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Journal of Clinical Medicine logoLink to Journal of Clinical Medicine
. 2022 Nov 16;11(22):6778. doi: 10.3390/jcm11226778

Traumatic Proximal Femoral Fractures during COVID-19 Pandemic in the US: An ACS NSQIP® Analysis

Muhammad Umar Jawad 1, Connor M Delman 2, Sean T Campbell 2, Ellen P Fitzpatrick 2, Gillian L S Soles 2, Mark A Lee 2, R Lor Randall 2, Steven W Thorpe 2,*
Editors: Enrique Gómez-Barrena, Moshe Salai
PMCID: PMC9697726  PMID: 36431255

Abstract

In order to determine the impact of COVID-19 on the treatment and outcomes in patients with proximal femoral fracture’s (PFF), we analyzed a national US sample. This is a retrospective review of American College of Surgery’s (ACS) National Surgical Quality Improvement Program (NSQIP) for patients with proximal femoral fractures. A total of 26,830 and 26,300 patients sustaining PFF and undergoing surgical treatment were sampled during 2019 and 2020, respectively. On multivariable logistic regression, patients were less likely to have ‘presence of non-healing wound’ (p < 0.001), functional status ‘independent’ (p = 0.012), undergo surgical procedures of ‘hemiarthroplasty’(p = 0.002) and ‘ORIF IT, Peritroch, Subtroch with plates and screws’ (p < 0.001) and to be ‘alive at 30-days post-op’ (p = 0.001) in 2020 as compared to 2019. Patients were more likely to have a case status ‘emergent’, ‘loss of ≥10% body weight’, discharge destination of ‘home’ (p < 0.001 for each) or ‘leaving against medical advice’ (p = 0.026), postoperative ‘acute renal failure (ARF)’ (p = 0.011), ‘myocardial infarction (MI)’ (p = 0.006), ‘pulmonary embolism (PE)’ (p = 0.047), and ‘deep venous thrombosis (DVT)’ (p = 0.049) in 2020 as compared to 2019. Patients sustaining PFF and undergoing surgical treatment during pandemic year 2020 differed significantly in preoperative characteristics and 30-day postoperative complications when compared to patients from the previous year.

Keywords: ACS NSQIP, COVID-19, proximal femur fracture, US national data

1. Introduction

The first case of COVID-19 was diagnosed on 20 January 2020 in Washington State in the U.S. [1]. Shortly thereafter, the World Health Organization (WHO) declared COVID-19 a pandemic on 11 March 2020 [1]. The effect of COVID-19 pandemic on the health care system in the U.S. has been unprecedented. The Centers of Medicare and Medicaid Services (CMS) announced delays for all non-essential medical, surgical, and dental procedures during COVID-19 outbreak on 18 March 2020 at the White House Task Force press briefing [2].

Surgical treatment of a hip fracture is typically thought of as an urgent procedure. Delay in hip fracture care has been linked to increased morbidity and mortality [3,4,5]. However, providing timely care to patients with hip fractures during COVID-19 was fraught with multiple challenges for the strained U.S. healthcare system. One of the challenges was the diversion of logistic and personnel resources resulting in potential delays in surgery.

There have been multiple recent publications from around the world, attempting to explore the impact of COVID-19 pandemic on hip fracture care. These include the single center review of medical records [6,7,8,9,10], review of national claims databases [11,12], meta-analyses [13,14] and national database [15,16,17] consortium data reviews [18,19,20]. There have been multiple reports of higher early mortality and post op complications in hip fracture patients with COVID-19 infection [6,18,19,21,22].

Studies emanating from single centers suffer from inherent selection bias. Insurance claims data is limited, inconsistent and subject to interpretation [23]. Previous early reviews of national databases or consortium data lacked some of the key information such as potential delay in surgeries or a comparison group of patients from pre-COVID era [15,16,18,19].

The purpose of this study was to compare the pre-operative characteristics, 30-day postoperative complications and mortality for patients who were treated for a traumatic hip fracture in 2020 (during the COVID-19 pandemic) with those treated in 2019 (pre-COVID-19), using American College of Surgeon (ACS) National Surgical Quality Improvement Program (NSQIP). Secondarily, we sought to compare the time from hospital admission to surgical fixation between these groups.

