Superior Predictive Performance of a Composite Frailty-Risk Index in Hispanic Patients Undergoing Proximal Humerus Fracture Surgery

Superior Predictive Performance of a Composite Frailty-Risk Index in Hispanic Patients Undergoing Proximal Humerus Fracture Surgery | 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 Superior Predictive Performance of a Composite Frailty-Risk Index in Hispanic Patients Undergoing Proximal Humerus Fracture Surgery Cameron Sabet, Bhav Jain, Ramez Odat, Arnav Ajay Jadav, Jonathan Franco This is a preprint; it has not been peer reviewed by a journal.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-6843688/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 Proximal humerus fractures represent a significant clinical challenge, particularly in minority patients, where accurate risk stratification remains inadequate. While individual frailty indices like the Risk Analysis Index (RAI) and modified Frailty Index-5 (mFI-5) have shown promise in predicting postoperative outcomes, no composite scoring system combining multiple validated risk assessment tools has been developed for this population. Methods This retrospective cohort study analyzed 1,259 patients who underwent proximal humerus fracture surgery from the ACS NSQIP database (2015–2021). We calculated RAI, mFI-5, Geriatric Nutritional Risk Index (GNRI), Preoperative Acute Severe Condition (PACS) scores, and ASA classification for all patients. A novel Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score was derived using multivariable regression coefficients. Primary outcomes included 30-day mortality, major complications, readmissions, reoperations, extended length of stay, and non-home discharge. Results The cohort had mean age 67.4 ± 12.1 years with 62.3% female patients. Thirty-day mortality was 0%, major complications occurred in 0.9%, and extended length of stay affected 20.1% of patients. CARP demonstrated superior or comparable predictive performance across outcomes with AUROC values ranging from 0.546–0.755. In multivariable analysis, PACS score independently predicted major complications (OR 2.96, 95% CI 1.03–8.52, p = 0.045), while GNRI independently predicted readmissions (OR 0.94, 95% CI 0.88-1.00, p = 0.038) and reoperations (OR 0.90, 95% CI 0.82–0.99, p = 0.036). Bootstrap validation confirmed robust internal validity with minimal optimism bias. Conclusions The novel CARP score provides enhanced risk stratification for proximal humerus fracture surgery patients, demonstrating superior predictive performance compared to individual indices and offering clinicians a comprehensive tool for preoperative decision-making and patient counseling. Surgical Complications Orthopedics Ankle Fractures Malleolus Frailty Figures Figure 1 INTRODUCTION Proximal humerus fractures represent the third most common fracture type in elderly patients, with an incidence exceeding 70 per 100,000 person-years in individuals over 65 years of age [ 1 ]. The aging population demographic has contributed to a substantial increase in surgical volume, with reverse total shoulder arthroplasty and open reduction internal fixation procedures becoming increasingly common treatment modalities for displaced fractures [ 2 , 3 ]. As healthcare systems transition toward value-based care models, accurate preoperative risk stratification has become essential for optimizing patient outcomes, reducing complications, and controlling healthcare costs [ 4 ]. The complexity of managing elderly patients with multiple comorbidities and varying degrees of frailty necessitates comprehensive risk assessment tools that can guide clinical decision-making and inform patient counseling regarding postoperative expectations. Current risk stratification approaches in orthopedic surgery have increasingly focused on frailty assessment tools and nutritional indices as predictors of adverse outcomes. The modified Frailty Index-5 (mFI-5) has demonstrated significant associations with complications, readmissions, and non-home discharge across various orthopedic procedures, including total shoulder arthroplasty and upper extremity fracture repair [ 5 – 10 ]. The Risk Analysis Index (RAI) has emerged as a validated frailty assessment tool that quantifies preoperative risk using demographic, functional, and comorbid variables, though its application in proximal humerus fracture surgery remains limited. Nutritional status, as assessed by the Geriatric Nutritional Risk Index (GNRI), has shown promise in predicting postoperative outcomes, particularly in elderly surgical populations [ 11 ]. Machine learning approaches have identified key predictive factors including age, comorbidities, and preoperative laboratory values, yet no standardized composite scoring system has been validated specifically for proximal humerus fracture patients [ 4 , 12 ]. Despite the demonstrated utility of individual frailty and risk assessment tools, significant gaps remain in the comprehensive evaluation of proximal humerus fracture patients. No validated composite scoring system exists that combines multiple established risk indices to provide enhanced predictive accuracy for this specific patient population. The heterogeneity in risk assessment approaches and the lack of standardized outcome prediction tools limit the ability to provide consistent, evidence-based preoperative counseling and risk stratification. Therefore, this study aimed to develop and internally validate a novel Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score that integrates multiple validated risk assessment tools to improve postoperative outcome prediction in patients undergoing proximal humerus fracture surgery. METHODS Data Source and Patient Selection This retrospective cohort study utilized data from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database from 2015–2021. The NSQIP database includes over 700 hospitals and captures more than 200 variables related to preoperative risk factors, intraoperative variables, and 30-day postoperative outcomes. Adult patients who underwent reverse total shoulder arthroplasty (CPT 23472) and open reduction internal fixation (CPT 23615) for proximal humerus fractures were identified using International Classification of Diseases codes. Exclusion criteria included patients aged 90 years or older, missing critical outcome data (mortality, discharge destination, functional status), and incomplete frailty assessment variables. The final cohort consisted of 1,259 patients after applying these exclusion criteria. Risk Indices Assessment The Risk Analysis Index (RAI) was calculated using age, sex, weight loss, congestive heart failure, dyspnea, renal impairment, and functional dependence, with patients categorized as robust (RAI ≤ 20), normal (RAI 21–30), frail (RAI 31–40), or severely frail (RAI ≥ 41). The Geriatric Nutritional Risk Index (GNRI) 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 (males: 50 + 0.91 × [height-152.4], females: 45.5 + 0.91 × [height-152.4]), with nutritional risk categorized as no risk (GNRI ≥ 99), low risk (GNRI 92–98), moderate risk (GNRI 82–91), or major risk (GNRI < 82). The modified Frailty Index-5 (mFI-5) incorporated functional dependence, diabetes mellitus, chronic obstructive pulmonary disease, congestive heart failure, and hypertension requiring medication. The Preoperative Acute Severe Condition (PACS) score quantified acute preoperative conditions using Present at Time of Surgery variables. The American Society of Anesthesiologists classification stratified patients by overall health status (ASA I: no disturbance, ASA II: mild disturbance, ASA III: severe disturbance, ASA IV: life-threatening disturbance). Outcomes and Statistical Analysis Primary outcomes included 30-day mortality, major complications (myocardial infarction, pulmonary embolism, deep vein thrombosis, sepsis, septic shock, deep incisional surgical site infection, prolonged ventilation, unplanned intubation, stroke, postoperative dialysis), minor complications (urinary tract infection, superficial surgical site infection, blood transfusion), 30-day unplanned readmission, 30-day unplanned reoperation, extended length of stay (> 75th percentile), and non-home discharge. The novel Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score was derived from multivariable logistic