ABSTRACT
Objective
The modified 5‐item frailty index (mFI‐5) is a comorbidity‐based risk stratification tool previously validated in patients undergoing facial plastic and reconstructive surgery. This retrospective US hospital database study investigates the association between mFI‐5 score and postoperative complications following zygomaticomaxillary complex (ZMC) fracture repair.
Methods
The National Surgical Quality Improvement Program was queried for patients undergoing ZMC fracture repair between 2005 and 2020. The mFI‐5 score was calculated by assigning one point for each of the following comorbidities: diabetes mellitus, hypertension, congestive heart failure, chronic obstructive pulmonary disease, and dependent functional status. Patients were stratified by mFI‐5 score. Univariate and multivariable analyses were performed to assess associations between frailty and postoperative complications.
Results
A total of 2260 patients were included and stratified by mFI‐5 scores of 0 (n = 1890), 1 (n = 285), and 2 or greater (n = 85). Higher mFI‐5 scores were associated with male sex, older age, higher American Society of Anesthesiologists classification, inpatient status, obesity, weight loss, bleeding disorders, and dependent functional status. Higher mFI‐5 was associated with increased rates of surgical complications, urinary tract infections, prolonged ventilation over 48 h, pneumonia, cumulative morbidity, and cumulative medical complications. On multivariable analysis, mFI‐5 scores of 1 (p = 0.027) and 2 or greater (p = 0.049) were independently associated with cumulative morbidity, and a score of 1 was associated with surgical complications (p = 0.022).
Conclusion
The mFI‐5 is associated with cumulative morbidity and surgical complications following ZMC fracture repair. Assessment of frailty may improve perioperative risk stratification in this population.
Level of Evidence
4.
Keywords: facial trauma, frailty, risk stratification, zygomaticomaxillary complex
1. Introduction
Zygomaticomaxillary complex (ZMC) fractures are a common fracture pattern accounting for an estimated 31% of maxillofacial fractures [1, 2]. These injuries primarily result from motor vehicle accidents, assaults, falls, and sports injuries [3]. Clinical features can include a variety of functional and cosmetic defects, including diplopia, enophthalmos, trismus, and cosmetic deformity [4]. Nondisplaced fractures often receive nonsurgical management, while displaced or comminuted fractures require surgical intervention [5].
The 5‐factor Modified Frailty Index (mFI‐5) is a validated tool for predicting postoperative complications for patients undergoing various surgical procedures [6, 7, 8]. The mFI‐5 assesses five comorbidities: diabetes, chronic obstructive pulmonary disease, congestive heart failure, hypertension requiring medication, and nonindependent functional status [9]. Prior studies have demonstrated that mFI‐5 is an independent predictor of adverse outcomes in facial fracture repair, geriatric ankle fractures, hip arthroplasty for femoral neck fractures, and geriatric hip fractures [9, 10, 11, 12]. Higher mFI‐5 scores are associated with increased risk of complications, readmissions, extended hospital stays, and mortality [10, 11, 12].
Despite the increasing emphasis on frailty in surgical literature, there is a notable gap in understanding how frailty influences outcomes in patients with ZMC fractures. There are shifting mechanisms of ZMC fracture, with falls increasing as a mechanism, especially in the elderly [13, 14, 15, 16]. Therefore, exploring the relationship between frailty and ZMC fracture outcomes is timely and essential. This study aims to address this gap by examining the impact of frailty, as measured by the mFI, on the management and postoperative course of patients with ZMC fractures.
2. Materials and Methods
The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database was retrospectively queried to identify patients undergoing zygomaticomaxillary complex (ZMC) fracture repair between 2005 and 2020. Cases were selected using Current Procedural Terminology (CPT) codes 21355, 21356, 21360, 21365, and 21366 [17]. Exclusion criteria included patients with missing age or incomplete comorbidity data required for frailty calculation (Figure 1).
FIGURE 1.
Inclusion and exclusion criteria for ZMC fracture cohort selection. Patients were screened and excluded based on preoperative characteristics, data completeness, and relevant comorbidities.
The modified 5‐item frailty index (mFI‐5) was calculated by assigning one point for the presence of each of the following comorbidities: diabetes mellitus, hypertension requiring medication, chronic obstructive pulmonary disease, congestive heart failure, and dependent functional status [18]. Patients were stratified into three groups according to their mFI‐5 score: 0, 1, and ≥ 2.