2. Methods

This is a retrospective review of ACS NSQIP® proximal femoral fracture dataset from 2019 and 2020. The inclusion criteria have been described below under the case selection. Patient with missing information were excluded from the respective analysis. The study was deemed exempt from institutional review board (IRB) review.

2.1. ACS-NSQIP®

The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP)® [23] was used to extract the data for patients undergoing surgical treatment of proximal femur fractures in 2020. Data for patients with the same ICD 10 diagnostic codes from 2019 were used for comparison. The NSQIP database is a collection of patient records from over 680 participating hospitals across the United States [23]. Each institution assigns a trained data reviewer who randomly selects from a variety of surgical cases and uploads deidentified patient information and outcomes up to 30 days post-op onto a Health Insurance Portability and Accountability Act (HIPAA) compliant web-based platform [23]. The data is made available to investigators affiliated with the participating hospitals. NSQIP extracts the data directly from patient’s medical chart, and not insurance claims. The data is risk-adjusted and case-mix adjusted [23]. The data from the entire year was used to counter balance any effect of seasonal variation in incidence, morbidity and mortality of hip fractures [24,25,26].

2.2. Case Selection

Case data were extracted from the yearly (2019 and 2020) Participant User File (PUF) provided by ACS. International Classification of Disease, 10th Revision (ICD-10) diagnosis codes were used to identify and extract cases with proximal femoral fractures: femoral neck (S72.0), intertrochanteric (S72.1) and subtrochanteric (S72.2). A detailed breakdown of all the cases with respective ICD-10 codes is provided in the Supplementary Table S1.

2.3. Pre-Operative and Intra-Operative Characteristics (Table 1)

Table 1.

Cross Table Year of Diagnosis and Preoperative and Intraoperative Patient Characteristics with Traumatic Proximal Femoral Fractures.