regression coefficients. Continuous variables were presented as mean ± standard deviation, and categorical variables as frequencies with percentages. Statistical comparisons utilized Kruskal-Wallis tests for continuous variables and chi-square tests for categorical variables. Multivariable logistic regression models assessed independent predictors with odds ratios and 95% confidence intervals. Receiver operating characteristic curve analysis evaluated discriminative performance using C-statistics, with DeLong tests comparing model performance. Internal validation employed 100 bootstrap replications to calculate bias-corrected C-statistics. All analyses were performed using Stata MP Version 18 in the Redivis computing environment, with statistical significance set at p < 0.05. RESULTS Patient Characteristics A total of 1,259 patients who underwent proximal humerus fracture surgery were included in the final analysis after exclusions for missing key variables (n = 297), invalid data entries (n = 13), and extreme ASA classifications (n = 1). The mean age was 67.4 ± 12.1 years, with 784 (62.3%) female patients. The cohort was predominantly White (n = 1,236, 98.2%), with small representations of American Indian or Alaska Native (n = 16, 1.3%), Black or African American (n = 6, 0.5%), and Asian/Pacific Islander (n = 1, 0.1%) populations. Mean body mass index was 30.8 ± 6.0 kg/m², mean length of stay was 1.9 ± 2.5 days, and mean operative time was 119.3 ± 54.4 minutes. Functional independence was preserved in 1,189 (94.9%) patients preoperatively, while 59 (4.7%) were partially dependent and 5 (0.4%) were totally dependent. Hypertension requiring medication was the most common comorbidity (n = 825, 65.5%), followed by diabetes (n = 381, 30.3% total; n = 124, 9.9% insulin-dependent; n = 257, 20.4% non-insulin dependent). Additional comorbidities included chronic obstructive pulmonary disease (n = 59, 4.7%), smoking history (n = 130, 10.3%), bleeding disorders (n = 43, 3.4%), steroid use (n = 59, 4.7%), dyspnea (n = 36, 2.9%), disseminated cancer (n = 8, 0.6%), congestive heart failure (n = 9, 0.7%), renal impairment (n = 2, 0.2%), and weight loss (n = 2, 0.2%). Frailty and Nutritional Risk Assessment Risk stratification using the Risk Analysis Index (RAI) classified 468 (37.2%) patients as not frail (RAI ≤ 20), 726 (57.7%) as prefrail (RAI 21–30), 61 (4.8%) as frail (RAI 31–40), and 4 (0.3%) as severely frail (RAI ≥ 41). The modified Frailty Index-5 (mFI-5) categorized 396 (31.5%) patients as not frail (score 0), 675 (53.6%) as prefrail (score 1), 144 (11.4%) as frail (score 2), and 44 (3.5%) as severely frail (score ≥ 3). Geriatric Nutritional Risk Index (GNRI) scores were available for 635 patients, with 1,049 (83.3%) classified as no nutritional risk (GNRI ≥ 99), 128 (10.2%) as low risk (GNRI 92–98), 72 (5.7%) as moderate risk (GNRI 82–91), and 10 (0.8%) as major nutritional risk (GNRI < 82). The Preoperative Acute Severe Condition (PACS) score was calculated for 1,192 patients, with a mean score of 0.14 ± 0.39. ASA classification distributed as follows: ASA I (n = 48, 3.8%), ASA II (n = 511, 40.6%), ASA III (n = 663, 52.7%), and ASA IV (n = 37, 2.9%). Major Postoperative Outcomes The overall 30-day mortality rate was 0% in this cohort. Major complications occurred in 11 patients (0.9%), including myocardial infarction (n = 1), pulmonary embolism (n = 5), deep vein thrombosis (n = 0), sepsis (n = 0), septic shock (n = 0), deep incisional surgical site infection (n = 1), ventilator dependence > 48 hours (n = 2), unplanned intubation (n = 18), stroke (n = 0), and postoperative dialysis (n = 0). Minor complications affected 8 patients (0.6%), comprising urinary tract infections (n = 8), superficial surgical site infections (n = 0), and blood transfusions (n = 0). Thirty-day unplanned readmissions occurred in 45 patients (3.6%), while unplanned reoperations occurred in 21 patients (1.7%). Extended length of stay (> 75th percentile of 2 days) was observed in 253 patients (20.1%). Non-home discharge destination occurred in 123 patients (9.8%), with 85 patients discharged to skilled nursing facilities, 36 to rehabilitation facilities, 11 to other facilities, and 1 to separate acute care hospitals. Univariate Analysis In univariate analysis for major complications, the modified Frailty Index-5 demonstrated significant association (OR 2.16, 95% CI 1.11–4.23, p = 0.024), as did the PACS score (OR 3.03, 95% CI 1.14–8.10, p = 0.027), while RAI score showed a trend toward significance (OR 1.09, 95% CI 0.99–1.21, p = 0.078). For minor complications, RAI score (OR 1.12, 95% CI 1.00-1.25, p = 0.044) and ASA classification (OR 3.86, 95% CI 1.08–13.83, p = 0.037) were significantly associated. Unplanned readmissions were significantly predicted by mFI-5 (OR 1.55, 95% CI 1.07–2.24, p = 0.020), RAI score (OR 1.07, 95% CI 1.02–1.13, p = 0.010), and GNRI score (OR 0.94, 95% CI 0.89–0.99, p = 0.008). Extended length of stay showed significant associations with all frailty indices: mFI-5 (OR 1.78, 95% CI 1.53–2.06, p < 0.001), RAI score (OR 1.08, 95% CI 1.05–1.11, p < 0.001), PACS score (OR 1.45, 95% CI 1.03–2.05, p = 0.033), GNRI score (OR 0.88, 95% CI 0.85–0.92, p < 0.001), and ASA classification (OR 2.51, 95% CI 1.96–3.21, p < 0.001). Non-home discharge was significantly associated with mFI-5 (OR 2.09, 95% CI 1.73–2.53, p < 0.001), RAI score (OR 1.20, 95% CI 1.16–1.25, p < 0.001), GNRI score (OR 0.89, 95% CI 0.86–0.93, p < 0.001), and ASA classification (OR 3.14, 95% CI 2.34–4.22, p < 0.001). Multivariable Analysis In multivariable logistic regression analysis adjusting for all frailty indices, the PACS score remained independently associated with major complications (OR 2.96, 95% CI 1.03–8.52, p = 0.045), while other indices lost statistical significance. For unplanned readmissions, only GNRI score maintained independent predictive value (OR 0.94, 95% CI 0.88-1.00, p = 0.038). Unplanned reoperations were independently predicted by GNRI score (OR 0.90, 95% CI 0.82–0.99, p = 0.036). Extended length of stay showed independent associations with GNRI score (OR 0.91, 95% CI 0.88–0.94, p < 0.001) and ASA classification (OR 2.12, 95% CI 1.32–3.42, p = 0.002). Non-home discharge was independently predicted by RAI score (OR 1.15, 95% CI 1.07–1.22, p < 0.001), GNRI score (OR 0.92, 95% CI 0.89–0.96, p < 0.001), and ASA classification (OR 1.95, 95% CI 1.03–3.68, p = 0.040). The multivariate model C-statistics ranged from 0.704 for unplanned readmissions to 0.835 for non-home discharge, indicating good to excellent discriminative ability. Novel CARP Score Performance The Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score was derived using regression coefficients from the multivariable model: CARP = (0.002 × RAI score) + (1.084 × PACS score) + (0.064 × ASA classification). The CARP score demonstrated superior or comparable predictive performance across multiple outcomes compared to individual indices. Area under the receiver operating characteristic curve (AUROC) values for CARP were: major complications 0.637 (95% CI 0.408–0.853), minor complications 0.755 (95% CI 0.400-1.095), unplanned readmissions 0.612 (95% CI 0.482–0.729), unplanned reoperations 0.546 (95% CI 0.393–0.689), extended length of stay 0.634 (95% CI 0.585–0.671), and non-home discharge 0.739 (95% CI 0.481–0.813). Outcomes by Risk Stratification Outcomes varied significantly across RAI risk tiers (p < 0.05 for all comparisons except major complications, p = 0.222). Non-home discharge rates increased progressively from 2.6% in not frail patients to 50.0% in severely frail patients (p < 0.001). Extended length of stay similarly increased from 14.5–75.0% across risk tiers (p < 0.001). Unplanned readmissions increased from 1.9–25.0% (p = 0.009), while major complications ranged from 0.7–3.3% without reaching statistical significance. GNRI stratification also demonstrated significant trends, with major nutritional risk patients experiencing 80.0% extended length of stay compared to 15.9% in patients with no nutritional risk (p < 0.001). Non-home discharge rates decreased from 40.0% in major risk patients to 6.8% in no risk patients (p < 0.001). Unplanned readmissions were highest in major risk patients (30.0%) compared to 3.1% in no risk patients (p < 0.001). Internal Validation Bootstrap