), 
being still in hospital >30 days, length of stay (LOS), discharge destination as home versus not 
home, hospital readmission, and unplanned reoperation. There will be three composite out-
comes: 1) venous thromboembolism (VTE), defined as pulmonary embolism or deep venous 
thrombosis, 2) sepsis, defined as sepsis or septic shock, and 3) any complication listed above 
or death. The definitions of secondary outcomes collected from NSQIP variables are included 
in Appendix 1 (Data Extraction Form). 
 
Exposure 

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The exposure to either SA or GA will be modelled as a binary variable (please see Appendix 
1 for detailed definitions). SA will be defined as having either spinal or managed anesthesia 
care (MAC) documented the primary anesthesia technique, without GA secondary anesthesia 
technique. In NSQIP, regional anesthesia with MAC would be coded as MAC for the principal 
technique. Since it is highly unlikely that patients can tolerate hip fracture repair under sedation 
alone, MAC is counted as SA for the analysis. Those receiving both SA and GA (presumably 
either failed spinal and/or conversion to GA for other purposes) will be excluded from the 
primary analysis, but included in a priori sensitivity analysis with cohort characteristics dis-
played as a third comparison group, and analyzed as part of the GA group for the multivariable 
logistic regression.  
 
Confounders 
We selected an a priori list of potential confounders to adjust for the choice of anesthetic tech-
nique, based on availability within NSQIP, literature, clinical experience, and consensus within 
our multidisciplinary team. The confounders are: age, sex, race, body mass index (BMI) (cal-
culated from height and weight in NSQIP), American Society of Anesthesiologists (ASA) 
Physical Status (PS), smoker within one year preoperatively, severe obstructive chronic pul-
monary disease (COPD), dyspnea, coagulopathy (bleeding disorders), congestive heart failure 
(CHF), hypertension on medications, preoperative renal failure (acute renal failure, dialysis, 
and/or GFR < 60 mL/min/1.73 m2 based on preoperative creatinine) (25), diabetes, preopera-
tive functional status, preoperative delirium or dementia, systemic sepsis, total operating time, 
and days from hospital admission to operation. The definitions of potential confounding vari-
ables collected from NSQIP variables are included in Appendix 1 (Data Extraction Form). 
Since some of the potential confounders may provide similar information, collinearity will be 
assessed and variables will be combined or eliminated as described in the statistical analysis 
plan below.  
We acknowledge that NSQIP is missing several important confounders, particularly in 
terms of having little clinical information indicating the severity of COVID infection, such as 
the extent of oxygen requirements or admission to higher monitoring settings. In NSQIP, “a 
patient who is prescribed supplemental oxygen and utilizes it on a regular basis would be as-
signed as dyspnea upon moderate exertion unless there is documentation of dyspnea at rest”.  
 
 
Statistical Analysis  
1. Preprocessing  
1. Convert age from the NSQIP character variable to continuous variable, with 
Age “90+” recoded as 90  
2. Standardizing the coding of missing values: missing coded in NSQIP as “-99”, 
“unknown”, “None Assigned”, or “NULL”. Each variable will be individually 
examined to ensure that the missing value is correctly standardized. 
3. Missing values  
· Patients with missing data on key variables will be excluded as de-
scribed in the inclusion/exclusion criteria. 
· For confounders in modeling, if 
1. >10% missing then exclude the confounder from the model 
2. <1% missing then delete case (complete case analysis) 
3. >=1% and <10% missing then multiple imputation  

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· The % of missing data for height and weight will be examined in co-
hort characteristics. If <1% is missing, then BMI will be calculated us-
ing these variables.   
4. Check for collinearity with variance inflation factor and correlation matrix: if 
present, combine information from collinear variables if feasible (e.g. new 
variables is “yes” if “yes” in any of the variables); if not, eliminate the varia-
ble that has more missing values or would be less accurately ascertained  
2. Descriptive statistics  
1. Cohort characteristics (perioperative and morbidity/mortality outcomes), in-
cluding by 1) COVID-positive patients by SA vs. GA, 2) COVID-negative pa-
tients January-December 2021 vs. 2017-2019, 3) COVID-positive vs. negative 
in January-December 2021 stratified by anesthesia technique, and 4) utiliza-
tion of SA vs. GA in 2021 (January-December) vs 2017-2019. Continuous 
variables will be presented as mean (standard deviation) and median (IQR) for 
parametric and nonparametric data, respectively. Categorical variables will be 
presented as frequency (%). For comparison amongst groups, ANOVA will be 
used for parametric data, Kruskal-Wallis for nonparametric data, and Chi-
square for categorical data. Standardized mean difference will be presented.  
3. Multivariable logistic regression 
1. To assess the association amongst anesthesia type and mortality in COVID-
positive patients, a multivariable logistic regression will be performed using a 
priori exposure (SA vs. GA) and confounder variables (above) for the primary 
outcome of mortality. The same independent variables will be used in the mul-
tivariable logistic regressions for secondary outcomes (linear regression with 
log transformation for the outcome of length of stay).  
4. Sensitivity and exploratory analysis include 
1. We will define the COVID-positive cohort using the additional definitions dis-
cussed above per Table 1.  
2. Cohort characteristics of the GA + SA group will be presented, and compared 
to the SA and GA groups. If this group consists of  >1% of the respective co-
hort, the multivariable logistic regression for mortality will be repeated with 
GA+SA included as part of the GA group for the multivariable logistic regres-
sion.   
3. Repeat the primary modeling with the cohort after excluding patients with age 
“90” (all patients aged 90 and above are coded as 90+ in NSQIP, thus poten-
tial residual confounding) 
4. If there is sufficient sample size, subgroup analysis by sex (male only, female 
only) and age (<65, >=65 year old) may be performed. 
5. The degree of unmeasured confounding will be assessed using E-value and the 
Durbin-Wu-Hausman test.  
6. Adjustment for multiple testing for secondary outcomes, including Bonferroni, 
False Discovery Rate, and Holm's method 
5. Sample size calculation  
1. There are approximately 10,000 patients in the NSQIP Hip fracture dataset per 
year. The prevalence of COVID-19 in the hip fracture patient population was 
10% (thus an estimated sample size of approximately 1000 patients) 
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7495188), with a mortality 