Demographic variables included age, sex, race, and inpatient versus outpatient status. Additional comorbidities of interest collected included obesity, smoking status, dyspnea, weight loss, bleeding disorders, and systemic sepsis. The American Society of Anesthesiologists (ASA) physical classification was also recorded.
Perioperative outcomes were assessed within 30 days postoperatively. Surgical complications included superficial or deep surgical site infection, organ space infection, wound disruption, postoperative bleeding, or graft failure. Medical complications included pneumonia, unplanned reintubation, pulmonary embolism, mechanical ventilation > 48 h, renal insufficiency, acute renal failure, urinary tract infection, stroke, myocardial infarction, deep vein thrombosis, sepsis, septic shock, or coma lasting > 24 h. Composite endpoints were defined as: (1) any complication (surgical or medical), (2) any surgical complication, and (3) any medical complication [19]. Readmissions, unplanned readmissions, reoperations, and mortality were also recorded.
Statistical analysis was performed using SPSS Version 29.0 (IBM Corp). Fisher's exact test and Pearson χ 2 were used for categorical comparisons. Multivariable logistic regression was performed to assess the independent effect of mFI‐5 on perioperative complications, controlling for demographic and comorbidity covariates. Covariates were evaluated based on clinical relevance and their associations with cumulative postoperative morbidity in preliminary univariate logistic regression analyses. Age cohort and inpatient status were selected for inclusion in multivariable logistic regression models. ASA classification was not included in regression modeling due to its correlation with mFI‐5, in order to avoid multicollinearity [20]. An α‐level of p < 0.05 was considered statistically significant.
3. Results
A total of 2260 patients undergoing ZMC fracture repair were identified. Patients were stratified into mFI‐5 = 0 (n = 1890, 83.6%), mFI‐5 = 1 (n = 285, 12.6%), and mFI‐5 ≥ 2 (n = 85, 3.8%). Higher mFI‐5 score cohorts were significantly older (p < 0.001) and demonstrated higher ASA classification (p < 0.001). Male patients predominated in all groups, though the proportions differed significantly (78.9% vs. 72.2% vs. 81.2%, p = 0.031). Race distribution also varied between cohorts (p = 0.003). Inpatient status was more common among patients with higher mFI‐5 scores (21.9% vs. 30.5% vs. 37.6%, p < 0.001) (Table 1).
TABLE 1.
Demographic characteristics and comorbidities across mFI‐5 frailty cohorts (n = 2260).
| mFI‐5 Score 0 (n = 1890) | mFI‐5 Score 1 (n = 285) | mFI‐5 Score ≥ 2 (n = 85) | p | ||
|---|---|---|---|---|---|
| Age cohorts in years, N (%) | < 0.001 | ||||
| ≤ 40 | 1191 (63.1%) | 48 (17.0%) | 1 (1.2%) | ||
| 41–60 | 576 (30.5%) | 134 (47.5%) | 37 (44.0%) | ||
| 61–80 | 109 (5.8%) | 83 (29.4%) | 36 (42.9%) | ||
| ≥ 81 | 10 (0.5%) | 17 (6.0%) | 10 (11.9%) | ||
| Sex, N (%) | 0.031 | ||||
| Female | 399 (21.1%) | 79 (27.8%) | 16 (18.8%) | ||
