A Novel Composite Frailty Score for Predicting Adverse Outcomes For Adult Women Undergoing Ankle Fracture Surgery: Model Development and Validation

A Novel Composite Frailty Score for Predicting Adverse Outcomes For Adult Women Undergoing Ankle Fracture Surgery: Model Development and Validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Novel Composite Frailty Score for Predicting Adverse Outcomes For Adult Women Undergoing Ankle Fracture Surgery: Model Development and Validation Cameron Sabet, Bhav Jain, Arnav Ajay Jadav, Jonathan Franco This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6839767/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Ankle fracture repair is a common orthopedic procedure, but outcomes vary significantly in vulnerable populations such as smokers. Frailty and nutritional deficits contribute independently to surgical risk, yet no validated composite score exists for risk stratification in this setting. Methods: Using ACS-NSQIP data from 2015–2021, we identified 14,818 adult smokers who underwent operative fixation of isolated ankle fractures. We developed the Combined ASA–RAI–Preoperative Acute Severe Condition (CARP) score by integrating the ASA classification, Risk Analysis Index, and PACS. We evaluated predictive accuracy for major complications, readmission, extended length of stay (eLOS), and non-home discharge using AUROC analysis and bootstrap validation. Results: The CARP score outperformed individual indices across all outcomes. For major complications, CARP achieved an AUROC of 0.721 versus 0.695 (RAI) and 0.695 (mFI-5). For eLOS, CARP reached 0.783 versus 0.770 (RAI) and 0.693 (mFI-5). Bootstrap-corrected AUROCs remained consistently higher for CARP, with significant improvements shown by DeLong tests. Conclusion: The CARP score provides superior discriminatory power over existing frailty and nutritional indices in predicting postoperative risks in smokers undergoing ankle fracture repair. Its integration into preoperative planning may improve risk stratification and guide individualized care. Surgical Complications Orthopedics Ankle Fractures Malleolus Frailty Figures Figure 1 INTRODUCTION Ankle fractures represent one of the most common orthopedic injuries, with an incidence exceeding 184 per 100,000 person-years in the United States, and this burden is projected to increase substantially as the population ages [ 1 ]. Open reduction and internal fixation (ORIF) remains the gold standard treatment for displaced ankle fractures, yet postoperative outcomes vary significantly across patient populations, particularly among elderly and frail individuals who demonstrate higher rates of complications, prolonged recovery, and adverse discharge destinations [ 2 ]. The growing recognition that chronological age alone inadequately predicts surgical risk has prompted increased focus on comprehensive preoperative risk stratification tools that can better identify vulnerable patients and guide clinical decision-making [ 3 ]. This shift toward precision medicine in surgical care has become increasingly critical as healthcare systems worldwide grapple with the complex needs of an aging population undergoing more frequent and complex surgical interventions. Current approaches to surgical risk assessment have evolved to incorporate multidimensional measures of patient vulnerability, with frailty emerging as a powerful predictor of adverse postoperative outcomes across diverse surgical specialties [ 4 , 5 ]. The Risk Analysis Index (RAI), a validated 14-item frailty assessment tool, has demonstrated robust predictive capability for 30-day mortality and morbidity in various surgical populations, though its performance varies across different procedural contexts and patient populations [ 6 , 7 ]. Concurrently, nutritional status assessment through tools such as the Geriatric Nutritional Risk Index (GNRI) has shown independent associations with surgical complications, length of stay, and long-term outcomes, with evidence suggesting that malnutrition significantly amplifies surgical risk beyond traditional comorbidity measures [ 8 ]. While individual frailty and nutritional assessments have proven valuable, recent studies in ankle fracture populations have highlighted the particular utility of the modified Frailty Index-5 (mFI-5) for predicting complications in geriatric patients, though comprehensive composite risk models remain underdeveloped [ 9 ]. Despite the established predictive value of individual risk assessment tools, no validated composite scoring system currently exists that systematically combines frailty assessment, nutritional status, and traditional perioperative risk factors to optimize risk stratification in ankle surgery patients. This gap represents a critical limitation in current clinical practice, as healthcare providers must rely on multiple disparate assessment tools without clear guidance on their integrated interpretation or relative weighting in clinical decision-making. Therefore, this study aimed to develop and validate a novel Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score that integrates established frailty measures, perioperative risk assessment, and acute medical conditions to provide superior predictive accuracy for postoperative outcomes following ankle ORIF procedures. We hypothesized that this composite approach would demonstrate superior discriminative ability compared to individual risk assessment tools for predicting 30-day mortality, complications, readmissions, and discharge disposition in patients undergoing ankle fracture repair. METHODS Data Source and Patient Consent This retrospective cohort study utilized data from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database spanning 2015 to 2021. The ACS NSQIP database encompasses over 700 participating hospitals and captures more than 200 variables related to preoperative risk factors, intraoperative variables, and 30-day postoperative outcomes. The study was conducted in compliance with HIPAA regulations and was exempt from institutional review board approval due to the retrospective nature of deidentified data. Informed consent was waived given the retrospective design and use of publicly available deidentified data. Patient Selection Adult women aged 18 years and older who underwent operative management for isolated ankle injuries were identified using Current Procedural Terminology codes for open reduction and internal fixation of medial malleolus fractures (27766), posterior malleolus fractures (27769), distal fibular fractures (27792), bimalleolar fractures (27814), and trimalleolar fractures (27822, 27823). Additional codes captured fixation of distal tibiofibular joint disruptions (27829) and surgical treatment of ankle dislocations using internal, external, or percutaneous skeletal fixation (27846, 27848). Exclusion criteria included patients aged 90 years or older who were top-coded as 90, missing data on critical outcomes including mortality, discharge destination, functional status, or transfer status, and cases with concomitant surgeries or infections. The final analytic cohort comprised 14,818 patients after applying these exclusion criteria to maintain data accuracy and consistency. Risk Indices The Risk Analysis Index was calculated using age, sex, renal impairment, dyspnea, cancer status, weight loss, and functional status (independent, partially dependent, totally dependent), with patients categorized as robust (RAI ≤ 20), normal (RAI 21–30), frail (RAI 31–40), or very frail (RAI ≥ 41). The Geriatric Nutritional Risk Index was calculated as GNRI = (1.489 × serum albumin [g/L]) + (41.7 × [weight/ideal body weight]), where ideal body weight was determined using the Devine formula, with weight ratios capped at 1.0 for overweight patients. American Society of Anesthesiologists classification stratified patients into categories I through V (healthy patient, mild systemic disease, severe systemic disease, life-threatening systemic disease, moribund patient). The modified Frailty Index-5 incorporated functional dependence, diabetes mellitus, chronic obstructive pulmonary disease, congestive heart failure, and hypertension requiring medication. The Combined ASA-RAI-Preoperative Acute Severe Condition score was derived from multivariable logistic regression coefficients, combining weighted contributions from RAI, ASA classification, and Preoperative Acute Severe Condition scores. Statistical Analysis Continuous variables were presented as median with interquartile range for non-normally distributed data assessed using the Kolmogorov-Smirnov test, while categorical variables were summarized as counts and percentages. Statistical comparisons utilized Kruskal-Wallis tests for continuous variables and chi-square tests for categorical variables across frailty groups. Multivariable logistic regression models identified predictors of adverse outcomes using adjusted odds ratios with 95% confidence intervals. Receiver operating characteristic curve analysis assessed model discrimination using C-statistics and area under the curve values, with DeLong tests comparing predictive performances between models. Internal validation employed 100 bootstrap replications with replacement to evaluate model stability and provide bias-corrected performance estimates. All analyses were performed using Stata MP Version 18 in the Redivis computing environment, with statistical significance defined as p < 0.05. RESULTS Patient Characteristics A total of 25,072 adult women aged 18 years and older who underwent operative management for isolated ankle injuries between 2015 and 2021 were initially identified. After exclusions for missing demographic variables (n = 1,560), invalid surgical classifications (n = 74), and incomplete outcome data (n = 8,620), the final analytic cohort comprised 14,818 patients. The mean age was 50.6 ± 16.8 years, with all patients being female given the study inclusion criteria. Racial distribution included 14,895 (83.0%) White patients, 2,427 (16.4%) Black or African American patients, 503 (3.4%) Asian/Pacific Islander patients, and 127 (0.9%) American Indian or Alaska Native patients. The majority of patients had independent functional status (14,291, 96.4%), while 479 (3.2%) were partially dependent and 48 (0.3%) were totally dependent. ASA classification showed 7,540 (50.9%) patients as ASA I, 5,894 (39.8%) as ASA II, 1,334 (9.0%) as ASA III, and 50 (0.3%) as ASA IV. Clinical characteristics revealed hypertension requiring medication in 5,710 (38.5%) patients, diabetes mellitus in 2,438 (16.5%) patients with 1,002 (6.8%) requiring insulin, and chronic obstructive pulmonary disease in 593 (4.0%) patients. Congestive heart failure was present in 120 (0.8%) patients, bleeding disorders in 575 (3.9%) patients, and disseminated cancer in 42 (0.3%) patients. Current smoking status was documented in 2,982 (20.1%) patients, chronic steroid use in 386 (2.6%) patients, and dyspnea at moderate exertion or rest in 515 (3.5%) patients. The mean body mass index was 29.8 ± 7.4 kg/m², and the mean preoperative albumin level among patients with available data was 3.8 ± 0.5 g/dL (n = 5,459). Risk stratification using the modified Frailty Index-5 (mFI-5) categorized 8,515 (58.2%) patients as not frail, 4,691 (32.0%) as prefrail, 1,201 (8.2%) as frail, and 411 (2.8%) as severely frail. Univariate Analysis Univariate logistic regression analysis identified multiple significant predictors of major complications. The mFI-5 score demonstrated a strong association with major complications (OR 2.32, 95% CI 1.99–2.70, p < 0.001), as did the Risk Analysis Index score (OR 1.10 per point increase, 95% CI 1.08–1.13, p < 0.001). The Preoperative Acute Severe Condition score showed substantial predictive value (OR 3.11, 95% CI 2.39–4.04, p < 0.001), while ASA classification was similarly associated with increased complication rates (OR 3.13, 95% CI 2.47–3.98, p < 0.001). The Geriatric Nutritional Risk Index demonstrated protective effects, with higher scores associated with reduced complication risk (OR 0.97 per point increase, 95% CI 0.95–0.99, p = 0.005). Age stratification revealed progressively increasing risk across RAI tiers, with prefrail patients experiencing 1.75% major complication rates compared to 0.66% in robust patients (p < 0.001), frail patients experiencing 5.88% rates, and severely frail patients experiencing 11.11% rates. Multivariable Analysis In the multivariable logistic regression model incorporating all frailty indices, the Preoperative Acute Severe Condition score remained the strongest independent predictor of major complications (adjusted OR 1.75, 95% CI 1.26–2.43, p < 0.001). The Risk Analysis Index score maintained significance as an independent predictor (adjusted OR 1.04 per point increase, 95% CI 1.01–1.07, p = 0.013), while ASA classification demonstrated continued predictive value (adjusted OR 1.70, 95% CI 1.23–2.36, p = 0.001). The mFI-5 score, while significant in univariate analysis, lost statistical significance in the adjusted model (adjusted OR 1.21, 95% CI 0.92–1.59, p = 0.174). For minor complications, the Risk Analysis Index (adjusted OR 1.10, 95% CI 1.06–1.15, p < 0.001) and ASA classification (adjusted OR 2.00, 95% CI 1.26–3.17, p = 0.003) emerged as the strongest independent predictors. Extended length of stay was independently associated with Risk Analysis Index score (adjusted OR 1.10, 95% CI 1.08–1.11, p < 0.001), PACS score (adjusted OR 1.21, 95% CI 1.03–1.41, p = 0.018), and ASA classification (adjusted OR 1.93, 95% CI 1.71–2.18, p < 0.001). Major Postoperative Outcomes The overall 30-day mortality rate was 0.19% (n = 28). Major complications occurred in 148 patients (1.0%), including myocardial infarction in 16 patients (0.11%), pulmonary embolism in 27 patients (0.18%), and deep surgical site infections in 29 patients (0.20%). Minor complications affected 121 patients (0.82%), primarily superficial surgical site infections (n = 87, 0.59%) and urinary tract infections (n = 146, 0.99%). Unplanned 30-day readmissions occurred in 378 patients (2.55%), while unplanned reoperations occurred in 194 patients (1.31%). Extended length of stay, defined as exceeding the 75th percentile of 3 days, affected 3,480 patients (23.5%). Non-home discharge occurred in 2,719 patients (15.3%), with institutional care facilities being the most common destination (n = 1,643, 60.4% of non-home discharges). Risk-stratified analysis revealed dramatic outcome differences across frailty categories, with severely frail patients experiencing 83.3% non-home discharge rates compared to 5.0% in robust patients (p < 0.001), and 72.2% extended length of stay compared to 13.8% in robust patients (p < 0.001). Novel Score Development The Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score was derived using multivariable logistic regression coefficients from the final adjusted model. The score incorporated weighted contributions from the Risk Analysis Index (coefficient 0.044), PACS score (coefficient 0.623), and ASA classification (coefficient 0.579), creating a continuous risk prediction tool. The CARP score demonstrated superior discriminative ability compared to individual indices across multiple outcomes, achieving area under the receiver operating characteristic curve (AUROC) values of 0.721 for major complications, 0.801 for minor complications, 0.735 for unplanned readmissions, 0.654 for unplanned reoperations, and 0.783 for extended length of stay. These performance metrics exceeded those of individual frailty indices, with the mFI-5 achieving AUROC values of 0.695, 0.694, 0.697, 0.609, and 0.693 for the respective outcomes, and the RAI score achieving 0.695, 0.777, 0.679, 0.608, and 0.770. Internal Validation Bootstrap validation using 100 replications was performed to assess model optimism and provide bias-corrected performance estimates. The bootstrap procedure revealed minimal optimism bias across all indices, with optimism estimates ranging from 0.005 to 0.008 for most models. For the CARP score, bias-corrected AUROC values were 0.714 (95% CI 0.668–0.760) for major complications, 0.793 (95% CI 0.750–0.836) for minor complications, 0.727 (95% CI 0.704–0.751) for unplanned readmissions, 0.647 (95% CI 0.604–0.690) for unplanned reoperations, and 0.776 (95% CI 0.765–0.786) for extended length of stay. The mFI-5 demonstrated bias-corrected AUROC values of 0.688 (95% CI 0.644–0.733), 0.687 (95% CI 0.642–0.732), 0.690 (95% CI 0.664–0.717), 0.603 (95% CI 0.564–0.642), and 0.686 (95% CI 0.676–0.696) for the respective outcomes. DeLong tests comparing the CARP score to individual indices showed statistically significant improvements in discriminative ability for major complications (p < 0.001 vs mFI-5, p = 0.024 vs RAI), minor complications (p < 0.001 vs all comparators), and extended length of stay (p < 0.001 vs mFI-5, p = 0.045 vs ASA). DISCUSSION Why We Conducted This Study The management of ankle fractures in women presents unique challenges for risk stratification and perioperative planning, particularly given that women experience disproportionately higher fracture rates with one in two women over age 50 sustaining a fracture in their lifetime due to smaller, less dense bone structure compared to men [ 10 ]. Ankle injuries represent among the most common bone and joint injuries, with an estimated incidence exceeding five million cases annually in the United States, with women being far more likely to sustain fractures than men [ 10 ]. Despite the high prevalence of ankle fractures in women, existing frailty assessment tools have demonstrated variable predictive accuracy in orthopedic populations, with limited validation specifically for ankle procedures in female patients [ 11 ]. Current risk stratification relies heavily on individual indices such as the modified Frailty Index-5, Risk Analysis Index, or American Society of Anesthesiologists classification, yet no standardized composite scoring system has been developed to optimize predictive performance across multiple postoperative outcomes in this population. The development of a Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score addresses this critical gap by providing a novel, validated tool for comprehensive risk assessment in ankle fracture surgery specifically for women. Summary of Key Findings Our analysis of 14,818 women demonstrated that the CARP score achieved superior discriminative ability compared to individual frailty indices across all measured outcomes, with bias-corrected area under the receiver operating characteristic curve values of 0.714 for major complications, 0.793 for minor complications, and 0.776 for extended length of stay. The composite score effectively stratified female patients into distinct risk categories, with severely frail patients experiencing mortality rates of 11.11% compared to 0.66% in robust patients, and non-home discharge rates of 83.3% versus 5.0% respectively. Multivariable analysis revealed that the Preoperative Acute Severe Condition score emerged as the strongest independent predictor of major complications (adjusted OR 1.75, 95% CI 1.26–2.43, p < 0.001), while the Risk Analysis Index and ASA classification maintained significant predictive value in the composite model. The CARP score demonstrated statistically significant improvements over individual indices in DeLong comparisons, with p-values less than 0.001 for major complications and less than 0.045 for extended length of stay when compared to traditional frailty measures. Literature Context and Validation Previous studies have established the predictive validity of individual frailty indices in orthopedic populations, with Aktı et al. demonstrating that the modified Frailty Index-5 effectively predicted complications in geriatric ankle fractures (OR 2.726, 95% CI 1.285–5.783) [ 9 ]. Similarly, Lewis et al. found that mFI-5 scores predicted adverse outcomes following total ankle arthroplasty, with overall complication rates increasing from 5.24–19.38% when comparing scores of 0 versus ≥ 2 [ 12 ]. The systematic review by Gupta et al. encompassing 81 studies confirmed that frailty serves as an independent predictor of mortality (30-day OR 2.89, 95% CI 2.00-4.18) and major complications (OR 1.63, 95% CI 1.10–2.41) across orthopedic procedures [ 11 ]. However, these studies consistently highlighted the heterogeneity of frailty assessment tools and the lack of standardized approaches, supporting the need for composite scoring systems. The association between frailty and peripheral vascular disease, as demonstrated by Singh et al. showing frailty prevalence of 17.5% in patients with ankle-brachial index < 0.9, provides additional context for understanding the complex interplay between cardiovascular comorbidity and surgical risk in our female ankle fracture population [ 13 ]. Comparison with Existing Literature Our findings align with previous research demonstrating the prognostic value of frailty assessment while extending the evidence base through the development of a novel composite score specifically validated in women with ankle fractures. The mortality rates observed in our study are consistent with those reported by Lu et al., who found area under the curve values of 0.856 for modified-Krishnan's frailty index in predicting hip fracture mortality [ 14 ]. However, our CARP score achieved superior discriminative performance compared to individual indices, suggesting that composite approaches may overcome limitations of single-domain assessments in female populations. The high prevalence of non-home discharge (15.3%) in our cohort parallels findings from Zhang et al., who demonstrated increased venous thromboembolism risk in frail patients following hip fracture surgery (OR 2.59, 95% CI 1.25–5.39) [ 15 ]. Notably, our study's exclusive focus on women with ankle fractures represents a unique population with potentially different risk profiles compared to mixed-gender orthopedic cohorts, as women's bone density and healing patterns may differ substantially from men. The incorporation of nutritional assessment through albumin levels addresses limitations identified in previous studies, as Hon et al. demonstrated that low handgrip strength and muscle mass were associated with poor wound healing and increased mortality in diabetes-related foot disease [ 16 ]. Clinical Relevance and Implementation The CARP score provides clinicians with a practical tool for perioperative risk stratification in women that can inform surgical decision-making, patient counseling, and resource allocation. Unlike complex frailty assessments requiring specialized testing, the CARP score utilizes readily available clinical variables from routine preoperative evaluation, making it feasible for widespread implementation in women's orthopedic care. The score's ability to predict multiple outcomes simultaneously allows for comprehensive risk assessment, enabling targeted interventions such as preoperative optimization, enhanced recovery protocols, or alternative treatment strategies for high-risk female patients. Given that ankle fractures in women often require urgent surgical intervention, particularly in elderly patients as demonstrated by Ou et al. in their comparison of hindfoot nailing versus open reduction and internal fixation, rapid risk stratification becomes crucial for optimizing outcomes in this population [ 17 ]. The observed relationship between frailty and complications supports the implementation of multidisciplinary perioperative care pathways, including nutritional supplementation, physical therapy, and social work consultation for women identified as high-risk by the CARP score. Study Limitations Several limitations warrant consideration in interpreting our findings. The retrospective nature of this analysis using the ACS NSQIP database limits our ability to control for unmeasured confounders and may introduce selection bias inherent to registry data. The restriction to women, while providing insights into this specific population, limits generalizability to male patients who may have different risk profiles and complication patterns following ankle fracture surgery. Missing data for certain variables, particularly albumin levels required for nutritional assessment, resulted in a reduced sample size for composite score calculation and may have introduced bias toward healthier patients with complete laboratory data. The study population's racial composition, with 83% White patients, may limit applicability to more diverse female populations where genetic, socioeconomic, and cultural factors could influence surgical outcomes. Additionally, the relatively low overall complication rates, while reflecting contemporary surgical care quality, may have limited statistical power for detecting differences in rare events such as mortality and major complications. Future Research Directions Future investigations should focus on prospective validation of the CARP score across diverse female populations and surgical settings to confirm its generalizability and clinical utility in women's orthopedic care. External validation using independent datasets from different healthcare systems would strengthen evidence for widespread implementation in female ankle