Cross Table Year of Diagnosis and Preoperative and Intraoperative Patient Characteristics with Traumatic
Proximal Femoral Fractures
2019 2020
n Valid% of Total n Valid% of Total p-Value
Total Patients 26,830 100 26,300 100
Age
18–39 years 182 0.7 182 0.7
40–64 years 2661 9.9 2572 9.8
>65 years 23,987 89.4 23,546 89.5 0.853
Gender
Male 8684 32.4 8620 32.8
Female 18,145 67.6 17,677 67.2
Non-Bi 1 0 3 0 0.356
Race
White 17,426 91.2 16,003 89.8
African American 934 4.9 938 5.3
AIAN 108 0.6 99 0.6
API 641 3.4 766 4.3
Other 0 0 10 0.1 <0.001
Ethnicity
Non-Hispanic 17,802 93.2 16,973 93.4
Hispanic 1295 6.8 1206 6.6 0.57
Diabetes
Type 1 2155 8 2109 8
Type 2 2909 10.8 2916 11.1
Non-Diabetic 21,766 81.1 21,275 80.9 0.664
Smoking Status
Non-Smoker 23,418 87.3 22,937 87.2
Smoker 3412 12.7 3363 12.8 0.809
Dyspnea
At rest 241 0.9 221 0.8
At moderate exertion 2036 7.6 1904 7.2
No Dyspnea 24,553 91.5 24,175 91.9 0.231
Functional Status
Independent 20,885 78.5 20,118 77.2
Partially Dependent 4822 18.1 4986 19.1
Fully Dependent 888 3.3 967 3.7 <0.001
Ventilator Dependant
Yes 44 0.2 53 0.2
No 26,786 99.8 26,247 99.8 0.311
COPD
Yes 2802 10.4 2652 10.1
No 24,028 89.6 23,648 89.9 0.172
Ascites
Yes 71 0.3 73 0.3
No 26,759 99.7 26,227 99.7 0.774
CHF
Yes 1040 3.9 944 3.6
No 25,790 96.1 25,356 96.4 0.081
HTN
Yes 17,625 64.3 17,027 64.7
No 9565 35.7 9273 35.3 0.345
Disseminated Cancer
Yes 488 1.8 471 1.8
No 26,342 98.2 25,829 98.2 0.809
Presence of Wound
Yes 800 3 670 2.5
No 26,030 97 25,630 97.5 0.002
Steroid Use
Yes 1382 5.2 1287 4.9
No 25,448 94.8 25,013 95.1 0.174
≥10% loss of body weight
Yes 647 2.4 1054 4
No 26,183 97.6 25,246 96 <0.001
Bleeding Disorder
Yes 3935 14.7 3814 14.5
No 22,895 85.3 22,486 85.5 0.591
Pre-Operative Transfusion
Yes 895 3.3 951 3.6
No 25,935 96.7 25,349 96.4 0.078
Pre-Operative Sepsis
None 23,413 87.3 22,650 86.1
SIRS 3290 12.3 3511 13.3
Sepsis 116 0.4 126 0.5
Septic Shock 11 0 13 0 0.002
Emergent
Yes 8437 31.4 9017 34.3
No 18,393 68.6 17,283 65.7 <0.001
Pre-Operative Superficial Surgical Site Infection
Yes 5 0 2 0
No 26,825 100 26,298 100 0.268
Pre-Operative Deep Surgical Site Infection (Muscles/Fascia)
Yes 1 0 1 0
No 26,829 100 26,299 100 0.989
Pre-Operative Deep Organ Surgical Site Infection (Bone/ Joint)
Yes 3 0 5 0
No 26,827 100 26,295 100 0.462
Pneumonia
Yes 204 0.8 215 0.8
No 26,626 99.2 26,085 99.2 0.457
Pre-Operative Urinary Tract Infection
Yes 236 0.9 234 0.9
No 26,594 99.1 26,066 99.1 0.803
Wound Class
Clean 26,454 98.6 25,931 98.6
Clean/Contaminated 318 1.2 320 1.2
Contaminated 52 0.2 40 0.2
Dirty/Infected 6 0 9 0 0.551
ASA
Class I 253 0.9 229 0.9
Class II 4623 17.3 4280 16.3
Class III 16,503 61.6 16,247 61.9
Class IV 5348 20 5458 20.8
Class V 48 0.2 47 0.2 0.014
Anesthesia
General 19,095 71.2 17,867 67.9
Regional 5359 20 5800 22.1
Other 2373 8.8 2630 10 <0.001
Surgical Procedure
Hemiarthroplasty 2851 10.6 2527 9.6
Total Hip Arthroplasty 1561 5.8 1497 5.7
ORIF Neck or Prosthetic 7851 29.3 7855 29.9
ORIF IT, Peritroch, Subtroch with plates and screws 1862 6.9 1586 6
IM Nail for IT, Peritroch, Subtroch 11,772 43.9 11,883 45.2
Other 933 3.5 952 3.6 <0.001

Demographic characteristics including age, gender, race and ethnicity were extracted. Information regarding pre-existing medical conditions: diabetes (type 1, type 2 and non-diabetic), COPD (Chronic Obstructive Pulmonary Disease), ascites, CHF (Congestive Heart Failure), HTN (Hypertension), presence of non-healing wound, dyspnea, vent dependance, steroid use, loss of ≥10% of body weight, bleeding disorder, pre-operative transfusion, pneumonia, pre-operative UTI (Urinary tract infection) and pre-operative infection of surgical site (superficial, muscle fascia, bone/joint) were also extracted. In addition, NSQIP collects information regarding functional status of patients and smoking history. Functional status is categorized as ‘independent’, ‘partially dependent’ and ‘fully dependent’ based upon the ability of patient to perform activities of daily living (ADLs) such as clothing, bathing and toileting at their peak physical function during the 30 days prior to admission. Smoking history is defined as having smoked within a year prior to surgery.

The intraoperative characteristics analyzed included anesthesia (general, regional and other) and wound class (clean, clean/contaminated, contaminated, and dirty/infected). Information regarding ASA (American Society of Anesthesiologist) class was also extracted and presented in Table 1. NSQIP presents surgical procedures as CPT (Current Procedural Terminology) codes. Surgical procedures with frequency more than 1% have also been presented separately in Table 1. The category ‘others’ includes CPT codes with frequency less than 1%. A detailed account of all the CPT codes for the entire cohort is presented in Supplementary Table S2.

2.4. Thirty-Day Postoperative Characteristics and Mortality (Table 2)

Table 2.