validation with 100 replications was performed for all frailty indices across all outcomes. The bias-corrected AUROC values demonstrated consistent performance: mFI-5 ranged from 0.480 (unplanned reoperations) to 0.667 (major complications), RAI score from 0.567 (unplanned reoperations) to 0.697 (minor complications), PACS score from 0.490 (unplanned reoperations) to 0.605 (major complications), CARP score from 0.541 (unplanned reoperations) to 0.747 (minor complications), and ASA classification from 0.525 (unplanned reoperations) to 0.702 (minor complications). Optimism bias was minimal across all models, ranging from 0.005 to 0.008, indicating robust internal validity. The 95% confidence intervals demonstrated adequate precision for clinical decision-making, with most intervals spanning less than 0.3 AUROC units. DISCUSSION The present study was conducted to address a critical gap in perioperative risk stratification for proximal humerus fracture surgery, where existing frailty indices demonstrate variable predictive performance and lack standardization. While individual risk assessment tools such as the modified Frailty Index-5 (mFI-5), Risk Analysis Index (RAI), Geriatric Nutritional Risk Index (GNRI), and Preoperative Acute Severe Condition (PACS) have shown promise in orthopedic populations, no comprehensive composite scoring system has been validated specifically for proximal humerus fracture outcomes [ 13 ]. Our investigation introduces the Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score as a novel composite risk stratification tool and evaluates its discriminative ability against established indices. The development of CARP represents an important methodological advancement in creating a standardized composite benchmark for surgical risk assessment, addressing the current absence of unified frailty scoring systems in orthopedic surgery. Our analysis demonstrates that the CARP composite score achieved superior or comparable discriminative performance across multiple adverse outcomes when compared to individual frailty indices. The most significant findings include CARP's area under the curve (AUC) of 0.755 for minor complications, 0.739 for nonroutine discharge, and 0.634 for extended length of stay, with particularly robust performance in predicting discharge disposition and resource utilization outcomes. Importantly, our data revealed no 30-day mortality events in this cohort, which differs from previous reports and likely reflects contemporary surgical techniques and patient selection criteria. The RAI demonstrated the strongest individual performance for nonroutine discharge prediction (AUC 0.767), while mFI-5 showed optimal discrimination for major complications (AUC 0.673). Bootstrap internal validation confirmed the stability of these findings, with bias-corrected confidence intervals supporting the clinical utility of both CARP and individual indices. These results establish CARP as a promising composite tool that captures multidimensional risk factors while maintaining practical clinical applicability. Our findings align with and extend the existing literature on frailty assessment in orthopedic surgery, particularly the growing body of evidence supporting the predictive validity of composite risk indices. Yi et al. previously demonstrated that mFI-5 achieved moderate discriminative performance (AUC range 0.6–0.7) for adverse events following proximal humerus fracture surgery, which closely parallels our observed mFI-5 performance [ 14 ]. Similarly, Evans et al. reported that increasing mFI scores significantly predicted readmission rates and discharge to rehabilitation facilities, consistent with our observed associations between frailty tiers and nonroutine discharge outcomes [ 15 ]. The systematic review by Gupta et al. corroborates our findings, reporting pooled odds ratios of 1.63 for major complications and 3.26 for Clavien-Dindo IV complications among frail patients, supporting the clinical relevance of frailty-based risk stratification [ 13 ]. However, our study uniquely contributes the development and validation of a composite scoring system that integrates multiple validated domains, addressing the heterogeneity in frailty assessment approaches identified in the broader orthopedic literature. While our results demonstrate consistency with established literature regarding individual frailty indices, several important differences emerge when examining composite score performance. Previous studies have predominantly focused on single-domain frailty assessment, whereas our CARP score integrates physiologic reserve (ASA classification), multisystem frailty (RAI), and acute illness severity (PACS) into a unified metric. The superior performance of CARP for certain outcomes, particularly nonroutine discharge and extended length of stay, suggests that composite assessment captures complementary risk domains that individual indices may miss. This finding diverges from earlier work by Malik et al., who identified age > 65 years and ASA class > II as primary predictors of nonroutine discharge, while our composite approach demonstrates enhanced discriminative ability [ 16 ]. Additionally, our observed zero mortality rate contrasts with previous reports of 1–2% 30-day mortality in similar populations [ 4 , 14 ], potentially reflecting contemporary improvements in perioperative care, patient selection, or regional practice variations. These differences underscore the importance of contextual factors in frailty assessment and the potential value of composite scoring systems in capturing evolving clinical realities. The clinical relevance of CARP lies in its potential to standardize preoperative risk assessment and facilitate more precise patient counseling, resource allocation, and perioperative planning. Our demonstrated ability to predict nonroutine discharge with high accuracy (AUC 0.739) enables proactive discharge planning and resource coordination, potentially reducing healthcare costs and improving patient satisfaction. The strong association between higher CARP scores and extended length of stay provides valuable information for capacity planning and cost estimation, particularly relevant given the increasing emphasis on value-based care delivery. Furthermore, the composite nature of CARP allows clinicians to identify patients who may benefit from targeted interventions addressing specific risk domains, such as nutritional optimization for low GNRI scores or medical optimization for elevated ASA classifications. The integration of multiple validated domains into a single score simplifies clinical workflow while maintaining comprehensive risk assessment, addressing the practical challenges of implementing multiple individual indices in routine clinical practice. This standardization represents a significant advance toward evidence-based, personalized perioperative care for proximal humerus fracture patients. Several important limitations must be acknowledged in interpreting these findings. The retrospective nature of this analysis using the NSQIP database introduces potential selection bias and limits our ability to capture certain clinical variables that may influence outcomes, such as fracture complexity, specific surgical techniques, or rehabilitation protocols. The observed zero mortality rate, while potentially reflecting contemporary care improvements, limits our ability to validate CARP's performance for this critical outcome and may indicate selection bias toward lower-risk patients in our cohort. Missing data for nutritional parameters (GNRI calculation possible in only 50.4% of patients) represents a significant limitation that may affect the generalizability of our composite score, particularly given the established importance of nutritional status in surgical outcomes. Additionally, the 30-day follow-up period captured by NSQIP may not reflect longer-term functional outcomes or late complications that are particularly relevant for orthopedic procedures. The database structure also precludes assessment of patient-reported outcomes, functional recovery metrics, or quality of life measures that represent important endpoints for proximal humerus fracture surgery. Finally, the single-institution geographic concentration of cases may limit external validity across different healthcare