https://orcid.org/0000-0002-4705-0262
Figures
Abstract
Background
Frailty has been recognized as an independent risk factor for inferior outcomes, but its effect on emergency general surgery (EGS) is understudied.
Objective
The purpose of the present study was to define the impact of frailty on risk-adjusted mortality, non-home discharge, and readmission following EGS operations.
Methods
Adults undergoing appendectomy, cholecystectomy, small bowel resection, large bowel resection, repair of perforated ulcer, or laparotomy within two days of an urgent admission were identified in the 2016–2017 Nationwide Readmissions Database. Frailty was defined using diagnosis codes corresponding to the Johns Hopkins Adjusted Clinical Groups frailty indicator. Multivariable regression was used to study in-hospital mortality and non-home discharge by operation, and Kaplan Meier analysis to study freedom from unplanned readmission at up to 90-days follow-up.
Results
Among 655,817 patients, 11.9% were considered frail. Frail patients most commonly underwent large bowel resection (37.3%) and cholecystectomy (29.2%). After adjustment, frail patients had higher mortality rates for all operations compared to nonfrail, including those most commonly performed (11.9% [95% CI 11.4–12.5%] vs 6.0% [95% CI 5.8–6.3%] for large bowel resection; 2.3% [95% CI 2.0–2.6%] vs 0.2% [95% CI 0.2–0.2%] for cholecystectomy). Adjusted non-home discharge rates were higher for frail compared to nonfrail patients following all operations, including large bowel resection (68.1% [95% CI 67.1–69.0%] vs 25.9% [95% CI 25.2–26.5%]) and cholecystectomy (33.7% [95% CI 32.7–34.7%] vs 2.9% [95% CI 2.8–3.0%]). Adjusted hospitalization costs were nearly twice as high for frail patients. On Kaplan-Meier analysis, frail patients had greater unplanned readmissions (log rank P<0.001), with 1 in 4 rehospitalized within 90 days.
Conclusions
Frail patients have inferior clinical outcomes and greater resource use following EGS, with the greatest absolute differences following complex operations. Simple frailty assessments may inform expectations, identify patients at risk of poor outcomes, and guide the need for more intensive postoperative care.
Citation: Hadaya J, Sanaiha Y, Juillard C, Benharash P (2021) Impact of frailty on clinical outcomes and resource use following emergency general surgery in the United States. PLoS ONE 16(7): e0255122. https://doi.org/10.1371/journal.pone.0255122
Editor: Saraschandra Vallabhajosyula, Emory University School of Medicine, UNITED STATES
Received: May 17, 2021; Accepted: July 9, 2021; Published: July 23, 2021
Copyright: © 2021 Hadaya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data cannot be shared directly by the authors because specific approval for data access and completion of a Data Use Agreement is required by the Agency for Healthcare Research and Quality. Data are available from the Agency for Healthcare Research and Quality (contact via www.hcup-us.ahrq.gov) for researchers who meet the criteria for access and complete a Data Use Agreement.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Operative emergencies in acute care surgery are associated with substantial risk of mortality and rehospitalization [1–3]. Emergency general surgery (EGS) operations are often performed in patients with severely deranged physiology, and occasionally in those with hemodynamic compromise and end organ dysfunction [4,5]. The underlying pathology in this cohort is often acute in presentation, limiting the body’s compensatory responses. Several factors including age, operative type, and burden of comorbidities are thought to impact the outcomes of EGS operations [2,4,6,7].
Traditional risk factors such as advanced age have been recognized to inadequately predict outcomes following complex operations [8–10]. Recently, several investigators have reported frailty to influence postoperative outcomes including death [10–14]. Often associated with accumulation of chronic conditions, frailty is generally considered as the inability to withstand physiologic stressors [15–17]. In the setting of EGS operations, Murphy et al. used the National Surgical Quality Improvement Program (NSQIP) database to identify frailty using the modified frailty index (mFI) and found frailty to adversely impact EGS outcomes in those >40 years of age [18]. However, applicability of these findings is limited by low participation rates in NSQIP (12% of hospitals performing surgery in 2013) and only 3.6% of the study cohort classified as highly frail by the mFI [19]. Furthermore, hospitals participating in NSQIP have greater case volumes and hospital beds, are more frequently academically-affiliated and less commonly critical access hospitals [19]. Thus, data sources with more uniform participation may provide a more accurate landscape of outcomes following EGS, particularly in high risk cohorts such as the frail.