| Male | 1490 (78.9%) | 205 (72.2%) | 69 (81.2%) | ||
| Inpatient status | < 0.001 | ||||
| Outpatient | 1477 (78.1%) | 198 (69.5%) | 53 (62.4%) | ||
| Inpatient | 413 (21.9%) | 87 (30.5%) | 32 (37.6%) | ||
| Race cohorts, N (%) | 0.003 | ||||
| White | 1171 (62.4%) | 184 (65.0%) | 53 (62.4%) | ||
| Black | 226 (12.0%) | 50 (17.7%) | 15 (17.6%) | ||
| Other | 98 (5.2%) | 17 (6.0%) | 5 (5.9%) | ||
| Unknown | 383 (20.4%) | 32 (11.3%) | 12 (14.1%) | ||
| ASA class, N (%) | < 0.001 | ||||
| I | 593 (31.4%) | 5 (1.8%) | 0 (0.0%) | ||
| II | 1098 (58.2%) | 162 (56.8%) | 13 (15.3%) | ||
| III | 189 (10.0%) | 112 (39.3%) | 62 (72.9%) | ||
| IV | 7 (0.4%) | 6 (2.1%) | 10 (11.8%) | ||
| Comorbidities, N (%) | |||||
| Obesity | 241 (13.3%) | 62 (22.4%) | 24 (29.6%) | < 0.001 | |
| Smoking | 751 (39.7%) | 102 (35.8%) | 27 (31.8%) | 0.171 | |
| Dyspnea | 9 (0.5%) | 7 (2.5%) | 7 (8.2%) | < 0.001 | |
| COPD | 0 (0.0%) | 14 (4.9%) | 20 (23.5%) | < 0.001 | |
| Diabetes mellitus | 0 (0.0%) | 37 (13.0%) | 54 (63.5%) | < 0.001 | |
| Hypertension | 0 (0.0%) | 223 (78.2%) | 85 (100.0%) | < 0.001 | |
| Weight loss | 4 (0.2%) | 2 (0.7%) | 3 (3.5%) | < 0.001 | |
| Bleeding disorder | 7 (0.4%) | 11 (3.9%) | 5 (5.9%) | < 0.001 | |
| Systemic sepsis | 23 (1.2%) | 3 (1.1%) | 2 (2.4%) | 0.588 | |
| Emergency surgery | 91 (4.8%) | 7 (2.5%) | 4 (4.7%) | 0.201 | |
| Dependent functional status | 0 (0.0%) | 11 (3.9%) | 10 (11.8%) | < 0.001 | |
Note: Totals used in calculating percentages excluded missing cases for each variable. Bold values indicate a significant p value < 0.05.
Abbreviations: ASA, American Society of Anesthesiologists; mFI‐5, Modified frailty index‐5.
Comorbidity burden increased with frailty score. Patients with higher mFI‐5 demonstrated greater prevalence of obesity (13.3% vs. 22.4% vs. 29.6%, p < 0.001), dyspnea (0.5% vs. 2.5% vs. 8.2%, p < 0.001), COPD (0% vs. 4.9% vs. 23.5%, p < 0.001), diabetes mellitus (0% vs. 13.0% vs. 63.5%, p < 0.001), hypertension (0% vs. 78.2% vs. 100%, p < 0.001), and dependent functional status (0% vs. 3.9% vs. 11.8%, p < 0.001) across mFI‐5 scores of 0, 1, and 2+, respectively.
Perioperative complication rates are listed in Table 2. Univariate analysis showed increasing frailty score to be associated with extended length of stay (1.3% vs. 4.6% vs. 12.9%, p < 0.001), pneumonia (0.1% vs. 0.0% vs. 2.4%, p = 0.001), urinary tract infection (0.1% vs. 0.3% vs. 2.4%, p < 0.001), and prolonged ventilation > 48 h (0.1% vs. 0.3% vs. 1.2%, p = 0.004). Overall morbidity also varied significantly between cohorts (1.6% vs. 4.9% vs. 7.1%, p < 0.001). Rates of readmission (1.9% vs. 3.0% vs. 7.4%, p = 0.005) and unplanned readmission (1.2% vs. 1.8% vs. 5.9%, p = 0.003) were higher among frailer groups. Differences in reoperation (1.7% vs. 2.1% vs. 1.2%, p = 0.961) and mortality (0.1% vs. 0.7% vs. 0%, p = 0.219) were not statistically significant.
TABLE 2.
Perioperative complication rates stratified by frailty level using the mFI‐5 index (n = 2260).