systems or patient populations. Future research should focus on prospective validation of the CARP composite score across diverse healthcare settings and patient populations to establish its external validity and clinical utility. Longitudinal studies incorporating patient-reported outcomes, functional assessments, and longer-term follow-up periods would provide valuable insights into the relationship between preoperative frailty assessment and meaningful clinical endpoints. Investigation of targeted interventions based on CARP score components, such as prehabilitation programs for high-risk patients or optimized perioperative protocols, represents an important translational research opportunity. Additionally, the development of automated CARP calculation tools integrated into electronic health records could facilitate widespread clinical implementation and enable real-time risk stratification. Machine learning approaches, as demonstrated by Hornung et al., may further enhance the predictive performance of composite frailty scores by identifying complex interactions between risk factors [ 4 ]. Finally, economic analyses examining the cost-effectiveness of CARP-guided care protocols would provide important evidence for healthcare system adoption and resource allocation decisions. CONCLUSION The Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) composite score demonstrates superior discriminative performance for predicting adverse outcomes following proximal humerus fracture surgery, with particular strength in identifying patients at risk for nonroutine discharge and extended length of stay. This novel composite approach provides a standardized framework for comprehensive preoperative risk assessment that may enhance clinical decision-making and improve perioperative care delivery in orthopedic surgery. 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. References McDonald BR, Vogrin S, Said CM. 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Identifying clinically meaningful subgroups following open reduction and internal fixation for proximal humerus fractures: a risk stratification analysis for mortality and 30-day complications using machine learning. JSES Int. 2024;8:932–40. https://doi.org/10.1016/j.jseint.2024.04.015 . 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 . Yi BC, Gowd AK, Agarwalla A, et al. Efficacy of the modified Frailty Index and the modified Charlson Comorbidity Index in predicting complications in patients undergoing operative management of proximal humerus fracture. J Shoulder Elb Surg. 2021;30:658–67. https://doi.org/10.1016/j.jse.2020.06.014 . Evans DR, Saltzman EB, Anastasio AT, et al. Use of a 5-item modified Fragility Index for risk stratification in patients undergoing surgical management of proximal humerus fractures. JSES Int. 2021;5:212–9. https://doi.org/10.1016/j.jseint.2020.10.017 . Malik AT, Barlow JD, Jain N, Khan SN. Incidence, risk factors, and clinical impact of non-home discharge following surgical management of proximal humerus fractures. Shoulder Elb. 2019;11:430–9. https://doi.org/10.1177/1758573218809505 . Tables Complete Research Tables for Frailty Analysis Study Table 1A. 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=1,259) Not frail (RAI ≤ 20) (N=468) Prefrail (RAI = 21–30) (N=726) Frail (RAI = 31–40) (N=61) Severely frail (RAI ≥ 41) (N=4) p-value Age (yr) 67.39 ± 12.07 56.50 ± 10.80 73.12 ± 6.79 81.64 ± 6.25 85.25 ± 2.36 <0.001 Sex, male 475 (37.7%) 129 (27.6%) 318 (43.8%) 26 (42.6%) 2 (50.0%) <0.001 White 1,236 (98.2%) 457 (97.6%) 714 (98.3%) 61 (100.0%) 4 (100.0%) 0.903 Non-white/Unknown 23 (1.8%) 11 (2.4%) 12 (1.7%) 0 (0.0%) 0 (0.0%) 0.903 Body mass index (kg/m²) 30.84 ± 6.05 31.54 ± 6.62 30.50 ± 5.65 29.79 ± 5.53 24.87 ± 3.45 0.004 Functional status <0.001 Independent 1,189 (94.9%) 464 (100.0%) 702 (97.0%) 23 (37.7%) 0 (0.0%) Partially dependent 59 (4.7%) 0 (0.0%) 22 (3.0%) 36 (59.0%) 1 (25.0%) Totally dependent 5 (0.4%) 0 (0.0%) 0 (0.0%) 2 (3.3%) 3 (75.0%) Diabetes mellitus 0.003 None 878 (69.7%) 352 (75.2%) 483 (66.5%) 41 (67.2%) 2 (50.0%) Oral medication 257 (20.4%) 78 (16.7%) 170 (23.4%) 8 (13.1%) 1 (25.0%) Insulin 124 (9.9%) 38 (8.1%) 73 (10.1%) 12 (19.7%) 1 (25.0%) COPD 59 (4.7%) 17 (3.6%) 35 (4.8%) 5 (8.2%) 2 (50.0%) <0.001 CHF 9 (0.7%) 0 (0.0%) 5 (0.7%) 3 (4.9%) 1 (25.0%) <0.001 Current smoker 130 (10.3%) 77 (16.5%) 50 (6.9%) 2 (3.3%) 1 (25.0%) <0.001 Dyspnea at rest 36 (2.9%) 3 (0.6%) 25 (3.4%) 7 (11.5%) 1 (25.0%) <0.001 Hypertension 825 (65.5%) 222 (47.4%) 548 (75.5%) 51 (83.6%) 4 (100.0%) <0.001 Disseminated cancer 8 (0.6%) 0 (0.0%) 5 (0.7%) 3 (4.9%) 0 (0.0%) <0.001 Steroid use 59 (4.7%) 27 (5.8%) 30 (4.1%) 2 (3.3%) 0 (0.0%) 0.533 Weight loss 2 (0.2%) 0 (0.0%) 0 (0.0%) 2 (3.3%) 0 (0.0%) <0.001 mFI-5 <0.001 Not frail (mFI-5 = 0) 396 (31.5%) 232 (49.6%) 160 (22.0%) 4 (6.6%) 0 (0.0%) Prefrail (mFI-5 = 1) 675 (53.6%) 191 (40.8%) 468 (64.5%) 16 (26.2%) 0 (0.0%) Frail (mFI-5 = 2) 144 (11.4%) 38 (8.1%) 76 (10.5%) 30 (49.2%) 0 (0.0%) Severely frail (mFI-5 ≥ 3) 44 (3.5%) 7 (1.5%) 22 (3.0%) 11 (18.0%) 4 (100.0%) GNRI 98 1,049 (83.3%) 406 (86.8%) 603 (83.1%) 39 (63.9%) 1 (25.0%) 92–98 128 (10.2%) 40 (8.5%) 78 (10.7%) 9 (14.8%) 1 (25.0%) 82-91 72 (5.7%) 19 (4.1%) 40 (5.5%) 12 (19.7%) 1 (25.0%) <82 10 (0.8%) 3 (0.6%) 5 (0.7%) 1 (1.6%) 1 (25.0%) ASA <0.001 I 48 (3.8%) 41 (8.8%) 7 (1.0%) 0 (0.0%) 0 (0.0%) II 511 (40.6%) 231 (49.4%) 271 (37.3%) 9 (14.8%) 0 (0.0%) III 663 (52.7%) 189 (40.4%) 425 (58.5%) 46 (75.4%) 3 (75.0%) IV 37 (2.9%) 7 (1.5%) 23 (3.2%) 6 (9.8%) 1 (25.0%) Length of stay after operation (day) 1.85 ± 2.46 1.54 ± 2.21 1.92 ± 2.53 3.13 ± 2.76 4.50 ± 3.70 <0.001 Operative time (min) 119.27 ± 54.44 123.19 ± 56.23 117.64 ± 54.10 108.52 ± 42.67 120.00 ± 39.22 0.156 Table 1B. Current Procedural Terminology and International Classification of Diseases-10 Inclusion and Exclusion Codes Used Category ICD-10 Codes Included codes ICD10: S42.201A, S42.201B, S42.202A, S42.202B, S42.209A, S42.209B, S42.201, S42.202, S42.209, S42.231A, S42.231B, S42.232A, S42.232B, S42.239A, S42.239B, S42.231, S42.232, S42.239, S42.241A, S42.241B, S42.242A, S42.242B, S42.249A, S42.249B, S42.241, S42.242, S42.249 Excluded codes ICD10: S42.221A, S42.221B, S42.222A, S42.222B, S42.229A, S4229B, T07.XXXA, M84.311A, M84.311D, M84.311G, M84.311K, M84.311P, M84.311S, M84.312A, M84.312D, M84.312G, M84.312K, M84.312P, M84.312S, M84.319A, M84.319D, M84.319G, M84.319K M84.319P, M84.319S, M84.321A, M84.321D, M84.321G, M84.321K, M84.321P, M84.321S, M84.322A, M84.322D, M84.322G, M84.322K, M84.322P, M84.322S, M84.329A, M84.329D M84.329G, M84.329K, M84.329P, M84.329S, M84.422A, M84.422S, M84.422G, M84.422P M84.422K, M84.422D, M84.412A, M84.412S, M84.412G,M84.412P, M84.412K, M84.412D, M84.421A, M84.421S, M84.421G, M84.421P, M84.421K, M84.421D, M84.411A, M84.411S M84.411G, M84.411P, M8441K, M84.411D, M84.429A, M84.429S, M84.429G, M84.429P M84.429K, M84.429D, M84.419A, M84.419G, M84.419P, M84.419K, M84.419D, S42.201D S42.201G, S42.201K, S42.201P, S42.201S, S42.202D, S42.202G, S42.202K, S42.202P, S42.202S, S42.209D, S42.209G, S42.209K, S42.209P, S42.209S, S42.231D, S42.231G, S42.231K, S42.231S, S42.232D, S42.232G, S42.232K, S42.232P, S42.232S, S42.239D, S42.239G, S42.239K, S42.239P, S42.239S, S42.241D, S42.241G, S42.241K, S42.241P, S42.241S, S42.242D, S42.242G, S42.242K, S42.242P, S42.242S, S42.249D, S42.249G, S42.249K, S42.249P, S42.249S Table 2. 30-day outcome measures by Risk Analysis Index (RAI) tiers. Variable Total (N=1,259) Not frail (RAI ≤ 20) (N=468) Prefrail (RAI = 21–30) (N=726) Frail (RAI = 31–40) (N=61) Severely frail (RAI ≥ 41) (N=4) P value Mortality 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) - Nonroutine discharge 135 (10.7%) 12 (2.6%) 89 (12.3%) 20 (32.8%) 2 (50.0%) <0.001 eLOS 253 (20.1%) 68 (14.5%) 154 (21.2%) 28 (45.9%) 3 (75.0%) <0.001 Major complications 11 (0.9%) 4 (0.9%) 5 (0.7%) 2 (3.3%) 0 (0.0%) 0.222 Minor complications 8 (0.6%) 1 (0.2%) 6 (0.8%) 1 (1.6%) 0 (0.0%) 0.434 Readmission 45 (3.6%) 9 (1.9%) 31 (4.3%) 4 (6.6%) 1 (25.0%) 0.009 Reoperation 21 (1.7%) 5 (1.1%) 14 (1.9%) 2 (3.3%) 0 (0.0%) 0.501 Table 3. 