Cross Table Year of Diagnosis and Postoperative Patient Characteristics with Traumatic Proximal Femoral Fractures.

Cross Table Year of Diagnosis and Postoperative Patient Characteristics with Traumatic Proximal Femoral Fractures
2019 2020
n Valid% of Total n Valid% of Total p-Value
Total Patients 26,830 100 26,300 100
Acute Renal Failure
Yes 188 0.7 240 0.9
No 26,642 99.3 26,060 99.1 0.006
Postoperative Superficial Surgical Site Infection
Yes 271 1 296 1.1
No 26,559 99 26,004 98.9 0.196
Postoperative Deep Surgical Site Infection (Muscles/Fascia)
Yes 35 0.1 41 0.2
No 26,795 99.9 26,529 99.8 0.438
Postoperative Deep Organ Surgical Site Infection (Bone/Joint)
Yes 68 0.3 80 0.3
No 26,762 99.7 26,220 99.7 0.267
Wound Dehiscence
Yes 28 0.1 25 0.1
No 26,802 99.9 26,275 99.9 0.734
Postoperative Pneumonia
Yes 990 3.7 1075 4.1
No 25,840 96.3 25,225 95.9 0.018
Reintubation
Yes 267 1 291 1.1
No 26,563 99 26,009 98.9 0.208
Postoperative Pulmonary Embolism
Yes 201 0.7 252 1
No 26,629 99.3 26,048 99 0.009
Postoperative Ventilator >48 h
Yes 131 0.5 124 0.5
No 26,699 99.5 26,176 99.5 0.78
Postoperative UTI
Yes 989 3.7 982 3.7
No 25,841 96.3 25,318 96.3 0.771
Postoperative CVA
Yes 203 0.8 187 0.7
No 26,627 99.2 26,113 99.3 0.538
Postoperative Cardiac Arrest
Yes 170 0.6 174 0.7
No 26,660 99.4 26,126 99.3 0.688
Postoperative Myocardial Infarction
Yes 509 1.9 605 2.3
No 26,321 98.1 25,695 97.7 0.001
Post-Operartive Bleeding Transfusion > 2
Yes 5212 19.4 5087 19.3
No 21,618 80.6 21,213 80.7 0.807
Postoperative DVT
Yes 234 0.9 279 1.1
No 26,596 99.1 26,021 98.9 0.026
Postoperative Sepsis
Yes 261 1 265 1
No 26,569 99 26,035 99 0.685
Postoperative Septic Shock
Yes 166 0.6 158 0.6
No 26,664 99.4 26,142 99.4 0.79
Return to Operating Room
Yes 627 2.3 615 2.3
No 26,203 97.7 25,685 97.7 0.991
Reoperation due to Hip Fracture
Yes 451 1.7 438 1.7
No 26,379 98.3 25,862 98.3 0.889
Unplanned Readmission
Yes 2018 7.5 2061 7.8
No 24,812 92.5 24,239 92.2 0.173
30-Day Mortality
Alive 25,487 95 24,811 94.3
Dead 1343 5 1489 5.7 0.003
Discharge Destination
Home 5644 21.4 7286 28.3
Facility 20,122 76.3 17,840 69.3
AMA 39 0.1 51 0.2
Expired 557 2.1 583 2.3 <0.001
Mean Standard Deviation Mean Standard Deviation
Length of total hospital stay 4.67 14.8 4.57 15.9 0.463
Total operation time (minutes) 66.68 39.4 67.35 38.9 0.047
Days from hospital admission to operation 1.15 3.1 1.16 3.3 0.935

A variety of 30-day postoperative characteristics are collected by NSQIP. These include acute renal failure, postoperative surgical site infection (superficial, muscle/fascia, bone/joint), wound dehiscence, pneumonia, reintubation, pulmonary embolism, ventilator >48 h, UTI, CVA, cardiac arrest, myocardial infarction, transfusion >2, DVT, sepsis, septic shock, return to operating room, readmission due to hip fracture and discharge destination. In addition, 30-day post-op mortality data is also presented in Table 2. In addition, continuous variables: length of total hospital stay, total operation time and time from admission to surgical fixation, have also been extracted and presented as mean and standard deviation (SD).