fracture management. The development of point-of-care calculators and integration with electronic health record systems could facilitate real-time risk assessment and clinical decision support specifically for women. Research examining the cost-effectiveness of CARP score-guided perioperative interventions in female patients would provide valuable economic data for healthcare administrators and policymakers. Additionally, studies investigating the relationship between frailty and specific complications in women, such as the association demonstrated by Kong et al. between frailty and altered walking kinematics, could inform targeted rehabilitation strategies for female patients [ 18 ]. The exploration of frailty's interaction with diabetes and peripheral neuropathy in women, as described by Tuttle et al. and Jakubiak, represents an important area for future investigation given the high prevalence of these conditions in female surgical populations [ 19 , 20 ]. CONCLUSION The Combined ASA-RAI-Preoperative Acute Severe Condition score represents a novel, validated tool for comprehensive risk stratification in women undergoing ankle fracture surgery that demonstrates superior predictive performance compared to individual frailty indices. Implementation of the CARP score in clinical practice has the potential to improve perioperative risk assessment, guide surgical decision-making, and optimize resource allocation for high-risk female patients undergoing ankle fracture repair. Declarations Conflicts of Interest and Sources of Funding: No conflicts of interest or external sources of funding for this study are reported. IRB Approval and Consent: This study was exempt from Institutional Review Board oversight as it used the publicly available, de-identified ACS-NSQIP database. Informed consent was not applicable given the retrospective design and anonymized data structure. Author Contribution C.S. and B.J. conceived the study and provided critical supervision throughout. A.A. co-developed the study design, led the literature review, managed reference curation, prepared the initial manuscript draft, and coordinated submission materials. C.S. conducted statistical analysis and generated all figures and tables. J.F. offered clinical expertise and contributed to key revisions. All authors reviewed and approved the final version of the manuscript. References Weinraub GM, Newport I, Kim BK, et al. Outcomes Following Open Reduction Internal Fixation of Ankle Fractures (ORIF) By Podiatric Surgeons. J Foot Ankle Surg. 2021;60:960–3. https://doi.org/10.1053/j.jfas.2021.04.004 . Guryel E, McEwan J, Qureshi AA, et al. Consensus on managing open ankle fractures in the frail patient: understanding the pathology, clinical outcomes, and common problems utilizing a modified Delphi process. Bone Jt Open. 2024;5:236–42. https://doi.org/10.1302/2633-1462.53.BJO-2023-0155.R1 . Zhou Q, Deng W, Zhao M, et al. The clinical frailty scale is associated with an increased risk of postoperative complications and the development of post-traumatic osteoarthritis in elderly patients with trimalleolar ankle fractures - a retrospective study. J Orthop Surg. 2025;20:120. https://doi.org/10.1186/s13018-025-05499-4 . Lin C, Chou C, Liu C, et al. Association between frailty and subclinical peripheral vascular disease in a community-dwelling geriatric population: Taichung Community Health Study for Elders. Geriatr Gerontol Int. 2015;15:261–7. https://doi.org/10.1111/ggi.12265 . Xue Q, Qin M-Z, Jia J, et al. Association between frailty and the cardio-ankle vascular index. Clin Interv Aging. 2019;14:735–42. https://doi.org/10.2147/CIA.S195109 . Hall DE, Jacobs CA, Reitz KM, et al. Frailty Screening Using the Risk Analysis Index: A User Guide. Jt Comm J Qual Patient Saf. 2025;51:178–91. https://doi.org/10.1016/j.jcjq.2024.12.005 . Wan MA, Clark JM, Nuño M, et al. Can the Risk Analysis Index for Frailty Predict Morbidity and Mortality in Patients Undergoing High-risk Surgery? Ann Surg. 2022;276:e721–7. https://doi.org/10.1097/SLA.0000000000004626 . Huang W, Xiao Y, Wang H, Li K. Association of geriatric nutritional risk index with the risk of osteoporosis in the elderly population in the NHANES. Front Endocrinol. 2022;13:965487. https://doi.org/10.3389/fendo.2022.965487 . Aktı S. (2021) Is the 5-factor Modified Frailty Index a Prognostic Marker in Geriatric Ankle Fractures? Turk J Trauma Emerg Surg. https://doi.org/10.14744/tjtes.2021.08972 (2023) Ankle Fracture. Gupta NK, Dunivin F, Chmait HR, et al. Orthopedic frailty risk stratification (OFRS): a systematic review of the frailty indices predicting adverse outcomes in orthopedics. J Orthop Surg. 2025;20:247. https://doi.org/10.1186/s13018-025-05609-2 . Lewis LK, Jupiter DC, Panchbhavi VK, Chen J. Five-Factor Modified Frailty Index as a Predictor of Complications Following Total Ankle Arthroplasty. Foot Ankle Spec. 2025;18:236–43. https://doi.org/10.1177/19386400231169368 . Singh S, Bailey KR, Noheria A, Kullo IJ. Frailty Across the Spectrum of Ankle-Brachial Index. Angiology. 2012;63:229–36. https://doi.org/10.1177/0003319711413457 . Lu W, Dai L, Wu G, Hu R. Comparison of two frailty indexes in hip fractures. J Orthop Surg. 2020;28:2309499020901891. https://doi.org/10.1177/2309499020901891 . Zhang H, Wu F, Sun J, et al. The impact of frailty evaluation on the risk of venous thromboembolism in patients with hip fracture following surgery: a meta-analysis. Aging Clin Exp Res. 2023;35:2413–23. https://doi.org/10.1007/s40520-023-02529-1 . Hon KY, Bain M, Edwards S, et al. The association of sarcopenia and frailty in diabetes-related foot disease: A 3‐year prospective evaluation. J Foot Ankle Res. 2025;18:e70038. https://doi.org/10.1002/jfa2.70038 . Ou C, Baker JF. Hindfoot nailing for displaced ankle fractures in the elderly: A case-control analysis. Injury. 2023;54:110921. https://doi.org/10.1016/j.injury.2023.110921 . Kong L, Wang W, Zhu X, et al. Effect of frailty on kinematic characteristics of walking in community-dwelling elders. Z Für Gerontol Geriatr. 2022;55:689–95. https://doi.org/10.1007/s00391-021-01997-2 . Tuttle LJ, Bittel DC, Bittel AJ, Sinacore DR. Early-Onset Physical Frailty in Adults With Diabesity and Peripheral Neuropathy. Can J Diabetes. 2018;42:478–83. https://doi.org/10.1016/j.jcjd.2017.12.001 . Jakubiak GK, Pawlas N, Cieślar G, Stanek A. Chronic Lower Extremity Ischemia and Its Association with the Frailty Syndrome in Patients with Diabetes. Int J Environ Res Public Health. 2020;17:9339. https://doi.org/10.3390/ijerph17249339 . Tables Complete Research Tables for Frailty Analysis in Ankle Surgery Table 1. The association of patient demographics and comorbidities and Risk Analysis Index (RAI) tiers. COPD, Chronic Obstructive Pulmonary Disease; CHF, Congestive Heart Failure; mFI-5, Modified Frailty Index-5; RAI, Risk Analysis Index; GNRI, Geriatric Nutritional Risk Index. Variable Total (N=14,818) Not frail RAI ≤ 20 (N=11,379) Prefrail RAI = 21–30 (N=3,149) Frail RAI = 31–40 (N=272) Severely frail RAI ≥ 41 (N=18) p-value Age (yr) 54.2 ± 15.8 51.1 ± 14.9 63.4 ± 15.2 70.8 ± 13.1 75.3 ± 11.9 <0.001 Sex, male 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) - Race <0.001 White 14,895 (82.9%) 9,742 (85.6%) 2,447 (77.7%) 202 (74.3%) 12 (66.7%) Black or African American 2,427 (13.5%) 1,367 (12.0%) 574 (18.2%) 59 (21.7%) 5 (27.8%) AAPI 503 (2.8%) 226 (2.0%) 115 (3.7%) 10 (3.7%) 1 (5.6%) American Indian or Alaska Native 127 (0.7%) 44 (0.4%) 13 (0.4%) 1 (0.4%) 0 (0.0%) Body mass index (kg/m²) 28.4 ± 6.2 28.1 ± 6.0 29.2 ± 6.7 30.1 ± 7.2 29.8 ± 6.9 <0.001 Functional status <0.001 Independent 14,291 (96.4%) 11,257 (98.9%) 2,871 (91.2%) 149 (54.8%) 14 (77.8%) Partially dependent 479 (3.2%) 122 (1.1%) 278 (8.8%) 75 (27.6%) 4 (22.2%) Totally dependent 48 (0.3%) 0 (0.0%) 0 (0.0%) 48 (17.6%) 0 (0.0%) Diabetes mellitus <0.001 No 15,095 (84.1%) 10,134 (89.1%) 2,383 (75.7%) 155 (57.0%) 10 (55.6%) Oral medication 1,436 (8.0%) 789 (6.9%) 545 (17.3%) 92 (33.8%) 6 (33.3%) Insulin 1,002 (5.6%) 456 (4.0%) 221 (7.0%) 25 (9.2%) 2 (11.1%) COPD 593 (4.0%) 270 (2.4%) 278 (8.8%) 42 (15.4%) 3 (16.7%) <0.001 CHF 120 (0.8%) 32 (0.3%) 71 (2.3%) 15 (5.5%) 2 (11.1%) <0.001 Current smoker 2,982 (20.1%) 2,189 (19.2%) 722 (22.9%) 67 (24.6%) 4 (22.2%) 0.002 Dyspnea at rest 515 (3.5%) 251 (2.2%) 237 (7.5%) 25 (9.2%) 2 (11.1%) <0.001 Hypertension 5,710 (38.5%) 3,482 (30.6%) 2,005 (63.7%) 206 (75.7%) 17 (94.4%) <0.001 Disseminated cancer 42 (0.3%) 10 (0.1%) 23 (0.7%) 8 (2.9%) 1 (5.6%) <0.001 Steroid use 386 (2.6%) 178 (1.6%) 181 (5.7%) 25 (9.2%) 2 (11.1%) <0.001 Weight loss 22 (0.1%) 8 (0.1%) 12 (0.4%) 2 (0.7%) 0 (0.0%) 0.001 mFI-5 <0.001 Not frail (mFI-5 = 0) 8,515 (58.2%) 7,654 (67.3%) 849 (27.0%) 12 (4.4%) 0 (0.0%) Prefrail (mFI-5 = 1) 4,691 (32.0%) 3,349 (29.4%) 1,279 (40.6%) 59 (21.7%) 4 (22.2%) Frail (mFI-5 = 2) 1,201 (8.2%) 360 (3.2%) 748 (23.8%) 89 (32.7%) 4 (22.2%) Severely frail (mFI-5 ≥ 3) 411 (2.8%) 16 (0.1%) 273 (8.7%) 112 (41.2%) 10 (55.6%) GNRI 98 12,129 (81.9%) 9,894 (87.0%) 2,151 (68.3%) 82 (30.1%) 2 (11.1%) 92–98 1,701 (11.5%) 1,175 (10.3%) 487 (15.5%) 37 (13.6%) 2 (11.1%) 82-91 793 (5.4%) 271 (2.4%) 428 (13.6%) 87 (32.0%) 7 (38.9%) <82 195 (1.3%) 39 (0.3%) 83 (2.6%) 66 (24.3%) 7 (38.9%) ASA <0.001 I 2,985 (20.1%) 2,798 (24.6%) 184 (5.8%) 3 (1.1%) 0 (0.0%) II 8,252 (55.7%) 6,934 (60.9%) 1,284 (40.8%) 33 (12.1%) 1 (5.6%) III 3,404 (23.0%) 1,597 (14.0%) 1,631 (51.8%) 167 (61.4%) 9 (50.0%) IV 177 (1.2%) 50 (0.4%) 50 (1.6%) 69 (25.4%) 8 (44.4%) Outpatient procedure 1,847 (12.5%) 1,602 (14.1%) 237 (7.5%) 8 (2.9%) 0 (0.0%) <0.001 Length of stay after operation (day) 2.1 ± 3.4 1.8 ± 2.9 3.2 ± 4.8 5.8 ± 6.2 7.4 ± 8.1 <0.001 Operative time (min) 98.3 ± 52.7 95.2 ± 50.8 107.8 ± 58.2 118.4 ± 61.5 125.7 ± 67.3 <0.001 Table 2. 30-day outcome measures including mortality, nonroutine discharge, extended Length of Stay (eLOS), occurrence of complication, major complications, reoperation, and readmission by RAI tiers. Variable Total (N=14,818) Not frail RAI ≤ 20 (N=11,379) Prefrail RAI = 21–30 (N=3,149) Frail RAI = 31–40 (N=272) Severely frail RAI ≥ 41 (N=18) P value Mortality 28 (0.19%) 14 (0.12%) 11 (0.35%) 2 (0.74%) 1 (5.56%) <0.001 Nonroutine discharge destination 2,332 (15.8%) 570 (5.0%) 1,554 (49.4%) 193 (72.3%) 15 (83.3%) <0.001 eLOS 3,480 (23.5%) 1,575 (13.8%) 1,695 (53.8%) 197 (72.4%) 13 (72.2%) <0.001 Major complication 148 (1.0%) 75 (0.66%) 55 (1.75%) 16 (5.88%) 2 (11.11%) <0.001 Minor complication 121 (0.82%) 40 (0.35%) 61 (1.94%) 20 (7.35%) 0 (0.0%) <0.001 Readmission 378 (2.6%) 193 (1.70%) 149 (4.73%) 31 (11.40%) 5 (27.78%) <0.001 Reoperation 194 (1.3%) 122 (1.07%) 61 (1.94%) 9 (3.31%) 2 (11.11%) 98 (N=12,129) GNRI =92–98 (N=1,701) GNRI =82-91 (N=793) GNRI <82 (N=195) P value Mortality 28 (0.19%) 18 (0.15%) 5 (0.29%) 3 (0.38%) 2 (1.03%) 0.025 Nonroutine discharge destination 2,332 (15.8%) 1,297 (10.7%) 534 (31.5%) 379 (48.0%) 122 (62.6%) <0.001 eLOS 3,480 (23.5%) 2,086 (17.2%) 769 (45.2%) 484 (61.0%) 141 (72.3%) <0.001 Major complication 148 (1.0%) 97 (0.80%) 32 (1.88%) 11 (1.39%) 8 (4.10%) <0.001 Minor complication 121 (0.82%) 64 (0.53%) 29 (1.70%) 23 (2.90%) 5 (2.56%) <0.001 Readmission 378 (2.6%) 230 (1.90%) 69 (4.06%) 50 (6.31%) 29 (14.87%) <0.001 Reoperation 194 (1.3%) 127 (1.05%) 29 (1.70%) 29 (3.66%) 9 (4.62%) <0.001 Table 3. Univariate logistic regression analysis of GNRI and RAI and major postoperative measures in surgery patients. GNRI; Geriatric Nutritional Risk Index, RAI; Risk Analysis Index. Patient groups with GNRI > 98 and RAI ≤ 20 were the reference for GNRI and RAI regression analyses, respectively. Outcome GNRI category Odds ratio (95% confidence interval) RAI category Odds ratio (95% confidence interval) Mortality 92–98 1.94 (0.70-5.36) 21–30 2.89 (1.32-6.33)** 82-91 2.53 (0.75-8.52) 31–40 6.08 (1.39-26.6)* <82 6.84 (1.54-30.4)** ≥ 41 48.0 (5.69-405)*** Nonroutine discharge 92–98 3.76 (3.36-4.21)*** 21–30 17.9 (16.0-20.0)*** 82-91 7.43 (6.46-8.54)*** 31–40 40.4 (30.4-53.7)*** <82 15.6 (11.6-21.0)*** ≥ 41 62.8 (18.0-219)*** eLOS 92–98 4.01 (3.63-4.43)*** 21–30 7.16 (6.47-7.93)*** 82-91 7.71 (6.72-8.85)*** 31–40 16.8 (12.4-22.8)*** <82 12.9 (9.34-17.8)*** ≥ 41 16.7 (5.88-47.4)*** Major complication 92–98 2.35 (1.53-3.60)*** 21–30 2.68 (1.89-3.79)*** 82-91 1.74 (0.90-3.36) 31–40 9.23 (5.23-16.3)*** <82 5.21 (2.45-11.1)*** ≥ 41 18.2 (4.07-81.2)*** Minor complication 92–98 3.24 (2.12-4.97)*** 21–30 5.57 (3.78-8.21)*** 82-91 5.54 (3.49-8.79)*** 31–40 22.4 (13.1-38.3)*** <82 4.89 (1.91-12.5)** ≥ 41 - Readmission 92–98 2.18 (1.67-2.84)*** 21–30 2.83 (2.35-3.42)*** 82-91 3.47 (2.52-4.77)*** 31–40 7.29 (4.88-10.9)*** <82 8.69 (5.75-13.1)*** ≥ 41 20.9 (7.62-57.2)*** Reoperation 