While a universal definition for frailty is lacking, several instruments ranging from intricate psychomotor testing to administrative algorithms have been employed to diagnose and quantify this state [20–24]. Many frailty tests are resource intensive and cannot be administered in the non-elective setting [23,24]. Coding-based scoring systems have garnered attention as methods to identify frailty using administrative data. The Johns Hopkins Adjusted Clinical Group cluster of diagnoses has recently been implemented as a coding-based method to identify frail patients in surgical patients, including those undergoing head and neck operations and cardiac surgery [25–28]. This binary system has several advantages, including ease of implementation (as it is solely derived from administrative data), lack of additional resources required for data collection, and inclusion of characteristics that do not typically overlap with postoperative complications.
We examined the impact of frailty as assessed by the Johns Hopkins frailty index on clinical outcomes and resource use following EGS operations in all adults using the Nationwide Readmissions Database (NRD), a widely inclusive administrative database. We hypothesized frailty to be independently associated with increased mortality, length of stay, hospitalization costs, rates of non-home discharge as well as readmissions across common EGS operations.
Methods
Data source and cohort definitions
The present study was a retrospective cohort study using the 2016–2017 NRD. The NRD is the largest, all-payer, national readmissions database and is maintained by the Agency for Healthcare Research and Quality (AHRQ) as part of the Healthcare Cost and Utilization Project (HCUP) [29]. The NRD samples 28 State Inpatient Databases annually and represents approximately 58% of all hospitalizations in the United States [29]. Patient-specific linkage numbers allow patients to be tracked across inpatient hospitalizations within each calendar year.
International Classification of Disease, Tenth Edition, Procedure Coding System (ICD-10-PCS) codes were used to identify patients undergoing one of the following EGS operations: large bowel resection, small bowel resection, repair of perforated ulcer, cholecystectomy, appendectomy, and lysis of adhesions. These procedures were chosen due to their frequency and clinical relevance to general surgery practice [30]. If multiple EGS procedures were tabulated for a patient, the primary operation was considered the procedure with the greatest probability of mortality [30].
Patients under the age of 18 and those admitted on an elective basis were excluded from further study. Patients with an admission for injury or trauma were excluded using International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes validated by the National Center for Health Statistics [31]. Patients with missing data for age, sex, admission type, and in-hospital mortality were excluded. To maintain a consistent definition of EGS operations, only operations performed on hospital days 0, 1, or 2 were considered [30].
Patients were divided into frail (Frail) and nonfrail (Nonfrail) cohorts, with frailty identified by the presence of at least one frailty-defining diagnosis as reported by Neiman et al [25]. ICD-10-CM codes were used to identify relevant diagnoses, which were derived from the validated Johns Hopkins Adjusted Clinical Groups (ACG) frailty-defining diagnoses indicator. This binary indicator categorizes frailty-defining diagnoses into malnutrition, dementia, impaired vision, decubitus ulcer, incontinence, weight loss, falls, difficulty walking, poverty, and barriers to healthcare access (S1 Table) [25]. Derivatives of the ACG have been extensively used in medical and surgical studies of frailty in administrative databases [25–28].
Variable definitions and study outcomes
Patient and hospital characteristics included age, sex, admission type, primary payer, income quartile, and hospital teaching status. Each hospital’s annual emergency general surgery volume was calculated, and hospitals were divided into volume low-, medium-, and high-volume tertiles based on the annual aggregate EGS caseload for each center. The Elixhauser Comorbidity Index, a validated composite score of 30 chronic comorbidities, was used to quantify patient comorbidities [32].
Mortality was defined as death during the index hospitalization. Non-home discharge was defined as discharge to an acute hospital, intermediate care facility, or skilled nursing facility. Readmission was defined as unplanned rehospitalization among patients surviving to index discharge. Hospital costs were calculated from charges using hospital-specific cost-to-charge ratios reported by the AHRQ and adjusted for inflation to 2017 using the Bureau of Labor Statistics Consumer Price Index [29,33].
The primary outcomes of the study were mortality at the index hospitalization, non-home discharge, and 30-day unplanned readmission rates. Several secondary outcomes included postoperative length of stay and hospitalization costs.
Statistical analysis
Categorical variables were reported as frequency and percent and continuous variables as mean and standard deviation, or median and interquartile range if non-normally distributed. Chi-squared and adjusted Wald t-tests were used to compare patient and hospital characteristics.