| mFI‐5 Score 0 (n = 1890) | mFI‐5 Score 1 (n = 285) | mFI‐5 Score ≥ 2 (n = 85) | p | ||
|---|---|---|---|---|---|
| Surgical complications, N (%) | Any | 24 (1.27%) | 9 (3.16%) | 2 (2.35%) | 0.037 |
| Extended length of stay | 25 (1.32%) | 13 (4.56%) | 11 (12.94%) | < 0.001 | |
| Superficial SSI | 16 (0.85%) | 1 (0.35%) | 0 (0.00%) | 0.225 | |
| Deep SSI | 1 (0.05%) | 2 (0.70%) | 0 (0.00%) | 0.097 | |
| Wound disruption | 4 (0.21%) | 0 (0.00%) | 1 (1.18%) | 0.360 | |
| Medical complications, N (%) | Any | 8 (0.42%) | 5 (1.75%) | 4 (4.71%) | < 0.001 |
| Pneumonia | 2 (0.11%) | 0 (0.00%) | 2 (2.35%) | 0.001 | |
| Re‐Intubation | 2 (0.11%) | 1 (0.35%) | 0 (0.00%) | 0.638 | |
| Ventilation > 48 h | 1 (0.05%) | 1 (0.35%) | 1 (1.18%) | 0.004 | |
| Urinary tract infection | 2 (0.11%) | 1 (0.35%) | 2 (2.35%) | < 0.001 | |
| Sepsis | 2 (0.11%) | 1 (0.35%) | 0 (0.00%) | 0.638 | |
| Any complication, N (%) | 31 (1.64%) | 14 (4.91%) | 6 (7.06%) | < 0.001 | |
| Readmissions, N (%) | 26 (1.90%) | 6 (2.97%) | 5 (7.35%) | 0.005 | |
| Unplanned readmissions, N (%) | 23 (1.22%) | 5 (1.75%) | 5 (5.88%) | 0.003 | |
| Reoperations, N (%) | 32 (1.69%) | 6 (2.11%) | 1 (1.18%) | 0.961 | |
| Mortality, N (%) | 2 (0.11%) | 2 (0.70%) | 0 (0.00%) | 0.219 | |
Note: Totals used in calculating percentages excluded missing cases for each variable. Any complication: Defined as the occurrence of either a surgical or medical complication. Surgical complication: Defined as the presence of any of the following: superficial or deep surgical site infection, organ space infection, wound disruption, postoperative bleeding, or graft failure. Medical complication: Defined as the presence of any of the following: pneumonia, reintubation, pulmonary embolism, mechanical ventilation for more than 48 h, renal insufficiency, acute renal failure, urinary tract infection, stroke, cardiac arrest, myocardial infarction, deep vein thrombosis, sepsis, septic shock, or coma lasting more than 24 h. Bold values indicate a significant p value < 0.05.
Abbreviations: h = hours. MI = Myocardial Infarction.
After adjusting for demographic and comorbidity covariates, patients with mFI‐5 = 1 had significantly greater odds of any complication (OR = 2.29, 95% CI 1.10–4.79, p = 0.027) and any surgical complication (OR = 2.80, 95% CI 1.16–6.73, p = 0.022). Patients with mFI‐5 ≥ 2 also had increased odds of any complication (OR = 2.83, 95% CI 1.01–7.93, p = 0.049). Associations between frailty and medical complications, readmissions, unplanned readmissions, and reoperations did not reach statistical significance in adjusted analysis (Table 3).
TABLE 3.
Multivariable logistic regression identifying factors associated with perioperative complications (n = 2252).
| Significance | OR | 95% CI | ||
|---|---|---|---|---|
| Any complication | mFI‐5 Score = 0 (Reference) | |||
| mFI‐5 Score = 1 | 0.027 | 2.29 | 1.10–4.79 | |
| mFI‐5 Score ≥ 2 | 0.049 | 2.83 | 1.01–7.93 | |
| Any medical complication | mFI‐5 Score = 0 (Reference) | |||
| mFI‐5 Score = 1 | 0.544 | 1.47 | 0.42–5.16 | |
| mFI‐5 Score ≥ 2 | 0.140 | 2.86 | 0.71–11.54 | |
| Any surgical complication | mFI‐5 Score = 0 (Reference) | |||
| mFI‐5 Score = 1 | 0.022 | 2.80 | 1.16–6.73 | |
| mFI‐5 Score ≥ 2 | 0.357 | 2.11 | 0.43–10.39 | |
| Readmission | mFI‐5 Score = 0 (Reference) | |||
| mFI‐5 Score = 1 | 0.902 | 1.06 | 0.40–2.82 | |
| mFI‐5 Score ≥ 2 | 0.172 | 2.21 | 0.71–6.90 | |
| Unplanned readmissions | mFI‐5 Score = 0 (Reference) | |||
| mFI‐5 Score = 1 | 0.893 | 0.93 | 0.32–2.68 | |
| mFI‐5 Score ≥ 2 | 0.086 | 2.71 | 0.87–8.46 | |
| Reoperations | mFI‐5 Score = 0 (Reference) | |||
| mFI‐5 Score = 1 | 0.724 | 1.19 | 0.44–3.21 | |
| mFI‐5 Score ≥ 2 | 0.634 | 0.60 | 0.07–5.01 |
Note: Any complication: Defined as the occurrence of either a surgical or medical complication. Surgical complication: Defined as the presence of any of the following: superficial or deep surgical site infection, organ space infection, wound disruption, postoperative bleeding, or graft failure. Medical complication: Defined as the presence of any of the following: pneumonia, reintubation, pulmonary embolism, mechanical ventilation for more than 48 h, renal insufficiency, acute renal failure, urinary tract infection, stroke, cardiac arrest, myocardial infarction, deep vein thrombosis, sepsis, septic shock, or coma lasting more than 24 h. Bold values indicate a significant p value < 0.05.