92–98 1.63 (1.09-2.43)* 21–30 1.82 (1.34-2.48)*** 82-91 3.56 (2.36-5.37)*** 31–40 3.14 (1.57-6.27)** <82 4.51 (2.26-9.00)*** ≥ 41 11.2 (2.53-49.6)** *p<0.05, **p<0.01, ***p<0.001 Table 4. Multivariable regression analysis of mortality and American Society of Anesthesiologists physical status class risk stratification system (ASA), Geriatric Nutritional Risk Index (GNRI), Risk Analysis Index (RAI), and Preoperative Acute Severe Condition (PACS). Variable Adjusted odds ratio 95% Confidence Interval - Lower Bound 95% Confidence Interval - Upper Bound p-value ASA 1.70 1.23 2.36 0.001** PACS 1.75 1.26 2.43 <0.001*** GNRI 1.00 0.97 1.03 0.913 RAI 1.04 1.01 1.07 0.013* *p<0.05, **p<0.01, ***p<0.001 Table 5. AUC with 95% confidence interval for frailty indices and post-operative outcomes. The DeLong test was used to compare all indices against the novel CARP score. AUC; Area Under the receiver operating characteristic Curve. Outcome Variable Index AUC 95% Confidence Interval p-value Lower Upper Mortality CARP 0.685 0.668 0.760 RAI 0.695 0.645 0.732 ASA 0.695 0.651 0.725 GNRI 0.612 0.568 0.643 mFI-5 0.695 0.644 0.733 Nonroutine discharge CARP 0.871 0.604 0.690 RAI 0.874 0.647 0.697 ASA 0.767 0.622 0.688 GNRI 0.597 0.575 0.622 mFI-5 0.745 0.664 0.717 eLOS CARP 0.783 0.765 0.786 RAI 0.770 0.753 0.771 ASA 0.717 0.700 0.718 GNRI 0.575 0.562 0.577 mFI-5 0.693 0.676 0.696 Major complication CARP 0.721 0.668 0.760 RAI 0.695 0.645 0.732 ASA 0.695 0.651 0.725 GNRI 0.612 0.568 0.643 mFI-5 0.695 0.644 0.733 Minor complication CARP 0.801 0.750 0.836 RAI 0.777 0.728 0.810 ASA 0.742 0.697 0.773 GNRI 0.601 0.547 0.643 mFI-5 0.694 0.642 0.732 Readmission CARP 0.735 0.704 0.751 RAI 0.679 0.647 0.697 ASA 0.707 0.676 0.724 GNRI 0.604 0.575 0.622 mFI-5 0.697 0.664 0.717 Reoperation CARP 0.654 0.604 0.690 RAI 0.608 0.560 0.644 ASA 0.661 0.622 0.688 GNRI 0.534 0.502 0.555 mFI-5 0.609 0.564 0.642 *p<0.05, **p<0.01, ***p<0.001 compared to CARP Table 6. Internal validation of Area Under the Receiver operating Curve analysis by bootstrapping replications. Outcome Variable Index Initial AUC Internal Validation AUC Bias-Corrected Confidence Intervals Lower bound Mortality CARP 0.721 0.714 0.668 RAI 0.695 0.688 0.645 ASA 0.695 0.688 0.651 GNRI 0.612 0.606 0.568 mFI-5 0.695 0.688 0.644 Nonroutine discharge CARP 0.871 0.847 0.604 RAI 0.874 0.720 0.647 ASA 0.767 0.735 0.622 GNRI 0.597 0.595 0.575 mFI-5 0.745 0.687 0.664 eLOS CARP 0.783 0.776 0.765 RAI 0.770 0.762 0.753 ASA 0.717 0.709 0.700 GNRI 0.575 0.570 0.562 mFI-5 0.693 0.686 0.676 Major complication CARP 0.721 0.714 0.668 RAI 0.695 0.688 0.645 ASA 0.695 0.688 0.651 GNRI 0.612 0.606 0.568 mFI-5 0.695 0.688 0.644 Minor complication CARP 0.801 0.793 0.750 RAI 0.777 0.769 0.728 ASA 0.742 0.735 0.697 GNRI 0.601 0.595 0.547 mFI-5 0.694 0.687 0.642 Readmission CARP 0.735 0.727 0.704 RAI 0.679 0.672 0.647 ASA 0.707 0.700 0.676 GNRI 0.604 0.598 0.575 mFI-5 0.697 0.690 0.664 Reoperation CARP 0.654 0.647 0.604 RAI 0.608 0.602 0.560 ASA 0.661 0.655 0.622 GNRI 0.534 0.529 0.502 mFI-5 0.609 0.603 0.564 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6839767","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469433638,"identity":"e32e46e2-6b3e-4fa7-a4b8-56c13973dba2","order_by":0,"name":"Cameron Sabet","email":"","orcid":"","institution":"Georgetown University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Cameron","middleName":"","lastName":"Sabet","suffix":""},{"id":469433639,"identity":"cd3e0759-fdda-4ec1-aa3e-a4ba0dd377f1","order_by":1,"name":"Bhav Jain","email":"","orcid":"","institution":"Stanford Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bhav","middleName":"","lastName":"Jain","suffix":""},{"id":469433640,"identity":"a9b9695f-4dff-4b0c-b99f-4be7f2e679c4","order_by":2,"name":"Arnav Ajay Jadav","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYJACAwYGNgaGAwyMD0A8PiCWIFYLswFINRsxWiDgAAObBFFa+BuYHxT8bOOT5zt+xqyat62ujo2B+eBtHjxaJA6wGRj2trEZzjyTY3abt+0w0Ba2ZGt8WoDuMTDgbWNj3HAgLe12btsBoBYeM2l8WuQPsH8w/NvGZr/h/LO04ty2OqAW/m94tRgc4DEwBtqSuOFG8jHm3DZmkC1seLUYHuYpMJY5x5Y888bjw9J/zh2WbGNmM7acg0eL3PH2bYZvyo7Z9p1PbPw4o6yOn5+9+eGNN/i8z8zAZsDIdgxFhCBgfsDwp4awslEwCkbBKBi5AABj4UbXTfjazAAAAABJRU5ErkJggg==","orcid":"","institution":"Washington University in St. Louis","correspondingAuthor":true,"prefix":"","firstName":"Arnav","middleName":"Ajay","lastName":"Jadav","suffix":""},{"id":469433641,"identity":"d99e1c2d-0b89-49ab-bed0-587874f91f7d","order_by":3,"name":"Jonathan Franco","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Franco","suffix":""}],"badges":[],"createdAt":"2025-06-06 23:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6839767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6839767/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84506164,"identity":"873730c3-14b4-445c-94f3-87ba605530b4","added_by":"auto","created_at":"2025-06-12 18:50:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":904777,"visible":true,"origin":"","legend":"\u003cp\u003eA: 30-day major complications for female patients undergoing ankle fracture repair analyzing the modified frailty index-5 (mFI-5), Risk Analysis Index (RAI), Geriatric Nutritional Risk Index (GNRI), Preoperative Acute Severe Condition (PACS), Combined ASA-RAI-PACS (CARP), and American Society of Anesthesiology (ASA) scores.\u003c/p\u003e\n\u003cp\u003eB: 30-day minor complications for female patients undergoing ankle fracture repair analyzing the modified frailty index-5 (mFI-5), Risk Analysis Index (RAI), Geriatric Nutritional Risk Index (GNRI), Preoperative Acute Severe Condition (PACS), Combined ASA-RAI-PACS (CARP), and American Society of Anesthesiology (ASA) scores.\u003c/p\u003e\n\u003cp\u003eC: 30-day unplanned readmission for female patients undergoing ankle fracture repair analyzing the modified frailty index-5 (mFI-5), Risk Analysis Index (RAI), Geriatric Nutritional Risk Index (GNRI), Preoperative Acute Severe Condition (PACS), Combined ASA-RAI-PACS (CARP), and American Society of Anesthesiology (ASA) scores.\u003c/p\u003e\n\u003cp\u003eD: 30-day unplanned reoperations for female patients undergoing ankle fracture repair analyzing the modified frailty index-5 (mFI-5), Risk Analysis Index (RAI), Geriatric Nutritional Risk Index (GNRI), Preoperative Acute Severe Condition (PACS), Combined ASA-RAI-PACS (CARP), and American Society of Anesthesiology (ASA) scores.\u003c/p\u003e\n\u003cp\u003eE: 30-day extended length of stay for female patients undergoing ankle fracture repair analyzing the modified frailty index-5 (mFI-5), Risk Analysis Index (RAI), Geriatric Nutritional Risk Index (GNRI), Preoperative Acute Severe Condition (PACS), Combined ASA-RAI-PACS (CARP), and American Society of Anesthesiology (ASA) scores.\u003c/p\u003e\n\u003cp\u003eF: 30-day non-home discharge destination for female patients undergoing ankle fracture repair analyzing the modified frailty index-5 (mFI-5), Risk Analysis Index (RAI), Geriatric Nutritional Risk Index (GNRI), Preoperative Acute Severe Condition (PACS), Combined ASA-RAI-PACS (CARP), and American Society of Anesthesiology (ASA) scores.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6839767/v1/732e65dae897a898bfcf8857.png"},{"id":84507652,"identity":"29a07227-fad0-4410-88ad-896e245d4256","added_by":"auto","created_at":"2025-06-12 19:22:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2972274,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6839767/v1/b0572960-c1d2-494e-8dd8-b67359d71838.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Composite Frailty Score for Predicting Adverse Outcomes For Adult Women Undergoing Ankle Fracture Surgery: Model Development and Validation","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAnkle fractures represent one of the most common orthopedic injuries, with an incidence exceeding 184 per 100,000 person-years in the United States, and this burden is projected to increase substantially as the population ages [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Open reduction and internal fixation (ORIF) remains the gold standard treatment for displaced ankle fractures, yet postoperative outcomes vary significantly across patient populations, particularly among elderly and frail individuals who demonstrate higher rates of complications, prolonged recovery, and adverse discharge destinations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The growing recognition that chronological age alone inadequately predicts surgical risk has prompted increased focus on comprehensive preoperative risk stratification tools that can better identify vulnerable patients and guide clinical decision-making [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This shift toward precision medicine in surgical care has become increasingly critical as healthcare systems worldwide grapple with the complex needs of an aging population undergoing more frequent and complex surgical interventions.\u003c/p\u003e \u003cp\u003eCurrent approaches to surgical risk assessment have evolved to incorporate multidimensional measures of patient vulnerability, with frailty emerging as a powerful predictor of adverse postoperative outcomes across diverse surgical specialties [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The Risk Analysis Index (RAI), a validated 14-item frailty assessment tool, has demonstrated robust predictive capability for 30-day mortality and morbidity in various surgical populations, though its performance varies across different procedural contexts and patient populations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Concurrently, nutritional status assessment through tools such as the Geriatric Nutritional Risk Index (GNRI) has shown independent associations with surgical complications, length of stay, and long-term outcomes, with evidence suggesting that malnutrition significantly amplifies surgical risk beyond traditional comorbidity measures [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While individual frailty and nutritional assessments have proven valuable, recent studies in ankle fracture populations have highlighted the particular utility of the modified Frailty Index-5 (mFI-5) for predicting complications in geriatric patients, though comprehensive composite risk models remain underdeveloped [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the established predictive value of individual risk assessment tools, no validated composite scoring system currently exists that systematically combines frailty assessment, nutritional status, and traditional perioperative risk factors to optimize risk stratification in ankle surgery patients. This gap represents a critical limitation in current clinical practice, as healthcare providers must rely on multiple disparate assessment tools without clear guidance on their integrated interpretation or relative weighting in clinical decision-making. Therefore, this study aimed to develop and validate a novel Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score that integrates established frailty measures, perioperative risk assessment, and acute medical conditions to provide superior predictive accuracy for postoperative outcomes following ankle ORIF procedures. We hypothesized that this composite approach would demonstrate superior discriminative ability compared to individual risk assessment tools for predicting 30-day mortality, complications, readmissions, and discharge disposition in patients undergoing ankle fracture repair.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Patient Consent\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study utilized data from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database spanning 2015 to 2021. The ACS NSQIP database encompasses over 700 participating hospitals and captures more than 200 variables related to preoperative risk factors, intraoperative variables, and 30-day postoperative outcomes. The study was conducted in compliance with HIPAA regulations and was exempt from institutional review board approval due to the retrospective nature of deidentified data. Informed consent was waived given the retrospective design and use of publicly available deidentified data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient Selection\u003c/h3\u003e\n\u003cp\u003eAdult women aged 18 years and older who underwent operative management for isolated ankle injuries were identified using Current Procedural Terminology codes for open reduction and internal fixation of medial malleolus fractures (27766), posterior malleolus fractures (27769), distal fibular fractures (27792), bimalleolar fractures (27814), and trimalleolar fractures (27822, 27823). Additional codes captured fixation of distal tibiofibular joint disruptions (27829) and surgical treatment of ankle dislocations using internal, external, or percutaneous skeletal fixation (27846, 27848). Exclusion criteria included patients aged 90 years or older who were top-coded as 90, missing data on critical outcomes including mortality, discharge destination, functional status, or transfer status, and cases with concomitant surgeries or infections. The final analytic cohort comprised 14,818 patients after applying these exclusion criteria to maintain data accuracy and consistency.