Multivariable logistic and linear models were used to identify independent associations between outcomes and frailty and covariates. A generalized linear regression model with gamma error distribution and log-link function was used to study costs. Hospital-specific discharge weights were used to obtain survey-weighted estimates that account for clustering [29]. Covariates remaining after backward stepwise elimination and those deemed clinically relevant were included in final models. Interaction terms between frailty and EGS operation type, and EGS operation type and hospital teaching status were included in all models. Models were evaluated using the receiver operating characteristics curve and Akaike information criterion. Following each regression, estimates were calculated using the Stata margins command. Adjusted outcomes are reported as estimates with 95% confidence interval (95% CI). Kaplan-Meier survival analysis with log rank test was used to compare occurrence of unplanned readmissions by cohort. Follow-up time was constrained by the structure of NRD, as patients are followed through the end of each calendar year.
This study was deemed exempt by the Institutional Review Board at the University of California, Los Angeles. Statistical analysis was performed with Stata 16.0 (StataCorp, College Station, TX). Statistical significance was set at α<0.05.
Results
Frail and Nonfrail cohort characteristics
Of an estimated 655,817 patients, 78,093 (11.9%) were considered Frail. Patients in the Frail cohort were older, with 65.0% of Frail >65 years of age, compared to 32.4% of Nonfrail (Table 1). The Frail cohort had a greater composite burden of comorbidities, with an Elixhauser Comorbidity Score >2 in 76.7% vs 34.9% in the Nonfrail cohort. There were greater rates of all examined comorbidities in the Frail cohort, including congestive heart failure, chronic lung disease, chronic liver disease, and end stage renal disease. The predominant primary payer in the Frail cohort was Medicare (68.3%), compared to private insurance (37.9%) in Nonfrail. Most patients were treated at teaching hospitals (64.1% of Frail vs 61.3% of Nonfrail). The most common operations performed were cholecystectomy (63.9% of Nonfrail and 29.2% of Frail) and large bowel resection (14.7% of Nonfrail and 37.3% of Frail, Table 1). A majority of patients underwent surgery on the day of admission (Table 1).
Clinical outcomes in Frail vs Nonfrail patients by operation
Compared to Nonfrail, the Frail cohort had greater observed rates of index-hospitalization mortality, non-home discharge, 30-day unplanned readmission and greater costs for each operative category (S2 Table). Adjusted estimates for each outcome by frail status and operation type were determined following multivariable logistic or linear regression (Table 2). In all operative categories, the Frail cohort had significantly greater adjusted rates of mortality at the index hospitalization compared to Nonfrail. The greatest absolute difference in adjusted rate of mortality for Frail vs Nonfrail occurred following repair of perforated ulcer (+8.3%, 95% CI 7.1–10.4%), followed by large bowel resection (+5.9%, 95% CI 5.3–6.5%, Fig 1). Frailty was associated with increased rates of non-home discharge (Fig 1), with greatest differences following repair of perforated ulcer and large bowel resection. Absolute differences in adjusted costs varied by operation, with the greatest difference between Frail and Nonfrail occurring following repair of perforated ulcer ($24,600, 95% CI 22,300–26,900) and small bowel resection ($21,600, 95% CI $20,000–23,100).
Estimate represents mean absolute difference in adjusted mortality or non-home discharge rate, with 95% confidence interval, by operation type.
Factors associated with mortality and readmission following EGS
Multivariable regression identified several additional factors independently associated with mortality and unplanned readmission (Table 3). An Elixhauser Comorbidity Index >2 (adjusted odds ratio, AOR, 1.79, 95% CI 1.65–1.94) and older age (AOR 1.04, 95% CI 1.04–1.04 per 1-year increment) were associated with increased odds of mortality. Specific comorbidities including congestive heart failure, chronic lung disease, ESRD, and chronic liver disease were independently associated with mortality. Nonmetastatic cancer was associated with reduced odds of mortality while there was no association between metastatic cancer and mortality. Notably, transferred patients were at greater odds of mortality (AOR 1.50, AOR 1.28–1.75) while no association was found between mortality and hospital teaching status. Relative to private insurers, Medicare or Medicaid payer status was associated with increased odds of mortality.