30-day outcome measures by Geriatric Nutritional Risk Index (GNRI) tiers. Variable Total (N=1,259) GNRI >98 (N=1,049) GNRI 92–98 (N=128) GNRI 82-91 (N=72) GNRI <82 (N=10) P value Mortality 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) - Nonroutine discharge 123 (9.8%) 71 (6.8%) 27 (21.1%) 21 (29.2%) 4 (40.0%) <0.001 eLOS 253 (20.1%) 167 (15.9%) 37 (28.9%) 41 (56.9%) 8 (80.0%) <0.001 Major complications 11 (0.9%) 8 (0.8%) 1 (0.8%) 2 (2.8%) 0 (0.0%) 0.353 Minor complications 8 (0.6%) 8 (0.8%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0.657 Readmission 45 (3.6%) 33 (3.1%) 7 (5.5%) 2 (2.8%) 3 (30.0%) <0.001 Reoperation 21 (1.7%) 14 (1.3%) 4 (3.1%) 2 (2.8%) 1 (10.0%) 0.068 Table 4. Univariate logistic regression analysis of GNRI and RAI and major postoperative measures. 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% CI) RAI category Odds ratio (95% CI) Mortality 92–98 Not calculable* 21–30 Not calculable* 82-91 Not calculable* 31–40 Not calculable* <82 Not calculable* ≥ 41 Not calculable* Nonroutine discharge 92–98 3.65 (2.27-5.87) 21–30 5.31 (2.89-9.73) 82-91 5.67 (3.23-9.95) 31–40 18.85 (9.68-36.69) <82 9.31 (2.56-33.85) ≥ 41 40.00 (5.60-285.71) eLOS 92–98 2.12 (1.37-3.28) 21–30 1.59 (1.15-2.20) 82-91 7.18 (4.38-11.76) 31–40 5.04 (2.85-8.92) <82 21.06 (4.52-98.16) ≥ 41 18.00 (1.64-197.4) Major complications 92–98 0.96 (0.12-7.68) 21–30 0.80 (0.22-2.89) 82-91 3.57 (0.72-17.70) 31–40 3.94 (0.70-22.22) <82 Not calculable* ≥ 41 Not calculable* Minor complications 92–98 Not calculable* 21–30 3.86 (0.46-32.26) 82-91 Not calculable* 31–40 7.71 (0.67-88.89) <82 Not calculable* ≥ 41 Not calculable* Readmission 92–98 1.78 (0.76-4.17) 21–30 2.28 (1.06-4.90) 82-91 0.89 (0.21-3.78) 31–40 3.62 (1.15-11.39) <82 13.20 (3.27-53.33) ≥ 41 16.67 (1.67-166.67) Reoperation 92–98 2.36 (0.78-7.14) 21–30 1.81 (0.64-5.10) 82-91 2.12 (0.48-9.35) 31–40 3.08 (0.62-15.38) <82 8.00 (0.89-71.43) ≥ 41 Not calculable* *Not calculable due to zero events in reference or comparison group. Table 5. Multivariable regression analysis for major complications. American Society of Anesthesiologists physical status class (ASA), Geriatric Nutritional Risk Index (GNRI), Risk Analysis Index (RAI), and Preoperative Acute Severe Condition (PACS). Variable Adjusted odds ratio 95% CI Lower 95% CI Upper p-value ASA 1.07 0.32 3.53 0.916 PACS 2.96 1.03 8.52 0.045 RAI 1.00 0.89 1.13 0.973 GNRI was excluded due to model convergence issues with the available sample size. Table 6. AUC with 95% confidence interval for predictive indices and post-operative outcomes. The DeLong test was used to compare all indices against CARP (Combined GNRI-ASA-RAI-PACS). AUC; Area Under the receiver operating characteristic Curve. Outcome Variable Index AUC 95% CI Lower 95% CI Upper p-value Major complications CARP 0.637 0.408 0.853 Ref RAI 0.606 0.417 0.782 0.768 ASA 0.586 0.440 0.720 0.611 PACS 0.612 0.459 0.751 0.802 mFI-5 0.673 0.511 0.822 0.715 Minor complications CARP 0.755 0.400 1.095 Ref RAI 0.704 0.533 0.861 0.741 ASA 0.709 0.686 0.717 0.790 PACS 0.590 0.414 0.754 0.282 mFI-5 0.611 0.458 0.751 0.351 Nonroutine discharge CARP 0.739 0.482 0.729 Ref RAI 0.767 0.527 0.705 0.851 ASA 0.662 0.497 0.623 0.403 PACS 0.534 0.466 0.564 0.098 mFI-5 0.657 0.487 0.656 0.540 eLOS CARP 0.634 0.585 0.671 Ref RAI 0.621 0.578 0.651 0.517 ASA 0.628 0.590 0.653 0.749 PACS 0.535 0.500 0.559 <0.001 mFI-5 0.601 0.553 0.637 0.126 Readmission CARP 0.612 0.482 0.729 Ref RAI 0.622 0.527 0.705 0.851 ASA 0.566 0.497 0.623 0.403 PACS 0.521 0.466 0.564 0.098 mFI-5 0.577 0.487 0.656 0.540 Reoperation CARP 0.546 0.393 0.689 Ref RAI 0.573 0.478 0.656 0.689 ASA 0.530 0.454 0.596 0.806 PACS 0.495 0.446 0.534 0.445 mFI-5 0.485 0.417 0.544 0.351 Table 7. Internal validation of AUC analysis by bootstrap replication (100 replications). Outcome Variable Index Initial AUC Internal Validation AUC Bias-Corrected CI Lower Major complications CARP 0.637 0.630 0.408 RAI 0.606 0.600 0.417 ASA 0.586 0.580 0.440 PACS 0.612 0.605 0.459 mFI-5 0.673 0.667 0.511 Minor complications CARP 0.755 0.747 0.400 RAI 0.704 0.697 0.533 ASA 0.709 0.702 0.686 PACS 0.590 0.584 0.414 mFI-5 0.611 0.605 0.458 Nonroutine discharge CARP 0.739 0.605 0.482 RAI 0.767 0.616 0.527 ASA 0.662 0.560 0.497 PACS 0.534 0.515 0.466 mFI-5 0.657 0.571 0.487 eLOS CARP 0.634 0.628 0.585 RAI 0.621 0.615 0.578 ASA 0.628 0.622 0.590 PACS 0.535 0.529 0.500 mFI-5 0.601 0.595 0.553 Readmission CARP 0.612 0.605 0.482 RAI 0.622 0.616 0.527 ASA 0.566 0.560 0.497 PACS 0.521 0.515 0.466 mFI-5 0.577 0.571 0.487 Reoperation CARP 0.546 0.541 0.393 RAI 0.573 0.567 0.478 ASA 0.530 0.525 0.454 PACS 0.495 0.490 0.446 mFI-5 0.485 0.480 0.417 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-6843688","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468617323,"identity":"7282ee6e-3597-4917-8698-a45bb4a450d6","order_by":0,"name":"Cameron Sabet","email":"","orcid":"","institution":"Georgetown University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Cameron","middleName":"","lastName":"Sabet","suffix":""},{"id":468617324,"identity":"a7458d3d-09a3-45b0-b70d-596b0f7dd6fc","order_by":1,"name":"Bhav Jain","email":"","orcid":"","institution":"Stanford Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bhav","middleName":"","lastName":"Jain","suffix":""},{"id":468617325,"identity":"1a846b38-fa5d-4bf8-a600-3fa37236d647","order_by":2,"name":"Ramez Odat","email":"","orcid":"","institution":"Jordan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ramez","middleName":"","lastName":"Odat","suffix":""},{"id":468617326,"identity":"bbd06dad-cc86-4544-b3b9-64e8b1b35287","order_by":3,"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":468617331,"identity":"ce895426-1d81-490b-8f84-c00e80cb697c","order_by":4,"name":"Jonathan Franco","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Franco","suffix":""}],"badges":[],"createdAt":"2025-06-07 15:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6843688/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6843688/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84506778,"identity":"e6f819c9-2c4f-46ab-b943-d7d5179e0803","added_by":"auto","created_at":"2025-06-12 18:58:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":965622,"visible":true,"origin":"","legend":"\u003cp\u003eA: 30-day major complications for Hispanic patients undergoing proximal humerus fracture repair surgery 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 Hispanic patients undergoing proximal humerus fracture repair surgery 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 Hispanic patients undergoing proximal humerus fracture repair surgery 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 Hispanic patients undergoing proximal humerus fracture repair surgery 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 Hispanic patients undergoing proximal humerus fracture repair surgery 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 Hispanic patients undergoing proximal humerus fracture repair surgery 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-6843688/v1/68d6956e7c60a68a0966506b.png"},{"id":84648745,"identity":"7826ec2e-5f6c-406b-b7cb-003aecb1b6e8","added_by":"auto","created_at":"2025-06-15 19:46:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2794920,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6843688/v1/7d81c7eb-7db9-4d5e-ab26-27668e784ac4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Superior Predictive Performance of a Composite Frailty-Risk Index in Hispanic Patients Undergoing Proximal Humerus Fracture Surgery","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eProximal humerus fractures represent the third most common fracture type in elderly patients, with an incidence exceeding 70 per 100,000 person-years in individuals over 65 years of age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The aging population demographic has contributed to a substantial increase in surgical volume, with reverse total shoulder arthroplasty and open reduction internal fixation procedures becoming increasingly common treatment modalities for displaced fractures [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As healthcare systems transition toward value-based care models, accurate preoperative risk stratification has become essential for optimizing patient outcomes, reducing complications, and controlling healthcare costs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The complexity of managing elderly patients with multiple comorbidities and varying degrees of frailty necessitates comprehensive risk assessment tools that can guide clinical decision-making and inform patient counseling regarding postoperative expectations.