4. Discussion
To our knowledge, this is the first study that investigates how mFI‐5, a comorbidity‐based risk stratification tool, can be utilized to predict adverse outcomes for ZMC fracture repair patients. This study demonstrates that increased patient frailty, as measured by the mFI‐5, was independently associated with increased risk of cumulative morbidity and surgical complications after ZMC fracture repair. Specifically, higher mFI‐5 scores were linked to various perioperative complications, which included an extended length of stay, urinary tract infections, pneumonia, unplanned readmissions, and ventilation exceeding 48 h. This may be an important consideration in an aging population as well as ZMC fracture, which is showing an increase in falls as the mechanism of injury, especially in older patients [21].
These findings are consistent with recent medical literature that has consistently demonstrated that frailty serves as a strong prognostic value for predicting morbidity, particularly throughout facial fracture repairs. In one such recent study, a link was established between increased frailty and increased complications, reoperation, readmission, adverse events, and prolonged hospitalization for patients who underwent mandibular ORIFs [22]. More broadly, studies grouping together various types of maxillofacial fracture repairs also revealed that frailty is a significant predictor of adverse postoperative outcomes, ranging from complications to extended stays [10, 23].
The predictive value of mFI‐5 has been further validated in the broader otolaryngology landscape. In the field of skull base surgery, Henry et al. showed that a one‐point rise in mFI‐5 score led to the odds of life‐threatening complications increasing by 42.8% [19]. Likewise, a study by Adams et al. for inpatient otolaryngologic operations demonstrated a 25.0% increase in Clavien‐Dindo Grade IV complication risk and a 30.0% increase in all complication risk when the mFI rose solely from 0 to 0.45 [24]. Seib et al. revealed that patients with an NSQIP mFI score of 4 or higher undergoing parathyroidectomy had 8 times greater odds of complication compared to those with an mFI score of 0–1 [25].
Compared to the wealth of research on technical and surgical aspects of fracture management, there is a lack of literature for ZMC fracture repair regarding comorbidities and their association with postoperative outcomes. More consideration should be given to how a patient's underlying health may affect their recovery [26]. An understanding of mFI‐5 for its utility towards ZMC fractures is of utmost importance in light of over 80% of patients from our retrospective study exhibiting a frailty score of 0. Our data are in line with broader maxillofacial trauma studies by Katz et al. and Lee et al., which noted 86% and 83% of patients to be nonfrail, respectively [10, 23]. As such, accounting for frailty could be particularly relevant for ZMC fracture patients.
The incidence of facial fractures has been increasing in elderly populations, particularly due to secondary or low‐energy mechanisms of injury such as falls [21, 27]. Several studies have applied mFI‐5 to elderly populations, as frailty has conventionally been associated with a geriatric population [7, 28, 29]. However, ZMC fracture patients represent a young cohort, and multiple epidemiological studies have found the mean age of ZMC cohorts to be in the third and fourth decades of life [30, 31]. In these traditionally younger cohorts, ZMC fractures frequently occur with concomitant craniofacial and systemic injuries, particularly following higher‐energy mechanisms such as motor vehicle collisions, highlighting the heterogeneous nature of these injuries [32, 33, 34, 35, 36, 37]. In contrast, among older adults, nearly 90% of ZMC fractures result from low‐energy falls, which more commonly produce isolated ZMC fractures than the high‐energy mechanisms typically seen in younger patients. Despite being managed operatively less frequently, these patients experience higher rates of concomitant brain injury and mortality [2, 13, 21, 38, 39, 40]. Therefore, our exploration of mFI‐5 across a wide spectrum of ages aims to incorporate traditionally younger patients who are often overlooked in frailty studies, and older patients who are experiencing a growth in facial fracture incidence and change in mechanism [13, 15, 21].