Among patients who survived to discharge, conditions associated with 30-day unplanned readmission included congestive heart failure, chronic lung disease, and ESRD. Similar to index mortality, the odds of rehospitalization were higher for patients with Medicare and Medicaid coverage when considering private insurance status as reference. Overall, Kaplan-Meier survival analysis showed greater unplanned readmission in Frail compared to the Nonfrail cohort (Fig 2, log rank P<0.001). When stratified by EGS procedure, frailty remained associated with increased readmission following all EGS operations considered (log rank P<0.001 for each).
Kaplan Meier curves with 95% confidence interval (shaded) include all EGS operations. Log rank P<0.001.
Discussion
In this population-based cohort study, we evaluated the impact of frailty, as defined by an administrative coding-based tool, on several clinical and financial endpoints following 6 common EGS operations. Frailty was associated with significantly increased mortality and readmission rates in all operative categories. Moreover, the presence of frailty was associated with a near doubling of adjusted hospitalization costs, with half of the frail cohort was discharged to a nursing home or rehabilitation facility (Fig 3). Importantly, we demonstrated the differential impact of frailty on operative outcomes, with a greater influence noted in more complex operations such as large bowel resection and repair of perforated ulcers.
Frailty adversely impacts odds of in-hospital mortality, non-home discharge, readmission, and is associated with greater resource use.
A large body of literature examining factors influencing outcomes in the EGS population has identified variables such as advanced age, burden of comorbidities as well as physiologic derangements to portend inferior outcomes [2,3,5]. Traditional risk factors aside, many surgeons have considered a patient’s fitness for surgery, informally known as the “eyeball test”, to play an important role in postoperative outcomes. Frailty has been characterized using instruments that range from intricate individualized examinations to scoring based on accumulation of comorbidities [23,24,34]. Given the additional constraints of preoperative testing in EGS, administrative coding algorithms including the NSQIP modified Frailty Index (mFI) have been utilized to assess the presence of frailty in surgical patients. Murphy and colleagues found an association between frailty, defined by the mFI, and 30-day mortality following several common EGS operations [18]. This study, however, was limited to patients aged >40 years with only 3.6% of the cohort considered highly frail. Furthermore, variable associations between the intermediately frail group and index hospitalization outcomes limit the generalizability of the mFI. In contrast to other frailty instruments, derivatives of the ACG indicator used in the present study do not consider traditional risk factors such as heart failure, stroke, and cardiovascular diseases in defining frailty [25,27]. McIsaac et al used the ACG in patients aged >65 years in Ontario, Canada, and found frailty to impact 1-year mortality following cholecystectomy and appendectomy [35]. Our results build on prior work by examining frailty using this binary indicator. The group of administrative codes used in the present study identified 12% of patients as frail and found a consistent adverse impact on outcomes across six common operations in an inclusive, nationally representative, population-based cohort. Importantly, this assessment of frailty captured conditions beyond traditional surgical risk factors, such that frailty remained independently associated with inferior outcomes after adjusting for medical comorbidities. The present study provides new insights into the outcomes expected in this vulnerable population, and may help guide surgical care.
Acuity and severity of illness in EGS present unique challenges in risk factor optimization and perioperative care [2,5]. While frailty may not be a modifiable risk factor in the short term, knowledge of its presence may aid in shared decision-making and counseling regarding expectations. For example, as anastomotic leak greatly increases surgical morbidity, high risk colonic anastomoses may more often warrant proximal diversion in frail patients, as inpatient mortality rates were high for the frail group in the present study. While frailty is often co-existent with medical comorbidities such as coronary disease and chronic lung disease, it remained strongly associated with poor outcomes across all conditions studied, and may provide additional data regarding clinical risk. In particular, frailty may inform discussions regarding expected length of stay, the possibility of discharge to rehabilitation or nursing facilities, and unanticipated rehospitalization. Given the significant cost differential for frail patients, interventions to mitigate complications and facilitate more efficient care may improve outcomes for this group. Similar approaches have been reported by the American College of Surgeons Program for Geriatric Surgery Verification, and have shown promise for improved management of geriatric patients undergoing general surgery operations [36]. Practical measures to reduce common hospital complications that are likely more prevalent in frail patients, such as postoperative delirium, falls, and aspiration, may contribute to improved outcomes. Moreover, aimed at addressing frailty-specific issues that contribute to poor outcomes, such as lack of mobility and poor nutrition, may facilitate the development of evidence-based interventions for frail patients.