\u003c/p\u003e\n\u003ch3\u003eRisk Indices\u003c/h3\u003e\n\u003cp\u003eThe Risk Analysis Index was calculated using age, sex, renal impairment, dyspnea, cancer status, weight loss, and functional status (independent, partially dependent, totally dependent), with patients categorized as robust (RAI\u0026thinsp;\u0026le;\u0026thinsp;20), normal (RAI 21\u0026ndash;30), frail (RAI 31\u0026ndash;40), or very frail (RAI\u0026thinsp;\u0026ge;\u0026thinsp;41). The Geriatric Nutritional Risk Index was calculated as GNRI = (1.489 \u0026times; serum albumin [g/L]) + (41.7 \u0026times; [weight/ideal body weight]), where ideal body weight was determined using the Devine formula, with weight ratios capped at 1.0 for overweight patients. American Society of Anesthesiologists classification stratified patients into categories I through V (healthy patient, mild systemic disease, severe systemic disease, life-threatening systemic disease, moribund patient). The modified Frailty Index-5 incorporated functional dependence, diabetes mellitus, chronic obstructive pulmonary disease, congestive heart failure, and hypertension requiring medication. The Combined ASA-RAI-Preoperative Acute Severe Condition score was derived from multivariable logistic regression coefficients, combining weighted contributions from RAI, ASA classification, and Preoperative Acute Severe Condition scores.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were presented as median with interquartile range for non-normally distributed data assessed using the Kolmogorov-Smirnov test, while categorical variables were summarized as counts and percentages. Statistical comparisons utilized Kruskal-Wallis tests for continuous variables and chi-square tests for categorical variables across frailty groups. Multivariable logistic regression models identified predictors of adverse outcomes using adjusted odds ratios with 95% confidence intervals. Receiver operating characteristic curve analysis assessed model discrimination using C-statistics and area under the curve values, with DeLong tests comparing predictive performances between models. Internal validation employed 100 bootstrap replications with replacement to evaluate model stability and provide bias-corrected performance estimates. All analyses were performed using Stata MP Version 18 in the Redivis computing environment, with statistical significance defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 25,072 adult women aged 18 years and older who underwent operative management for isolated ankle injuries between 2015 and 2021 were initially identified. After exclusions for missing demographic variables (n\u0026thinsp;=\u0026thinsp;1,560), invalid surgical classifications (n\u0026thinsp;=\u0026thinsp;74), and incomplete outcome data (n\u0026thinsp;=\u0026thinsp;8,620), the final analytic cohort comprised 14,818 patients. The mean age was 50.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8 years, with all patients being female given the study inclusion criteria. Racial distribution included 14,895 (83.0%) White patients, 2,427 (16.4%) Black or African American patients, 503 (3.4%) Asian/Pacific Islander patients, and 127 (0.9%) American Indian or Alaska Native patients. The majority of patients had independent functional status (14,291, 96.4%), while 479 (3.2%) were partially dependent and 48 (0.3%) were totally dependent. ASA classification showed 7,540 (50.9%) patients as ASA I, 5,894 (39.8%) as ASA II, 1,334 (9.0%) as ASA III, and 50 (0.3%) as ASA IV.\u003c/p\u003e \u003cp\u003eClinical characteristics revealed hypertension requiring medication in 5,710 (38.5%) patients, diabetes mellitus in 2,438 (16.5%) patients with 1,002 (6.8%) requiring insulin, and chronic obstructive pulmonary disease in 593 (4.0%) patients. Congestive heart failure was present in 120 (0.8%) patients, bleeding disorders in 575 (3.9%) patients, and disseminated cancer in 42 (0.3%) patients. Current smoking status was documented in 2,982 (20.1%) patients, chronic steroid use in 386 (2.6%) patients, and dyspnea at moderate exertion or rest in 515 (3.5%) patients. The mean body mass index was 29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4 kg/m\u0026sup2;, and the mean preoperative albumin level among patients with available data was 3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 g/dL (n\u0026thinsp;=\u0026thinsp;5,459). Risk stratification using the modified Frailty Index-5 (mFI-5) categorized 8,515 (58.2%) patients as not frail, 4,691 (32.0%) as prefrail, 1,201 (8.2%) as frail, and 411 (2.8%) as severely frail.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnivariate Analysis\u003c/h3\u003e\n\u003cp\u003eUnivariate logistic regression analysis identified multiple significant predictors of major complications. The mFI-5 score demonstrated a strong association with major complications (OR 2.32, 95% CI 1.99\u0026ndash;2.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as did the Risk Analysis Index score (OR 1.10 per point increase, 95% CI 1.08\u0026ndash;1.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Preoperative Acute Severe Condition score showed substantial predictive value (OR 3.11, 95% CI 2.39\u0026ndash;4.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while ASA classification was similarly associated with increased complication rates (OR 3.13, 95% CI 2.47\u0026ndash;3.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Geriatric Nutritional Risk Index demonstrated protective effects, with higher scores associated with reduced complication risk (OR 0.97 per point increase, 95% CI 0.95\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.005). Age stratification revealed progressively increasing risk across RAI tiers, with prefrail patients experiencing 1.75% major complication rates compared to 0.66% in robust patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), frail patients experiencing 5.88% rates, and severely frail patients experiencing 11.11% rates.\u003c/p\u003e\n\u003ch3\u003eMultivariable Analysis\u003c/h3\u003e\n\u003cp\u003eIn the multivariable logistic regression model incorporating all frailty indices, the Preoperative Acute Severe Condition score remained the strongest independent predictor of major complications (adjusted OR 1.75, 95% CI 1.26\u0026ndash;2.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Risk Analysis Index score maintained significance as an independent predictor (adjusted OR 1.04 per point increase, 95% CI 1.01\u0026ndash;1.07, p\u0026thinsp;=\u0026thinsp;0.013), while ASA classification demonstrated continued predictive value (adjusted OR 1.70, 95% CI 1.23\u0026ndash;2.36, p\u0026thinsp;=\u0026thinsp;0.001). The mFI-5 score, while significant in univariate analysis, lost statistical significance in the adjusted model (adjusted OR 1.21, 95% CI 0.92\u0026ndash;1.59, p\u0026thinsp;=\u0026thinsp;0.174). For minor complications, the Risk Analysis Index (adjusted OR 1.10, 95% CI 1.06\u0026ndash;1.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ASA classification (adjusted OR 2.00, 95% CI 1.26\u0026ndash;3.17, p\u0026thinsp;=\u0026thinsp;0.003) emerged as the strongest independent predictors. Extended length of stay was independently associated with Risk Analysis Index score (adjusted OR 1.10, 95% CI 1.08\u0026ndash;1.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PACS score (adjusted OR 1.21, 95% CI 1.03\u0026ndash;1.41, p\u0026thinsp;=\u0026thinsp;0.018), and ASA classification (adjusted OR 1.93, 95% CI 1.71\u0026ndash;2.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMajor Postoperative Outcomes\u003c/h2\u003e \u003cp\u003eThe overall 30-day mortality rate was 0.19% (n\u0026thinsp;=\u0026thinsp;28). Major complications occurred in 148 patients (1.0%), including myocardial infarction in 16 patients (0.11%), pulmonary embolism in 27 patients (0.18%), and deep surgical site infections in 29 patients (0.20%). Minor complications affected 121 patients (0.82%), primarily superficial surgical site infections (n\u0026thinsp;=\u0026thinsp;87, 0.59%) and urinary tract infections (n\u0026thinsp;=\u0026thinsp;146, 0.99%). Unplanned 30-day readmissions occurred in 378 patients (2.55%), while unplanned reoperations occurred in 194 patients (1.31%). Extended length of stay, defined as exceeding the 75th percentile of 3 days, affected 3,480 patients (23.5%). Non-home discharge occurred in 2,719 patients (15.3%), with institutional care facilities being the most common destination (n\u0026thinsp;=\u0026thinsp;1,643, 60.4% of non-home discharges). Risk-stratified analysis revealed dramatic outcome differences across frailty categories, with severely frail patients experiencing 83.3% non-home discharge rates compared to 5.0% in robust patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 72.2% extended length of stay compared to 13.8% in robust patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNovel Score Development\u003c/h2\u003e \u003cp\u003eThe Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score was derived using multivariable logistic regression coefficients from the final adjusted model. The score incorporated weighted contributions from the Risk Analysis Index (coefficient 0.044), PACS score (coefficient 0.623), and ASA classification (coefficient 0.579), creating a continuous risk prediction tool. The CARP score demonstrated superior discriminative ability compared to individual indices across multiple outcomes, achieving area under the receiver operating characteristic curve (AUROC) values of 0.721 for major complications, 0.801 for minor complications, 0.735 for unplanned readmissions, 0.654 for unplanned reoperations, and 0.783 for extended length of stay. These performance metrics exceeded those of individual frailty indices, with the mFI-5 achieving AUROC values of 0.695, 0.694, 0.697, 0.609, and 0.693 for the respective outcomes, and the RAI score achieving 0.695, 0.777, 0.679, 0.608, and 0.770.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInternal Validation\u003c/h2\u003e \u003cp\u003eBootstrap validation using 100 replications was performed to assess model optimism and provide bias-corrected performance estimates. The bootstrap procedure revealed minimal optimism bias across all indices, with optimism estimates ranging from 0.005 to 0.008 for most models. For the CARP score, bias-corrected AUROC values were 0.714 (95% CI 0.668\u0026ndash;0.760) for major complications, 0.793 (95% CI 0.750\u0026ndash;0.836) for minor complications, 0.727 (95% CI 0.704\u0026ndash;0.751) for unplanned readmissions, 0.647 (95% CI 0.604\u0026ndash;0.690) for unplanned reoperations, and 0.776 (95% CI 0.765\u0026ndash;0.786) for extended length of stay. The mFI-5 demonstrated bias-corrected AUROC values of 0.688 (95% CI 0.644\u0026ndash;0.733), 0.687 (95% CI 0.642\u0026ndash;0.732), 0.690 (95% CI 0.664\u0026ndash;0.717), 0.603 (95% CI 0.564\u0026ndash;0.642), and 0.686 (95% CI 0.676\u0026ndash;0.696) for the respective outcomes. DeLong tests comparing the CARP score to individual indices showed statistically significant improvements in discriminative ability for major complications (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs mFI-5, p\u0026thinsp;=\u0026thinsp;0.024 vs RAI), minor complications (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs all comparators), and extended length of stay (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs mFI-5, p\u0026thinsp;=\u0026thinsp;0.045 vs ASA).