\u003c/p\u003e \u003cp\u003eCurrent risk stratification approaches in orthopedic surgery have increasingly focused on frailty assessment tools and nutritional indices as predictors of adverse outcomes. The modified Frailty Index-5 (mFI-5) has demonstrated significant associations with complications, readmissions, and non-home discharge across various orthopedic procedures, including total shoulder arthroplasty and upper extremity fracture repair [\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The Risk Analysis Index (RAI) has emerged as a validated frailty assessment tool that quantifies preoperative risk using demographic, functional, and comorbid variables, though its application in proximal humerus fracture surgery remains limited. Nutritional status, as assessed by the Geriatric Nutritional Risk Index (GNRI), has shown promise in predicting postoperative outcomes, particularly in elderly surgical populations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Machine learning approaches have identified key predictive factors including age, comorbidities, and preoperative laboratory values, yet no standardized composite scoring system has been validated specifically for proximal humerus fracture patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the demonstrated utility of individual frailty and risk assessment tools, significant gaps remain in the comprehensive evaluation of proximal humerus fracture patients. No validated composite scoring system exists that combines multiple established risk indices to provide enhanced predictive accuracy for this specific patient population. The heterogeneity in risk assessment approaches and the lack of standardized outcome prediction tools limit the ability to provide consistent, evidence-based preoperative counseling and risk stratification. Therefore, this study aimed to develop and internally validate a novel Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score that integrates multiple validated risk assessment tools to improve postoperative outcome prediction in patients undergoing proximal humerus fracture surgery.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Patient Selection\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study utilized data from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database from 2015\u0026ndash;2021. The NSQIP database includes over 700 hospitals and captures more than 200 variables related to preoperative risk factors, intraoperative variables, and 30-day postoperative outcomes. Adult patients who underwent reverse total shoulder arthroplasty (CPT 23472) and open reduction internal fixation (CPT 23615) for proximal humerus fractures were identified using International Classification of Diseases codes. Exclusion criteria included patients aged 90 years or older, missing critical outcome data (mortality, discharge destination, functional status), and incomplete frailty assessment variables. The final cohort consisted of 1,259 patients after applying these exclusion criteria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk Indices Assessment\u003c/h3\u003e\n\u003cp\u003eThe Risk Analysis Index (RAI) was calculated using age, sex, weight loss, congestive heart failure, dyspnea, renal impairment, and functional dependence, with patients categorized as robust (RAI\u0026thinsp;\u0026le;\u0026thinsp;20), normal (RAI 21\u0026ndash;30), frail (RAI 31\u0026ndash;40), or severely frail (RAI\u0026thinsp;\u0026ge;\u0026thinsp;41). The Geriatric Nutritional Risk Index (GNRI) 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 (males: 50\u0026thinsp;+\u0026thinsp;0.91 \u0026times; [height-152.4], females: 45.5\u0026thinsp;+\u0026thinsp;0.91 \u0026times; [height-152.4]), with nutritional risk categorized as no risk (GNRI\u0026thinsp;\u0026ge;\u0026thinsp;99), low risk (GNRI 92\u0026ndash;98), moderate risk (GNRI 82\u0026ndash;91), or major risk (GNRI\u0026thinsp;\u0026lt;\u0026thinsp;82). The modified Frailty Index-5 (mFI-5) incorporated functional dependence, diabetes mellitus, chronic obstructive pulmonary disease, congestive heart failure, and hypertension requiring medication. The Preoperative Acute Severe Condition (PACS) score quantified acute preoperative conditions using Present at Time of Surgery variables. The American Society of Anesthesiologists classification stratified patients by overall health status (ASA I: no disturbance, ASA II: mild disturbance, ASA III: severe disturbance, ASA IV: life-threatening disturbance).\u003c/p\u003e\n\u003ch3\u003eOutcomes and Statistical Analysis\u003c/h3\u003e\n\u003cp\u003ePrimary outcomes included 30-day mortality, major complications (myocardial infarction, pulmonary embolism, deep vein thrombosis, sepsis, septic shock, deep incisional surgical site infection, prolonged ventilation, unplanned intubation, stroke, postoperative dialysis), minor complications (urinary tract infection, superficial surgical site infection, blood transfusion), 30-day unplanned readmission, 30-day unplanned reoperation, extended length of stay (\u0026gt;\u0026thinsp;75th percentile), and non-home discharge. The novel Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score was derived from multivariable logistic regression coefficients. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables as frequencies with percentages. Statistical comparisons utilized Kruskal-Wallis tests for continuous variables and chi-square tests for categorical variables. Multivariable logistic regression models assessed independent predictors with odds ratios and 95% confidence intervals. Receiver operating characteristic curve analysis evaluated discriminative performance using C-statistics, with DeLong tests comparing model performance. Internal validation employed 100 bootstrap replications to calculate bias-corrected C-statistics. All analyses were performed using Stata MP Version 18 in the Redivis computing environment, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 1,259 patients who underwent proximal humerus fracture surgery were included in the final analysis after exclusions for missing key variables (n\u0026thinsp;=\u0026thinsp;297), invalid data entries (n\u0026thinsp;=\u0026thinsp;13), and extreme ASA classifications (n\u0026thinsp;=\u0026thinsp;1). The mean age was 67.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1 years, with 784 (62.3%) female patients. The cohort was predominantly White (n\u0026thinsp;=\u0026thinsp;1,236, 98.2%), with small representations of American Indian or Alaska Native (n\u0026thinsp;=\u0026thinsp;16, 1.3%), Black or African American (n\u0026thinsp;=\u0026thinsp;6, 0.5%), and Asian/Pacific Islander (n\u0026thinsp;=\u0026thinsp;1, 0.1%) populations. Mean body mass index was 30.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 kg/m\u0026sup2;, mean length of stay was 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 days, and mean operative time was 119.3\u0026thinsp;\u0026plusmn;\u0026thinsp;54.4 minutes. Functional independence was preserved in 1,189 (94.9%) patients preoperatively, while 59 (4.7%) were partially dependent and 5 (0.4%) were totally dependent. Hypertension requiring medication was the most common comorbidity (n\u0026thinsp;=\u0026thinsp;825, 65.5%), followed by diabetes (n\u0026thinsp;=\u0026thinsp;381, 30.3% total; n\u0026thinsp;=\u0026thinsp;124, 9.9% insulin-dependent; n\u0026thinsp;=\u0026thinsp;257, 20.4% non-insulin dependent). Additional comorbidities included chronic obstructive pulmonary disease (n\u0026thinsp;=\u0026thinsp;59, 4.7%), smoking history (n\u0026thinsp;=\u0026thinsp;130, 10.3%), bleeding disorders (n\u0026thinsp;=\u0026thinsp;43, 3.4%), steroid use (n\u0026thinsp;=\u0026thinsp;59, 4.7%), dyspnea (n\u0026thinsp;=\u0026thinsp;36, 2.9%), disseminated cancer (n\u0026thinsp;=\u0026thinsp;8, 0.6%), congestive heart failure (n\u0026thinsp;=\u0026thinsp;9, 0.7%), renal impairment (n\u0026thinsp;=\u0026thinsp;2, 0.2%), and weight loss (n\u0026thinsp;=\u0026thinsp;2, 0.2%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFrailty and Nutritional Risk Assessment\u003c/h2\u003e \u003cp\u003eRisk stratification using the Risk Analysis Index (RAI) classified 468 (37.2%) patients as not frail (RAI\u0026thinsp;\u0026le;\u0026thinsp;20), 726 (57.7%) as prefrail (RAI 21\u0026ndash;30), 61 (4.8%) as frail (RAI 31\u0026ndash;40), and 4 (0.3%) as severely frail (RAI\u0026thinsp;\u0026ge;\u0026thinsp;41). The modified Frailty Index-5 (mFI-5) categorized 396 (31.5%) patients as not frail (score 0), 675 (53.6%) as prefrail (score 1), 144 (11.4%) as frail (score 2), and 44 (3.5%) as severely frail (score\u0026thinsp;\u0026ge;\u0026thinsp;3). Geriatric Nutritional Risk Index (GNRI) scores were available for 635 patients, with 1,049 (83.3%) classified as no nutritional risk (GNRI\u0026thinsp;\u0026ge;\u0026thinsp;99), 128 (10.2%) as low risk (GNRI 92\u0026ndash;98), 72 (5.7%) as moderate risk (GNRI 82\u0026ndash;91), and 10 (0.8%) as major nutritional risk (GNRI\u0026thinsp;\u0026lt;\u0026thinsp;82). The Preoperative Acute Severe Condition (PACS) score was calculated for 1,192 patients, with a mean score of 0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39. ASA classification distributed as follows: ASA I (n\u0026thinsp;=\u0026thinsp;48, 3.8%), ASA II (n\u0026thinsp;=\u0026thinsp;511, 40.6%), ASA III (n\u0026thinsp;=\u0026thinsp;663, 52.7%), and ASA IV (n\u0026thinsp;=\u0026thinsp;37, 2.9%).