Several other variables were also associated with mortality and 30-day readmissions. In the present work, Medicare and Medicaid insurance were associated with greater odds of mortality and readmissions in all EGS categories. Others have reported poor clinical outcomes and increased resource use following elective operations including colectomy and gastrectomy, as well as in the setting of blunt trauma, in these demographic groups [37,38]. These findings may be related to reduced access to care or delays in treatment, and further study of relationships between other social determinants of health, such as education, employment, and race, and access to general surgery care are imperative. Of note, public insurance had a greater magnitude of association with in-hospital mortality and 30-day readmissions compared to several comorbidities including chronic lung disease and heart failure. This underscore the importance of public health efforts to improve access to care, develop strategies for patient engagement, and incentivize hospitals and providers who care for these patients. We similarly found patients who were transferred to the operating hospital to be at increased odds of mortality, which may be related to possible delays of care related to recognition of a surgical condition, referral, and transport time [39]. These factors may be of greater relevance in frail patients, as they are already at greater odds of mortality in all EGS operations.
A unique finding of the present study is the differential impact of frailty on outcomes following various EGS operations. More invasive and complex procedures, such as large bowel resection and repair of perforated ulcer, had a greater absolute difference in adjusted rates of mortality and non-home discharge between the frail and nonfrail cohorts. Conversely, more routine EGS operations, such as cholecystectomy, had the least absolute difference between the two groups. These findings may be related to a greater degree of underlying illness and acute decompensation that result in the need for an urgent colectomy or repair of perforated ulcer. Likewise, reduced tolerance to the physiologic effects of an operative illness and its sequalae may explain the greater differences in non-home discharge rates in complex operations, resulting in the need for further care outside of the hospital. McIsaac et al reported a greater hazard ratio for 1-year mortality with the presence of frailty in patients undergoing appendectomy, cholecystectomy and bowel resection, but not ulcer repair [35]. However, this study did not examine absolute differences in frailty-based outcomes and was limited to patients >65 years of age in a single Canadian province, where practice patterns may vary from the US. While absolute differences were greater for complex operations in this study, even more routine operations such as cholecystectomy had almost a 10-fold increase in adjusted mortality for Frail versus Nonfrail cohorts. Given the substantial variation in operative risk of these EGS procedures, the present work provides practical guidance that may better inform shared decision making, expectations, and postoperative care.
The present study has several limitations inherent to the nature of a large, administrative database. Although the NRD is the largest, all-payer readmission database, it only estimates approximately 58% of admissions in the United States across 28 states. We limited our analysis to six common operations, and focused on those undergoing surgery on hospital day 0 to 2, which does not encompass the entirety of emergency general surgery. Clinical data, including laboratory values and imaging findings, and measures of disease severity, were unavailable for analysis. Although some aspects of functional status are captured using a coding-based frailty indicator, other clinical markers of frailty, such as hypoalbumenia, sarcopenia, and grip strength, could not be studied. In the NRD, mortality outside of a hospital setting cannot be identified, and, as such, we limited our analysis to readmissions. Despite these limitations, we used validated data practices recommended by HCUP to report nationally representative outcomes of EGS.
Conclusions
We found frailty to be independently associated with inferior outcomes following all examined EGS operations, with increased rates of mortality, non-home discharge, and hospitalization costs. Frailty has the most pronounced absolute effect on outcomes for more complex and higher acuity operations such as repair of perforated ulcer or large bowel resection, and the least for routine operations. These findings underscore the value of a simple frailty assessment for patients undergoing EGS operations, which may be useful in setting expectations about courses of care as well as in identifying patients that may require more intensive care postoperatively. Further study to identify factors that may mitigate the effect of frailty, as well as efforts to implement practices to better manage frail patients perioperatively, may improve outcomes for this vulnerable patient population.
Supporting information
S1 Table. Derivatives of Johns Hopkins ACG categories and representative diagnoses.
https://doi.org/10.1371/journal.pone.0255122.s001
(DOCX)
S2 Table. Unadjusted outcomes for Nonfrail and Frail cohorts stratified by operation.
aCosts reported in $1000 USD with IQR. bLength of stay reported as days with IQR. P<0.001 for all comparisons between Frail versus Nonfrail by operation and outcome.