2.5. Statistical Analysis

The χ2 test of independence was used to compare the categorical variables. Two-sided independent sample t-test was used to compare the means of continuous variables. Logistic regression model was constructed to run a multivariable analysis with categorical pre-operative and postoperative variables achieving significance on univariable analysis.

The variable ‘Race’ was not included in our multivariable model. Inclusion of ‘Race’ in multivariable model resulted in unexpected singularities in the Hessian matrix.

3. Results

There were a total of 26,830 cases sampled for 2019 and 26,300 cases sampled for 2020 (Table 1).

3.1. Demographics (Table 1)

There was no significant change in age (p = 0.853) or gender (p = 0.356) distribution of patients undergoing proximal femoral fracture surgery before COVID-19 pandemic (2019) and during COVID-19 pandemic (2020). However, a statistically significant increase in racial distribution of Asian Pacific Islanders and a decrease in Caucasians was observed among patients with proximal femoral fractures in 2020 (p < 0.001).

3.2. Pre-Operative Factors (Table 1)

A statistically significant decrease was seen in the number of ‘independent’ patients from 2019 (78.5%) to 2020 (77.2%) (p < 0.001). Similarly, there was a decrease observed in the number of patients with chronic wounds from pre-COVID-19 (2019) to COVID-19 (2020) time period (p = 0.002). More than 10% loss of body weight was seen in only 2.4% of the patients during 2019 as compared to 4% of the patients during 2020 (p < 0.001). There were only 87.3% of patients with no signs of sepsis before the proximal femoral fracture surgery in 2019 as compared to 86.1% of patients in 2020 (p = 0.002). A statistically significantly higher number of cases were designated as emergent during 2020 (p < 0.001).

3.3. Intraoperative Factors

A higher ASA classification (p = 0.014) and a lower proportion of patients receiving general anesthesia (p < 0.001) was seen during COVID-19 pandemic (2020). A higher proportion of patients underwent surgical procedure ‘IM Nail placement’ during 2020 (45.2% vs. 43.9%). Additionally, surgical procedures ‘hemiarthroplasty’ (10.6% vs. 9.6%) and ‘ORIF intertrochanteric, peri-trochanteric, and sub-trochanteric with plates and screws’ (6.9% vs. 6%) were more commonly seen during 2019 (p < 0.001). There was an increased mean total operation time during 2020 (67.35) when compared to 2019 (66.68). This finding achieved border line statistical significance with p = 0.047 and is of limited clinical significance.

3.4. Postoperative Factors (Table 2)

A higher proportion of patients developed postoperative acute renal failure during the COVID-19 pandemic (0.9%) as compared to 2019 (0.7%). This finding achieved statistical significance with p-value = 0.006. A similar increase was seen in patients developing postoperative pneumonia (p = 0.018), DVT (p = 0.026), PE (p = 0.009) and acute myocardial infarction (MI) (p = 0.001). A higher proportion of patients was discharged home rather than another health facility during 2020 (28.3%) as compared to 2019 (21.4%) (p < 0.001). A higher 30-day post-op mortality of 5.7% was also observed during 2020 as compared to 5% during 2019 (p = 0.003).

3.5. Multivariable Analysis (Table 3)

Table 3.

Multivariable analysis and logistic regression.