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of 36% in this population. The pre-pandemic SA:GA utilization ratio was ap-
proximately 1:4 (20). With 80% power and 0.05 alpha, the sample size re-
quired to detect a 10% absolute decrease in mortality rate with the SA expo-
sure (from 36% to 26%) is 1064, and 525 for 14% absolute decrease in mortal-
ity rate, as calculated by the Sample Size Calculator (https://clin-
calc.com/stats/samplesize.aspx). 
2. For population cohort studies where logistic regression is used, the sample 
size recommended is at least 500 patients, or 100 + 50i (i=number of inde-
pendent variables in the final model) (26). With an estimated sample size of 
1000 patients, 18 independent variables can be included.  
3. Sample size calculated based on R-squared (variance explained by the multi-
variable logistic regression) and mortality difference between SA vs. GA (Ta-
ble 2). Assumptions are: alpha = 0.05, baseline mortality rate 36%, ratio of 
SA:GA of 1:4. For example, with approximately 500-1000 patients from 
NSQIP, the study would likely be able to detect a 12 to 14% mortality differ-
ence depending on the R-squared.  
 
 
Table 2. Sample size calculation for logistic regression using G*Power software.  
 
Detectable difference in mortality 
Amount of R2 
accounted for by 
covariates 
8% 
10% 
12% 
14% 
0.1 
1820 
1139 
771 
552 
0.2 
2048 
1281 
868 
621 
0.3 
2340 
1464 
992 
709 
0.4 
2730 
1708 
1157 
827 
 
 
 
1. 
 
Data will be analyzed using R. Significance level will be set at P <0.05. 
 
 

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List of variables in cohort characteristics include but are not limited to (Please see Ap-
pendix 1 for details) 
Patient characteristics: Age, sex, race, body mass index (BMI) (calculated from height and 
weight in NSQIP), American Society of Anesthesiologists (ASA) Physical Status (PS), 
smoker within one year preoperatively, severe obstructive chronic pulmonary disease 
(COPD), dyspnea, coagulopathy, congestive heart failure (CHF), hypertension on medica-
tions, preoperative renal failure (acute renal failure, dialysis, and/or GFR < 60 mL/min/1.73 
m2 based on preoperative creatinine) (25), diabetes, ascites, preoperative functional status, 
preoperative dementia/delirium, systemic sepsis, preoperative use of mobility aid, and pre-
operative pressure sore.  
 
Surgical factors: Total operating time, year of surgery, hip fracture type, pathological frac-
ture, and days from hospital admission to operation. 
 
Systems factors: Medical co-management, and participation in a standardized hip fracture 
care program. 
 
Postoperative variables:  
Mortality 
Myocardial infarction  
Stroke/CVA 
Postoperative delirium 
VTE (composite: pulmonary embolism, DVT) 
Pneumonia 
On ventilator >48h postoperatively 
Acute renal failure  
Sepsis or septic shock (composite of systemic sepsis and septic shock) 
Transfusion (packed red blood cell (PRBC) within the first 72 hours of surgery start time) 
Readmission: also present % unplanned  
Unplanned reoperation  
Still in Hospital > 30 Days 
Non-home discharge 
Weight bearing as tolerated (WBAT) on postoperative day (POD) #1  
Wound issues (composite: superficial and deep infection, wound disruption)  
New postoperative pressure sore 
Urinary tract infection  
Cardiac Arrest Requiring cardiopulmonary resuscitation (CPR) 
Postoperative use of mobility aid 
 
 
Limitations 
Due to the retrospective nature of this study, the main limitations include confounding by in-
dication and misclassification. Confounding by indication may be mitigated using multivaria-
ble adjustment of potential confounders. Misclassification of COVID is possible due to the 
limitations of testing and variations in disease presentation. 
 

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