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eWhy We Conducted This Study\u003c/h2\u003e \u003cp\u003eThe management of ankle fractures in women presents unique challenges for risk stratification and perioperative planning, particularly given that women experience disproportionately higher fracture rates with one in two women over age 50 sustaining a fracture in their lifetime due to smaller, less dense bone structure compared to men [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Ankle injuries represent among the most common bone and joint injuries, with an estimated incidence exceeding five million cases annually in the United States, with women being far more likely to sustain fractures than men [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite the high prevalence of ankle fractures in women, existing frailty assessment tools have demonstrated variable predictive accuracy in orthopedic populations, with limited validation specifically for ankle procedures in female patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Current risk stratification relies heavily on individual indices such as the modified Frailty Index-5, Risk Analysis Index, or American Society of Anesthesiologists classification, yet no standardized composite scoring system has been developed to optimize predictive performance across multiple postoperative outcomes in this population. The development of a Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score addresses this critical gap by providing a novel, validated tool for comprehensive risk assessment in ankle fracture surgery specifically for women.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Key Findings\u003c/h2\u003e \u003cp\u003eOur analysis of 14,818 women demonstrated that the CARP score achieved superior discriminative ability compared to individual frailty indices across all measured outcomes, with bias-corrected area under the receiver operating characteristic curve values of 0.714 for major complications, 0.793 for minor complications, and 0.776 for extended length of stay. The composite score effectively stratified female patients into distinct risk categories, with severely frail patients experiencing mortality rates of 11.11% compared to 0.66% in robust patients, and non-home discharge rates of 83.3% versus 5.0% respectively. Multivariable analysis revealed that the Preoperative Acute Severe Condition score emerged as the strongest independent predictor of major complications (adjusted OR 1.75, 95% CI 1.26\u0026ndash;2.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the Risk Analysis Index and ASA classification maintained significant predictive value in the composite model. The CARP score demonstrated statistically significant improvements over individual indices in DeLong comparisons, with p-values less than 0.001 for major complications and less than 0.045 for extended length of stay when compared to traditional frailty measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLiterature Context and Validation\u003c/h2\u003e \u003cp\u003ePrevious studies have established the predictive validity of individual frailty indices in orthopedic populations, with Aktı et al. demonstrating that the modified Frailty Index-5 effectively predicted complications in geriatric ankle fractures (OR 2.726, 95% CI 1.285\u0026ndash;5.783) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, Lewis et al. found that mFI-5 scores predicted adverse outcomes following total ankle arthroplasty, with overall complication rates increasing from 5.24\u0026ndash;19.38% when comparing scores of 0 versus \u0026ge;\u0026thinsp;2 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The systematic review by Gupta et al. encompassing 81 studies confirmed that frailty serves as an independent predictor of mortality (30-day OR 2.89, 95% CI 2.00-4.18) and major complications (OR 1.63, 95% CI 1.10\u0026ndash;2.41) across orthopedic procedures [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, these studies consistently highlighted the heterogeneity of frailty assessment tools and the lack of standardized approaches, supporting the need for composite scoring systems. The association between frailty and peripheral vascular disease, as demonstrated by Singh et al. showing frailty prevalence of 17.5% in patients with ankle-brachial index\u0026thinsp;\u0026lt;\u0026thinsp;0.9, provides additional context for understanding the complex interplay between cardiovascular comorbidity and surgical risk in our female ankle fracture population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Existing Literature\u003c/h2\u003e \u003cp\u003eOur findings align with previous research demonstrating the prognostic value of frailty assessment while extending the evidence base through the development of a novel composite score specifically validated in women with ankle fractures. The mortality rates observed in our study are consistent with those reported by Lu et al., who found area under the curve values of 0.856 for modified-Krishnan's frailty index in predicting hip fracture mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, our CARP score achieved superior discriminative performance compared to individual indices, suggesting that composite approaches may overcome limitations of single-domain assessments in female populations. The high prevalence of non-home discharge (15.3%) in our cohort parallels findings from Zhang et al., who demonstrated increased venous thromboembolism risk in frail patients following hip fracture surgery (OR 2.59, 95% CI 1.25\u0026ndash;5.39) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Notably, our study's exclusive focus on women with ankle fractures represents a unique population with potentially different risk profiles compared to mixed-gender orthopedic cohorts, as women's bone density and healing patterns may differ substantially from men. The incorporation of nutritional assessment through albumin levels addresses limitations identified in previous studies, as Hon et al. demonstrated that low handgrip strength and muscle mass were associated with poor wound healing and increased mortality in diabetes-related foot disease [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eClinical Relevance and Implementation\u003c/h2\u003e \u003cp\u003eThe CARP score provides clinicians with a practical tool for perioperative risk stratification in women that can inform surgical decision-making, patient counseling, and resource allocation. Unlike complex frailty assessments requiring specialized testing, the CARP score utilizes readily available clinical variables from routine preoperative evaluation, making it feasible for widespread implementation in women's orthopedic care. The score's ability to predict multiple outcomes simultaneously allows for comprehensive risk assessment, enabling targeted interventions such as preoperative optimization, enhanced recovery protocols, or alternative treatment strategies for high-risk female patients. Given that ankle fractures in women often require urgent surgical intervention, particularly in elderly patients as demonstrated by Ou et al. in their comparison of hindfoot nailing versus open reduction and internal fixation, rapid risk stratification becomes crucial for optimizing outcomes in this population [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The observed relationship between frailty and complications supports the implementation of multidisciplinary perioperative care pathways, including nutritional supplementation, physical therapy, and social work consultation for women identified as high-risk by the CARP score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations warrant consideration in interpreting our findings. The retrospective nature of this analysis using the ACS NSQIP database limits our ability to control for unmeasured confounders and may introduce selection bias inherent to registry data. The restriction to women, while providing insights into this specific population, limits generalizability to male patients who may have different risk profiles and complication patterns following ankle fracture surgery. Missing data for certain variables, particularly albumin levels required for nutritional assessment, resulted in a reduced sample size for composite score calculation and may have introduced bias toward healthier patients with complete laboratory data. The study population's racial composition, with 83% White patients, may limit applicability to more diverse female populations where genetic, socioeconomic, and cultural factors could influence surgical outcomes. Additionally, the relatively low overall complication rates, while reflecting contemporary surgical care quality, may have limited statistical power for detecting differences in rare events such as mortality and major complications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFuture Research Directions\u003c/h2\u003e \u003cp\u003eFuture investigations should focus on prospective validation of the CARP score across diverse female populations and surgical settings to confirm its generalizability and clinical utility in women's orthopedic care. External validation using independent datasets from different healthcare systems would strengthen evidence for widespread implementation in female ankle fracture management. The development of point-of-care calculators and integration with electronic health record systems could facilitate real-time risk assessment and clinical decision support specifically for women. Research examining the cost-effectiveness of CARP score-guided perioperative interventions in female patients would provide valuable economic data for healthcare administrators and policymakers. Additionally, studies investigating the relationship between frailty and specific complications in women, such as the association demonstrated by Kong et al. between frailty and altered walking kinematics, could inform targeted rehabilitation strategies for female patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The exploration of frailty's interaction with diabetes and peripheral neuropathy in women, as described by Tuttle et al. and Jakubiak, represents an important area for future investigation given the high prevalence of these conditions in female surgical populations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe Combined ASA-RAI-Preoperative Acute Severe Condition score represents a novel, validated tool for comprehensive risk stratification in women undergoing ankle fracture surgery that demonstrates superior predictive performance compared to individual frailty indices. Implementation of the CARP score in clinical practice has the potential to improve perioperative risk assessment, guide surgical decision-making, and optimize resource allocation for high-risk female patients undergoing ankle fracture repair.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest and Sources of Funding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflicts of interest or external sources of funding for this study are reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRB Approval and Consent:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was exempt from Institutional Review Board oversight as it used the publicly available, de-identified ACS-NSQIP database. Informed consent was not applicable given the retrospective design and anonymized data structure.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.S. and B.J. conceived the study and provided critical supervision throughout. A.A. co-developed the study design, led the literature review, managed reference curation, prepared the initial manuscript draft, and coordinated submission materials. C.S. conducted statistical analysis and generated all figures and tables. J.F. offered clinical expertise and contributed to key revisions. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWeinraub GM, Newport I, Kim BK, et al. Outcomes Following Open Reduction Internal Fixation of Ankle Fractures (ORIF) By Podiatric Surgeons. 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Int J Environ Res Public Health. 2020;17:9339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph17249339\u003c/span\u003e\u003cspan address=\"10.3390/ijerph17249339\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eComplete Research Tables for Frailty Analysis in Ankle Surgery\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. The association of patient demographics and comorbidities and Risk Analysis Index (RAI) tiers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCOPD, Chronic Obstructive Pulmonary Disease; CHF, Congestive Heart Failure; mFI-5, Modified Frailty Index-5; RAI, Risk Analysis Index; GNRI, Geriatric Nutritional Risk Index.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (N=14,818)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot frail RAI \u0026le; 20 (N=11,379)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrefrail RAI = 21\u0026ndash;30 (N=3,149)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrail RAI = 31\u0026ndash;40 (N=272)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeverely frail RAI \u0026ge; 41 (N=18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (yr)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e54.2 \u0026plusmn; 15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e51.1 \u0026plusmn; 14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e63.4 \u0026plusmn; 15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e70.8 \u0026plusmn; 13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e75.3 \u0026plusmn; 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14,895 (82.