Multivariable Analysis
Logistic Regression n OR 95% CI p-Value
Year of Diagnosis Dependent Variable
2019 26,075 Reference
2020 25,513
≥10% loss of body weight
Yes 1630 1.669 1.506–1.850 <0.001
No 49,958 Reference
Presence of wound
Yes 1404 0.832 0.747–0.927 <0.001
No 50,184 Reference
Pre-Operative Sepsis
None 44,721 0.927 0.391–2.198 0.863
SIRS 6614 1.027 0.433–2.436 0.953
Sepsis 232 1.022 0.415–2.517 0.962
Septic Shock 21 Reference
Emergent
Yes 16,630 1.1 1.059–1.143 <0.001
No 34,958 Reference
Functional Status
Independent 40,356 0.883 0.802–0.973 0.012
Partially Dependent 9440 0.954 0.861–1.056 0.362
Fully Dependent 1792 Reference
ASA
Class I 480 0.824 0.507–1.34 0.436
Class II 8797 0.946 0.602–1.487 0.809
Class III 31,983 1.076 0.686–1.688 0.751
Class IV 10,251 1.091 0.695–1.714 0.704
Class V 77 Reference
Anesthesia
General 36,123 3.501 0.391–31.372 0.263
Regional 10,764 3.948 0.44–35.389 0.22
Other 4703 Reference
Acute Renal Failure
Yes 413 1.292 1.061–1.1574 0.011
No 51,175 Reference
Postoperative Pulmonary Embolism
Yes 425 1.219 1.003–1.481 0.047
No 51,163 Reference
Postoperative Myocardial Infarction
Yes 1036 1.192 1.051–1.351 0.006
No 50,552 Reference
Postoperative DVT
Yes 495 1.192 1.051–1.351 0.049
No 51,093 Reference
30-Day Mortality
Alive 48,811 0.849 0.768–0.939 0.001
Dead 2777 Reference
Discharge Destination
Home 12,817 1.693 1.446–1.983 <0.001
Facility 37,569 1.089 0.934–1.27 0.276
AMA 90 1.658 1.061–2.59 0.026
Expired 1112 Reference
Surgical Procedure
Hemiarthroplasty 5161 0.843 0.757–0.939 0.002
Total Hip Arthroplasty 3014 0.889 0.791-1 0.049
ORIF Neck or Prosthetic 15,272 0.955 0.866–1.053 0.354
ORIF IT, Peritroch, Subtroch with plates and screws 3257 0.782 0.697–0.878 <0.001
IM Nail for IT, Peritroch, Subtroch 23,041 0.983 0.893–1.082 0.726
Other 1843 Reference

A multivariable logistic regression model of statistically significant categorical variables on univariable analysis is presented in Table 3. Pre-operative factors: ‘Emergent’ status of the surgery (p < 0.001), ≥10% loss of body weight (p < 0.001), and postoperative factors: acute renal failure (0.012), PE (0.043), MI (0.005), DVT (0.044), discharge destination ‘Home’ (p < 0.001) and leaving ‘AMA’ (p = 0.027) and higher rate of 30-day mortality (p = 0.001) were statistically significantly associated with the year 2020 (pandemic year). The presence of an open wound (p < 0.001), ‘independent’ functional status (p < 0.001) and surgical procedures ‘hemiarthroplasty’ (p = 0.002) and ‘ORIF intertrochanteric, peri-trochanteric, and sub-trochanteric with plates and screws’ (p < 0.001) were statistically significant associated with pre-COVID-19.

4. Discussion

This study utilized the ACS NSQIP PUF from year 2019 and 2020 to compare the demographics, preoperative, intraoperative, 30-day postoperative characteristics and 30-day post-op mortality for patients with proximal femoral fractures before and during the COVID-19 pandemic. To our knowledge, this is the first investigation of its kind, comparing a national sample of proximal femoral fracture patients before and during the pandemic. Our findings confirm higher 30-day post-op mortality during the pandemic year, a finding shared by others [6,7,10,18,19,21]. The current study highlights significant differences in patient characteristics and outcomes during the pandemic. The findings of this study are novel as this is the first study to report impact of COVID-19 on care of proximal femoral fractures sampled from the US national data. We believe that these findings will provide the fundamentals to institute policies and changes to equip and prepare the current health care system to face and tackle a similar challenge in future.

Using the logistic regression, we were able to ascertain independent preoperative and postoperative variables associated with the pandemic. Proximal femoral fracture surgery was more likely to be declared ‘Emergent’ status during the year 2020. Although this finding has not been previously reported in the literature, a case with an emergent status is more likely to be authorized, given the shortage and diversion of resources during COVID-19. A weight loss of ≥10% of body weight was also found to be independently statistically significant for the pandemic year, another finding unique to our analysis. Patients with proximal femoral fractures during pandemic were less likely to be functionally independent and less likely to have an open wound. Boukebous et al. presented an analysis of proximal femoral fracture patients treated at a level 1 trauma center in France during the pandemic [10]. They did not find any difference in IADL (Instrument of activities of daily living) between 2019 and 2020. However, IADL was an independent predictor of all-cause 30-day mortality in their analysis [10].