https://doi.org/10.1371/journal.pone.0255122.s002
(DOCX)
References
- 1. Havens JM, Olufajo OA, Cooper ZR, Haider AH, Shah AA, Salim A. Defining rates and risk factors for readmissions following emergency general surgery. JAMA Surg. 2016;151: 330–336. pmid:26559368
- 2. Havens JM, Peetz AB, Do WS, Cooper Z, Kelly E, Askari R, et al. The excess morbidity and mortality of emergency general surgery. J Trauma Acute Care Surg. 2015;78: 306–311. pmid:25757115
- 3. Mullen MG, Michaels AD, Mehaffey HJ, Guidry CA, Turrentine LE, Hedrick TL, et al. Risk associated with complications and mortality after urgent surgery vs elective and emergency surgery: Implications for defining “quality” and reporting outcomes for urgent surgery. JAMA Surg. 2017;152: 768–774. pmid:28492821
- 4. Akinbami F, Askari R, Steinberg J, Panizales M, Rogers SO. Factors affecting morbidity in emergency general surgery. Am J Surg. 2011;201: 456–462. pmid:21421099
- 5. Matsuyama T, Iranami H, Fujii K, Inoue M, Nakagawa R, Kawashima K. Risk factors for postoperative mortality and morbidities in emergency surgeries. J Anesth. 2013;27: 838–843. pmid:23700220
- 6. Turrentine FE, Wang H, Simpson VB, Jones RS. Surgical Risk Factors, Morbidity, and Mortality in Elderly Patients. J Am Coll Surg. 2006;203: 865–877. pmid:17116555
- 7. Sundaresan S, McLeod R, Irish J, Burns J, Hunter A, Meertens E, et al. Early results after regionalization of thoracic surgical practice in a single-payer system. Ann Thorac Surg. 2013;95: 472–479. pmid:23261113
- 8. Sankar A, Beattie WS, Wijeysundera DN. How can we identify the high-risk patient? Curr Opin Crit Care. 2015;21: 328–335. pmid:26083327
- 9. Davenport DL, Bowe EA, Henderson WG, Khuri SF, Mentzer RM. National Surgical Quality Improvement Program (NSQIP) risk factors can be used to validate American Society of Anesthesiologists Physical Status Classification (ASA PS) levels. Ann Surg. 2006;243: 636–641. pmid:16632998
- 10. Seib CD, Rochefort H, Chomsky-Higgins K, Gosnell JE, Suh I, Shen WT, et al. Association of patient frailty with increased morbidity after common ambulatory general surgery operations. JAMA Surg. 2018;153: 160–168. pmid:29049457
- 11. Augustin T, Burstein MD, Schneider EB, Morris-Stiff G, Wey J, Chalikonda S, et al. Frailty predicts risk of life-threatening complications and mortality after pancreatic resections. Surgery. 2016;160: 987–996. pmid:27545992
- 12. McIsaac DI, Bryson GL, van Walraven C. Association of Frailty and 1-Year Postoperative Mortality Following Major Elective Noncardiac Surgery: A Population-Based Cohort Study. JAMA Surg. 2016;151: 538–45. pmid:26791334
- 13. Farhat JS, Velanovich V, Falvo AJ, Horst HM, Swartz A, Patton JH, et al. Are the frail destined to fail? Frailty index as predictor of surgical morbidity and mortality in the elderly. J Trauma Acute Care Surg. 2012;72: 1526–1531. pmid:22695416
- 14. Makary MA, Segev DL, Pronovost PJ, Syin D, Bandeen-Roche K, Patel P, et al. Frailty as a Predictor of Surgical Outcomes in Older Patients. J Am Coll Surg. 2010;210: 901–908. pmid:20510798
- 15. Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the Concepts of Disability, Frailty, and Comorbidity: Implications for Improved Targeting and Care. Journals Gerontol Ser A Biol Sci Med Sci. 2004;59: M255–M263. pmid:15031310
- 16. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in Older Adults: Evidence for a Phenotype. Journals Gerontol Ser A Biol Sci Med Sci. 2001;56: M146–M157. pmid:11253156
- 17. Morley JE, Vellas B, Abellan van Kan G, Anker SD, Bauer JM, Bernabei R, et al. Frailty consensus: A call to action. J Am Med Dir Assoc. 2013;14: 392–397. pmid:23764209
- 18. Murphy PB, Savage SA, Zarzaur BL. Impact of Patient Frailty on Morbidity and Mortality after Common Emergency General Surgery Operations. J Surg Res. 2020;247: 95–102. pmid:31787316
- 19.