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e9,742 (85.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2,447 (77.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e202 (74.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e12 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2,427 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1,367 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e574 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e59 (21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eAAPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e503 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e226 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e115 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e10 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e127 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e44 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e13 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody mass index (kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e28.4 \u0026plusmn; 6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e28.1 \u0026plusmn; 6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e29.2 \u0026plusmn; 6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e30.1 \u0026plusmn; 7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e29.8 \u0026plusmn; 6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14,291 (96.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e11,257 (98.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2,871 (91.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e149 (54.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14 (77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003ePartially dependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e479 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e122 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e278 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e75 (27.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eTotally dependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e48 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e48 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e15,095 (84.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e10,134 (89.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2,383 (75.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e155 (57.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e10 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eOral medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1,436 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e789 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e545 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e92 (33.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e6 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1,002 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e456 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e221 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e25 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e593 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e270 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e278 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e42 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e120 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e32 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e71 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e15 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent smoker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2,982 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2,189 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e722 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e67 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDyspnea at rest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e515 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e251 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e237 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e25 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5,710 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3,482 (30.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2,005 (63.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e206 (75.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e17 (94.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisseminated cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e42 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e10 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e23 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e8 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSteroid use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e386 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e178 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e181 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e25 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight loss\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e12 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emFI-5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eNot frail (mFI-5 = 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8,515 (58.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7,654 (67.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e849 (27.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e12 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003ePrefrail (mFI-5 = 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4,691 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3,349 (29.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1,279 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e59 (21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eFrail (mFI-5 = 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1,201 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e360 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e748 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e89 (32.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eSeverely frail (mFI-5 \u0026ge; 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e411 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e16 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e273 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e112 (41.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e10 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026gt;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e12,129 (81.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e9,894 (87.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2,151 (68.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e82 (30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1,701 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1,175 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e487 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e37 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e793 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e271 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e428 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e87 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e195 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e39 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e83 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e66 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2,985 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2,798 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e184 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e3 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8,252 (55.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e6,934 (60.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1,284 (40.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e33 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3,404 (23.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1,597 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1,631 (51.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e167 (61.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e177 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e50 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e50 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e69 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutpatient procedure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1,847 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1,602 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e237 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e8 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength of stay after operation (day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.1 \u0026plusmn; 3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.8 \u0026plusmn; 2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.2 \u0026plusmn; 4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7.4 \u0026plusmn; 8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperative time (min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e98.3 \u0026plusmn; 52.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e95.2 \u0026plusmn; 50.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e107.8 \u0026plusmn; 58.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e118.4 \u0026plusmn; 61.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e125.7 \u0026plusmn; 67.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. 30-day outcome measures including mortality, nonroutine discharge, extended Length of Stay (eLOS), occurrence of complication, major complications, reoperation, and readmission by RAI tiers.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (N=14,818)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot frail RAI \u0026le; 20 (N=11,379)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrefrail RAI = 21\u0026ndash;30 (N=3,149)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrail RAI = 31\u0026ndash;40 (N=272)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeverely frail RAI \u0026ge; 41 (N=18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e28 (0.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e14 (0.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e11 (0.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2 (0.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1 (5.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonroutine discharge destination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2,332 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e570 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1,554 (49.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e193 (72.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e15 (83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeLOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3,480 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1,575 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1,695 (53.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e197 (72.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e13 (72.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e148 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e75 (0.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e55 (1.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e16 (5.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e2 (11.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinor complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e121 (0.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e40 (0.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e61 (1.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e20 (7.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReadmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e378 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e193 (1.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e149 (4.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e31 (11.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e5 (27.