Patients undergoing surgery for proximal femoral fractures were more likely to develop acute renal failure during pandemic. Levitt et al. presented an analysis of N3C (National COVID-19 cohort collaborative) and found a statistically significant increase in acute kidney injury in COVID-19 positive patients [19]. Similar findings were also reported by Hall et al. [15]. In the current study, patients with proximal femoral fractures had a higher likelihood of DVT, PE and MI during the pandemic. A higher incidence of thromboembolic events has also been reported by others [11,15,19]. Galivanche et al. reported a higher incidence of myocardial infarction among COVID-19 positive patients undergoing surgical fixation [11]. Our investigation has also shown that patients were more likely to receive ‘hemiarthroplasty’ or ‘ORIF intertrochanteric, peri-trochanteric, and sub-trochanteric with plates and screws’ in 2019 on a multivariable analysis. This finding has not been previously reported in the literature. Our dataset lacked the details about surgical approach for hemiarthroplasty, however, a recent study emanating from Italy reported improved oxygenation in lateral decubitus position as compared to supine position [27].

Our investigation has shown an increase in proportion of Asian Pacific islanders and a decrease in proportion of Caucasians with proximal femoral fractures during 2020. Egol et al. also reported an observed increase in proportion of Asian Pacific islander patients with hip fractures as compared to the corresponding months of the previous year [6]. However, due to a smaller sample size, their finding was not statistically significant. A statistically significant increase in Asian Pacific Islander patients has not been previously reported in the literature. However, caution should be exercised in interpreting this finding. As mentioned earlier, NSQIP collects and reports a sample of cases from over 680 hospitals in the U.S. [23]. The sampling methods for data collection have been standardized [23]. In the past, others have utilized the NSQIP data to validate the demographic distribution in prospective clinical trials [28]. However, considering that NSQIP collects a sample of actual data, this finding needs additional validation. Such validation would be ideally obtained by using a national population-based registry to calculate the incidence of hip fracture during 2020 for different racial groups.

Limitation

The major limitation of our analysis is the lack of data regarding covid infection. ACS NSQIP® has yet to release the data regarding COVID-19 status. Thus, a direct comparison of covid positive patients with COVID-19 negative patients was not possible. We have only presented the data during the COVID-19 pandemic and compared it to the data before the pandemic. Another limitation of our study is a lack of survival analysis to determine the prognostic factors. COVID-19 positivity has been widely implicated as a significant prognostic factor in the literature [6,15,19,29]. An analysis of prognostic factors in the absence of COVID-19 status would be inherently biased. Thus, an analysis of prognostication has not been included.

5. Conclusions

In this retrospective NSQIP database study comparing hip fracture surgery patients prior to and during the COVID-19 pandemic, we identified an increased proportion of Asian Pacific islanders patients with hip fractures during the pandemic. Additionally, this study confirmed previously identified increased postoperative mortality among hip fracture patients during the pandemic [6,7,10,18,19,21]. Our study has also demonstrated that despite the stress of COVID-19 on the healthcare system, time from hospital admission to OR for proximal femur fractures did not increase. Surgical procedures of ‘hemiarthroplasty’ and ‘ORIF intertrochanteric, peri-trochanteric and sub-trochanteric with plates and screws’ were performed less often during the pandemic. The study sample is derived from more than 680 hospitals across the US and thus is representative of the national data.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm11226778/s1, Table S1: Frequency table Diagnoses, ICD-10 codes with description; Table S2: Surgical Procedure CPT Codes.

Author Contributions

Data curation, analysis, statistical analysis and initial write up: M.U.J. and C.M.D. Manuscript revision: S.T.C., E.P.F. and G.L.S.S. Conception and idea: M.A.L., R.L.R. and S.W.T. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Ethical review and approval were waived for this study due to retrospective analysis of database.

Informed Consent Statement

Not applicable.

Data Availability Statement

The source data is publicly available at ACS website: https://www.facs.org/quality-programs/data-and-registries/acs-nsqip/participant-use-data-file/ (accessed on 7 October 2022).

Conflicts of Interest

All of the authors confirm that there are no financial conflict of interest.

Funding Statement

This research received no external funding.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data Availability Statement

The source data is publicly available at ACS website: https://www.facs.org/quality-programs/data-and-registries/acs-nsqip/participant-use-data-file/ (accessed on 7 October 2022).


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