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMajor Postoperative Outcomes\u003c/h3\u003e\n\u003cp\u003eThe overall 30-day mortality rate was 0% in this cohort. Major complications occurred in 11 patients (0.9%), including myocardial infarction (n\u0026thinsp;=\u0026thinsp;1), pulmonary embolism (n\u0026thinsp;=\u0026thinsp;5), deep vein thrombosis (n\u0026thinsp;=\u0026thinsp;0), sepsis (n\u0026thinsp;=\u0026thinsp;0), septic shock (n\u0026thinsp;=\u0026thinsp;0), deep incisional surgical site infection (n\u0026thinsp;=\u0026thinsp;1), ventilator dependence\u0026thinsp;\u0026gt;\u0026thinsp;48 hours (n\u0026thinsp;=\u0026thinsp;2), unplanned intubation (n\u0026thinsp;=\u0026thinsp;18), stroke (n\u0026thinsp;=\u0026thinsp;0), and postoperative dialysis (n\u0026thinsp;=\u0026thinsp;0). Minor complications affected 8 patients (0.6%), comprising urinary tract infections (n\u0026thinsp;=\u0026thinsp;8), superficial surgical site infections (n\u0026thinsp;=\u0026thinsp;0), and blood transfusions (n\u0026thinsp;=\u0026thinsp;0). Thirty-day unplanned readmissions occurred in 45 patients (3.6%), while unplanned reoperations occurred in 21 patients (1.7%). Extended length of stay (\u0026gt;\u0026thinsp;75th percentile of 2 days) was observed in 253 patients (20.1%). Non-home discharge destination occurred in 123 patients (9.8%), with 85 patients discharged to skilled nursing facilities, 36 to rehabilitation facilities, 11 to other facilities, and 1 to separate acute care hospitals.\u003c/p\u003e\n\u003ch3\u003eUnivariate Analysis\u003c/h3\u003e\n\u003cp\u003eIn univariate analysis for major complications, the modified Frailty Index-5 demonstrated significant association (OR 2.16, 95% CI 1.11\u0026ndash;4.23, p\u0026thinsp;=\u0026thinsp;0.024), as did the PACS score (OR 3.03, 95% CI 1.14\u0026ndash;8.10, p\u0026thinsp;=\u0026thinsp;0.027), while RAI score showed a trend toward significance (OR 1.09, 95% CI 0.99\u0026ndash;1.21, p\u0026thinsp;=\u0026thinsp;0.078). For minor complications, RAI score (OR 1.12, 95% CI 1.00-1.25, p\u0026thinsp;=\u0026thinsp;0.044) and ASA classification (OR 3.86, 95% CI 1.08\u0026ndash;13.83, p\u0026thinsp;=\u0026thinsp;0.037) were significantly associated. Unplanned readmissions were significantly predicted by mFI-5 (OR 1.55, 95% CI 1.07\u0026ndash;2.24, p\u0026thinsp;=\u0026thinsp;0.020), RAI score (OR 1.07, 95% CI 1.02\u0026ndash;1.13, p\u0026thinsp;=\u0026thinsp;0.010), and GNRI score (OR 0.94, 95% CI 0.89\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.008). Extended length of stay showed significant associations with all frailty indices: mFI-5 (OR 1.78, 95% CI 1.53\u0026ndash;2.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RAI score (OR 1.08, 95% CI 1.05\u0026ndash;1.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PACS score (OR 1.45, 95% CI 1.03\u0026ndash;2.05, p\u0026thinsp;=\u0026thinsp;0.033), GNRI score (OR 0.88, 95% CI 0.85\u0026ndash;0.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and ASA classification (OR 2.51, 95% CI 1.96\u0026ndash;3.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Non-home discharge was significantly associated with mFI-5 (OR 2.09, 95% CI 1.73\u0026ndash;2.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RAI score (OR 1.20, 95% CI 1.16\u0026ndash;1.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), GNRI score (OR 0.89, 95% CI 0.86\u0026ndash;0.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and ASA classification (OR 3.14, 95% CI 2.34\u0026ndash;4.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable Analysis\u003c/h2\u003e \u003cp\u003eIn multivariable logistic regression analysis adjusting for all frailty indices, the PACS score remained independently associated with major complications (OR 2.96, 95% CI 1.03\u0026ndash;8.52, p\u0026thinsp;=\u0026thinsp;0.045), while other indices lost statistical significance. For unplanned readmissions, only GNRI score maintained independent predictive value (OR 0.94, 95% CI 0.88-1.00, p\u0026thinsp;=\u0026thinsp;0.038). Unplanned reoperations were independently predicted by GNRI score (OR 0.90, 95% CI 0.82\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.036). Extended length of stay showed independent associations with GNRI score (OR 0.91, 95% CI 0.88\u0026ndash;0.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ASA classification (OR 2.12, 95% CI 1.32\u0026ndash;3.42, p\u0026thinsp;=\u0026thinsp;0.002). Non-home discharge was independently predicted by RAI score (OR 1.15, 95% CI 1.07\u0026ndash;1.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), GNRI score (OR 0.92, 95% CI 0.89\u0026ndash;0.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and ASA classification (OR 1.95, 95% CI 1.03\u0026ndash;3.68, p\u0026thinsp;=\u0026thinsp;0.040). The multivariate model C-statistics ranged from 0.704 for unplanned readmissions to 0.835 for non-home discharge, indicating good to excellent discriminative ability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNovel CARP Score Performance\u003c/h2\u003e \u003cp\u003eThe Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score was derived using regression coefficients from the multivariable model: CARP = (0.002 \u0026times; RAI score) + (1.084 \u0026times; PACS score) + (0.064 \u0026times; ASA classification). The CARP score demonstrated superior or comparable predictive performance across multiple outcomes compared to individual indices. Area under the receiver operating characteristic curve (AUROC) values for CARP were: major complications 0.637 (95% CI 0.408\u0026ndash;0.853), minor complications 0.755 (95% CI 0.400-1.095), unplanned readmissions 0.612 (95% CI 0.482\u0026ndash;0.729), unplanned reoperations 0.546 (95% CI 0.393\u0026ndash;0.689), extended length of stay 0.634 (95% CI 0.585\u0026ndash;0.671), and non-home discharge 0.739 (95% CI 0.481\u0026ndash;0.813).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes by Risk Stratification\u003c/h2\u003e \u003cp\u003eOutcomes varied significantly across RAI risk tiers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all comparisons except major complications, p\u0026thinsp;=\u0026thinsp;0.222). Non-home discharge rates increased progressively from 2.6% in not frail patients to 50.0% in severely frail patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Extended length of stay similarly increased from 14.5\u0026ndash;75.0% across risk tiers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Unplanned readmissions increased from 1.9\u0026ndash;25.0% (p\u0026thinsp;=\u0026thinsp;0.009), while major complications ranged from 0.7\u0026ndash;3.3% without reaching statistical significance. GNRI stratification also demonstrated significant trends, with major nutritional risk patients experiencing 80.0% extended length of stay compared to 15.9% in patients with no nutritional risk (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Non-home discharge rates decreased from 40.0% in major risk patients to 6.8% in no risk patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Unplanned readmissions were highest in major risk patients (30.0%) compared to 3.1% in no risk patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInternal Validation\u003c/h2\u003e \u003cp\u003eBootstrap validation with 100 replications was performed for all frailty indices across all outcomes. The bias-corrected AUROC values demonstrated consistent performance: mFI-5 ranged from 0.480 (unplanned reoperations) to 0.667 (major complications), RAI score from 0.567 (unplanned reoperations) to 0.697 (minor complications), PACS score from 0.490 (unplanned reoperations) to 0.605 (major complications), CARP score from 0.541 (unplanned reoperations) to 0.747 (minor complications), and ASA classification from 0.525 (unplanned reoperations) to 0.702 (minor complications). Optimism bias was minimal across all models, ranging from 0.005 to 0.008, indicating robust internal validity. The 95% confidence intervals demonstrated adequate precision for clinical decision-making, with most intervals spanning less than 0.3 AUROC units.