The mFI‐5 is a practical and effective clinical screening tool, requiring only five comorbid variables that can be easily extracted from the patient history and collected in a routine review of systems [9, 10, 41]. In cases where these factors are not concurrently accessible, functional status as a single variable may serve as a surrogate for frailty and offer meaningful prognostic value [24]. Our study underscores the consideration of mFI‐5 scores to help preemptively mitigate risk for perioperative complications in these types of procedures. Due to the lack of consensus over fracture management for ZMC procedures, the utilization of risk stratification tools and comorbidities is paramount because of the necessity for an individual approach to each case [42, 43]. Moreover, increased complications and length of stay impact overall costs of care. Identifying patients at higher risk of complications and readmissions is necessary to improve quality of outcomes and also has the potential to reduce healthcare costs [44, 45]. Furthermore, elderly fracture patients may not be able to tolerate complications such as pneumonia, UTI, and prolonged ventilation like their young counterparts. These complications incur devastating and lasting effects on our elderly population in particular [46, 47].
Numerous studies have shown mFI‐5 to independently outperform traditional indicators, such as ASA classification or age, for postoperative morbidity and mortality [10, 24, 41, 48]. In fact, one study by Wilson et al. found the burden of frailty to be 28 times that of age alone, possibly due to mFI‐5 incorporating physiological reserve in its grading scale [18]. However, conventional methods of predicting surgical outcomes should not be discredited. Our secondary findings of interest demonstrate a clear association between higher frailty scores and older age. In the context of an increasing incidence of geriatric facial trauma, these results support consideration of nonoperative management when appropriate or enhanced perioperative care for frail patients. These proactive initiatives can guide informed decision‐making and promote high‐value care for ZMC fracture patients to ultimately improve outcomes.
There are several limitations in this study. First, the NSQIP database does not report severity and precise location of fractures, surgical techniques, and other various clinical and radiographic factors that may influence complications for ZMC fracture patients. Socioeconomic factors have also been shown to impact outcomes in trauma populations, but this data was not inherently available to analyze [49]. In addition, there is a possibility of coding or misclassification errors in our methods, although NSQIP has implemented a stringent protocol and strict coding training to mitigate this risk. Next, our study was limited to patients identified using CPT codes for operative ZMC fracture repair, which classify the operative approach (percutaneous versus open surgical treatment) rather than whether a fracture was clinically open or closed. Consequently, our findings may not be generalizable to patients managed nonoperatively or to all patterns of ZMC injury. Lastly, given the maxillofacial frailty study by Lee et al. showing that wound classification had the most significant impact on the odds of an adverse event, more work is prompted to see how wound classification can independently, and in conjunction with frailty, serve as a predictor of postoperative complications in ZMC patients [10]. Additional research on nuanced risk stratification and improving outcomes for ZMC fracture patients may help define evidence‐based perioperative pathways. Nonetheless, our study determines that mFI‐5 is a valuable prognostic tool for ZMC fracture repair, enhancing risk assessment and decision‐making in otolaryngology facial trauma practice.
5. Conclusion
The mFI‐5 was associated with cumulative morbidity and surgical complications in patients undergoing surgical repair of ZMC fractures. Evaluating mFI‐5 can help assess the risk of postoperative complications for patients undergoing ZMC fracture repair.
Funding
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
The authors did not use any artificial intelligence or AI‐assisted technologies.
Data Availability Statement
The data that support the findings of this study are available in ACS National Surgical Quality Improvement Program at https://www.facs.org/quality‐programs/data‐and‐registries/acs‐nsqip/. These data were derived from the following resources available in the public domain: ACS NSQIP Participant Use Data File, https://www.facs.org/quality‐programs/data‐and‐registries/acs‐nsqip/participant‐use‐data‐file/.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available in ACS National Surgical Quality Improvement Program at https://www.facs.org/quality‐programs/data‐and‐registries/acs‐nsqip/. These data were derived from the following resources available in the public domain: ACS NSQIP Participant Use Data File, https://www.facs.org/quality‐programs/data‐and‐registries/acs‐nsqip/participant‐use‐data‐file/.