Sheils C, Dahlke A, Yang A, Bilimoria K. Are NSQIP Hospitals Unique? A Description of Hospitals Participating in ACS NSQIP. Presented at: Academic Surgical Congress 2016; February 3, 2016; Jacksonville, FL. https://www.asc-abstracts.org/abs2016/51-03-are-nsqip-hospitals-unique-a-description-of-hospitals-participating-in-acs-nsqip/.
- 20. Walston J, Buta B, Xue QL. Frailty Screening and Interventions: Considerations for Clinical Practice. Clin Geriatr Med. 2018;34: 25–38. pmid:29129215
- 21. Subramaniam S, Aalberg JJ, Soriano RP, Divino CM. New 5-Factor Modified Frailty Index Using American College of Surgeons NSQIP Data. J Am Coll Surg. 2018;226: 173–181.e8. pmid:29155268
- 22. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173: 489–495. pmid:16129869
- 23. Abellan Van Kan G, Rolland Y, Andrieu S, Bauer J, Beauchet O, Bonnefoy M, et al. Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) task force. J Nutr Heal Aging. 2009;13: 881–889. pmid:19924348
- 24. Richardson S. The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons. J Am Geriatr Soc. 1991;39: 142–148. pmid:1991946
- 25. Nieman CL, Pitman KT, Tufaro AP, Eisele DW, Frick KD, Gourin CG. The effect of frailty on short-term outcomes after head and neck cancer surgery. Laryngoscope. 2018;128: 102–110. pmid:28731497
- 26. Asemota AO, Gallia GL. Impact of frailty on short-term outcomes in patients undergoing transsphenoidal pituitary surgery. J Neurosurg. 2020;132: 360–370. pmid:30797214
- 27. Tran DTT, Tu J V, Dupuis J-Y, Bader Eddeen A, Sun LY. Association of Frailty and Long-Term Survival in Patients Undergoing Coronary Artery Bypass Grafting. J Am Heart Assoc. 2018;7. pmid:30030214
- 28. Iyengar A, Goel N, Kelly JJ, Han J, Brown CR, Khurshan F, et al. Effects of Frailty on Outcomes and 30-day Readmissions After Surgical Mitral Valve Replacement. Ann Thorac Surg. 2020;109: 1120–1126. pmid:32200907
- 29.
NRD Overview. [cited 12 Mar 2021]. https://www.hcup-us.ahrq.gov/nrdoverview.jsp.
- 30. Scott JW, Olufajo OA, Brat GA, Rose JA, Zogg CK, Haider AH, et al. Use of national burden to define operative emergency general surgery. JAMA Surg. 2016;151: e160480. pmid:27120712
- 31. Hedegaard H, Johnson RL. An updated international classification of diseases, 10th revision, clinical modification (ICD-10-CM) surveillance case definition for injury hospitalizations. Natl Health Stat Report. 2019;125: 1–8. pmid:31751206
- 32. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity Measures for Use with Administrative Data. Med Care. 1998;36: 8–27. pmid:9431328
- 33.
Using Appropriate Price Indices for Expenditure Comparisons. [cited 12 Mar 2021]. https://meps.ahrq.gov/about_meps/Price_Index.shtml.
- 34. Gilbert T, Neuburger J, Kraindler J, Keeble E, Smith P, Ariti C, et al. Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study. Lancet. 2018;391: 1775–1782. pmid:29706364
- 35. McIsaac DI, Moloo H, Bryson GL, Van Walraven C. The association of frailty with outcomes and resource use after emergency general surgery: A population-based cohort study. Anesth Analg. 2017;124: 1653–1661. pmid:28431425
- 36.
Geriatric Surgery Verification Program. [cited 21 Mar 2021]. https://www.facs.org/quality-programs/geriatric-surgery.
- 37. Lapar DJ, Bhamidipati CM, Mery CM, Stukenborg GJ, Jones DR, Schirmer BD, et al. Primary payer status affects mortality for major surgical operations. Ann Surg. 2010;252: 544–550. pmid:20647910
- 38. Haider AH, Weygandt PL, Bentley JM, Monn MF, Rehman KA, Zarzaur BL, et al. Disparities in trauma care and outcomes in the United States: A systematic review and meta-analysis. J Trauma Acute Care Surg. 2013;74: 1195–1205. pmid:23609267
- 39. DeWane MP, Davis KA, Schuster KM, Erwin SP, Maung AA, Becher RD. Transfer status. J Trauma Acute Care Surg. 2018;85: 348–353. pmid:29664889