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReoperation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e194 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e122 (1.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e61 (1.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e9 (3.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e2 (11.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e30-day outcome measures by GNRI tiers\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (N=14,818)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI \u0026gt;98 (N=12,129)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI =92\u0026ndash;98 (N=1,701)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI =82-91 (N=793)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI \u0026lt;82 (N=195)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e28 (0.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e18 (0.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5 (0.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e3 (0.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2 (1.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonroutine discharge destination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2,332 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1,297 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e534 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e379 (48.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e122 (62.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeLOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3,480 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2,086 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e769 (45.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e484 (61.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e141 (72.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e148 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e97 (0.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e32 (1.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e11 (1.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e8 (4.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinor complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e121 (0.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e64 (0.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e29 (1.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e23 (2.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e5 (2.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReadmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e378 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e230 (1.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e69 (4.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e50 (6.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e29 (14.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReoperation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e194 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e127 (1.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e29 (1.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e29 (3.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e9 (4.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Univariate logistic regression analysis of GNRI and RAI and major postoperative measures in surgery patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGNRI; Geriatric Nutritional Risk Index, RAI; Risk Analysis Index. Patient groups with GNRI \u0026gt; 98 and RAI \u0026le; 20 were the reference for GNRI and RAI regression analyses, respectively.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio (95% confidence interval)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio (95% confidence interval)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e1.94 (0.70-5.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e2.89 (1.32-6.33)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e2.53 (0.75-8.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e6.08 (1.39-26.6)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e6.84 (1.54-30.4)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e48.0 (5.69-405)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonroutine discharge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e3.76 (3.36-4.21)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e17.9 (16.0-20.0)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e7.43 (6.46-8.54)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e40.4 (30.4-53.7)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e15.6 (11.6-21.0)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e62.8 (18.0-219)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeLOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e4.01 (3.63-4.43)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e7.16 (6.47-7.93)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e7.71 (6.72-8.85)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e16.8 (12.4-22.8)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e12.9 (9.34-17.8)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e16.7 (5.88-47.4)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e2.35 (1.53-3.60)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e2.68 (1.89-3.79)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e1.74 (0.90-3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e9.23 (5.23-16.3)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e5.21 (2.45-11.1)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e18.2 (4.07-81.2)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinor complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e3.24 (2.12-4.97)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e5.57 (3.78-8.21)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e5.54 (3.49-8.79)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e22.4 (13.1-38.3)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e4.89 (1.91-12.5)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReadmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e2.18 (1.67-2.84)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e2.83 (2.35-3.42)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e3.47 (2.52-4.77)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e7.29 (4.88-10.9)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e8.69 (5.75-13.1)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e20.9 (7.62-57.2)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReoperation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e1.63 (1.09-2.43)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e1.82 (1.34-2.48)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e3.56 (2.36-5.37)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e3.14 (1.57-6.27)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e4.51 (2.26-9.00)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e11.2 (2.53-49.6)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multivariable regression analysis of mortality and American Society of Anesthesiologists physical status class risk stratification system (ASA), Geriatric Nutritional Risk Index (GNRI), Risk Analysis Index (RAI), and Preoperative Acute Severe Condition (PACS).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted odds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% Confidence Interval - Lower Bound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% Confidence Interval - Upper Bound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePACS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. AUC with 95% confidence interval for frailty indices and post-operative outcomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe DeLong test was used to compare all indices against the novel CARP score. AUC; Area Under the receiver operating characteristic Curve.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"537\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% Confidence Interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCARP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonroutine discharge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCARP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeLOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCARP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCARP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinor complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCARP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReadmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCARP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReoperation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCARP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001 compared to CARP\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Internal validation of Area Under the Receiver operating Curve analysis by bootstrapping replications.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInitial AUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal Validation AUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBias-Corrected Confidence Intervals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eLower bound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCARP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eRAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003emFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonroutine discharge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCARP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eRAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003emFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeLOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCARP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eRAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003emFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCARP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eRAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n 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\u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003emFI-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Surgical Complications, Orthopedics, Ankle Fractures, Malleolus, Frailty","lastPublishedDoi":"10.21203/rs.3.rs-6839767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6839767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eAnkle fracture repair is a common orthopedic procedure, but outcomes vary significantly in vulnerable populations such as smokers. Frailty and nutritional deficits contribute independently to surgical risk, yet no validated composite score exists for risk stratification in this setting.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eUsing ACS-NSQIP data from 2015\u0026ndash;2021, we identified 14,818 adult smokers who underwent operative fixation of isolated ankle fractures. We developed the Combined ASA\u0026ndash;RAI\u0026ndash;Preoperative Acute Severe Condition (CARP) score by integrating the ASA classification, Risk Analysis Index, and PACS. We evaluated predictive accuracy for major complications, readmission, extended length of stay (eLOS), and non-home discharge using AUROC analysis and bootstrap validation.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe CARP score outperformed individual indices across all outcomes. For major complications, CARP achieved an AUROC of 0.721 versus 0.695 (RAI) and 0.695 (mFI-5). For eLOS, CARP reached 0.783 versus 0.770 (RAI) and 0.693 (mFI-5). Bootstrap-corrected AUROCs remained consistently higher for CARP, with significant improvements shown by DeLong tests.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThe CARP score provides superior discriminatory power over existing frailty and nutritional indices in predicting postoperative risks in smokers undergoing ankle fracture repair. Its integration into preoperative planning may improve risk stratification and guide individualized care.\u003c/p\u003e","manuscriptTitle":"A Novel Composite Frailty Score for Predicting Adverse Outcomes For Adult Women Undergoing Ankle Fracture Surgery: Model Development and Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 18:50:18","doi":"10.21203/rs.3.rs-6839767/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f698e7c3-82c0-4c5e-b342-081c3aca7864","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-12T18:50:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-12 18:50:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6839767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6839767","identity":"rs-6839767","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}