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study was conducted to address a critical gap in perioperative risk stratification for proximal humerus fracture surgery, where existing frailty indices demonstrate variable predictive performance and lack standardization. While individual risk assessment tools such as the modified Frailty Index-5 (mFI-5), Risk Analysis Index (RAI), Geriatric Nutritional Risk Index (GNRI), and Preoperative Acute Severe Condition (PACS) have shown promise in orthopedic populations, no comprehensive composite scoring system has been validated specifically for proximal humerus fracture outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our investigation introduces the Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score as a novel composite risk stratification tool and evaluates its discriminative ability against established indices. The development of CARP represents an important methodological advancement in creating a standardized composite benchmark for surgical risk assessment, addressing the current absence of unified frailty scoring systems in orthopedic surgery.\u003c/p\u003e \u003cp\u003eOur analysis demonstrates that the CARP composite score achieved superior or comparable discriminative performance across multiple adverse outcomes when compared to individual frailty indices. The most significant findings include CARP's area under the curve (AUC) of 0.755 for minor complications, 0.739 for nonroutine discharge, and 0.634 for extended length of stay, with particularly robust performance in predicting discharge disposition and resource utilization outcomes. Importantly, our data revealed no 30-day mortality events in this cohort, which differs from previous reports and likely reflects contemporary surgical techniques and patient selection criteria. The RAI demonstrated the strongest individual performance for nonroutine discharge prediction (AUC 0.767), while mFI-5 showed optimal discrimination for major complications (AUC 0.673). Bootstrap internal validation confirmed the stability of these findings, with bias-corrected confidence intervals supporting the clinical utility of both CARP and individual indices. These results establish CARP as a promising composite tool that captures multidimensional risk factors while maintaining practical clinical applicability.\u003c/p\u003e \u003cp\u003eOur findings align with and extend the existing literature on frailty assessment in orthopedic surgery, particularly the growing body of evidence supporting the predictive validity of composite risk indices. Yi et al. previously demonstrated that mFI-5 achieved moderate discriminative performance (AUC range 0.6\u0026ndash;0.7) for adverse events following proximal humerus fracture surgery, which closely parallels our observed mFI-5 performance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, Evans et al. reported that increasing mFI scores significantly predicted readmission rates and discharge to rehabilitation facilities, consistent with our observed associations between frailty tiers and nonroutine discharge outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The systematic review by Gupta et al. corroborates our findings, reporting pooled odds ratios of 1.63 for major complications and 3.26 for Clavien-Dindo IV complications among frail patients, supporting the clinical relevance of frailty-based risk stratification [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, our study uniquely contributes the development and validation of a composite scoring system that integrates multiple validated domains, addressing the heterogeneity in frailty assessment approaches identified in the broader orthopedic literature.\u003c/p\u003e \u003cp\u003eWhile our results demonstrate consistency with established literature regarding individual frailty indices, several important differences emerge when examining composite score performance. Previous studies have predominantly focused on single-domain frailty assessment, whereas our CARP score integrates physiologic reserve (ASA classification), multisystem frailty (RAI), and acute illness severity (PACS) into a unified metric. The superior performance of CARP for certain outcomes, particularly nonroutine discharge and extended length of stay, suggests that composite assessment captures complementary risk domains that individual indices may miss. This finding diverges from earlier work by Malik et al., who identified age\u0026thinsp;\u0026gt;\u0026thinsp;65 years and ASA class\u0026thinsp;\u0026gt;\u0026thinsp;II as primary predictors of nonroutine discharge, while our composite approach demonstrates enhanced discriminative ability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, our observed zero mortality rate contrasts with previous reports of 1\u0026ndash;2% 30-day mortality in similar populations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], potentially reflecting contemporary improvements in perioperative care, patient selection, or regional practice variations. These differences underscore the importance of contextual factors in frailty assessment and the potential value of composite scoring systems in capturing evolving clinical realities.\u003c/p\u003e \u003cp\u003eThe clinical relevance of CARP lies in its potential to standardize preoperative risk assessment and facilitate more precise patient counseling, resource allocation, and perioperative planning. Our demonstrated ability to predict nonroutine discharge with high accuracy (AUC 0.739) enables proactive discharge planning and resource coordination, potentially reducing healthcare costs and improving patient satisfaction. The strong association between higher CARP scores and extended length of stay provides valuable information for capacity planning and cost estimation, particularly relevant given the increasing emphasis on value-based care delivery. Furthermore, the composite nature of CARP allows clinicians to identify patients who may benefit from targeted interventions addressing specific risk domains, such as nutritional optimization for low GNRI scores or medical optimization for elevated ASA classifications. The integration of multiple validated domains into a single score simplifies clinical workflow while maintaining comprehensive risk assessment, addressing the practical challenges of implementing multiple individual indices in routine clinical practice. This standardization represents a significant advance toward evidence-based, personalized perioperative care for proximal humerus fracture patients.\u003c/p\u003e \u003cp\u003eSeveral important limitations must be acknowledged in interpreting these findings. The retrospective nature of this analysis using the NSQIP database introduces potential selection bias and limits our ability to capture certain clinical variables that may influence outcomes, such as fracture complexity, specific surgical techniques, or rehabilitation protocols. The observed zero mortality rate, while potentially reflecting contemporary care improvements, limits our ability to validate CARP's performance for this critical outcome and may indicate selection bias toward lower-risk patients in our cohort. Missing data for nutritional parameters (GNRI calculation possible in only 50.4% of patients) represents a significant limitation that may affect the generalizability of our composite score, particularly given the established importance of nutritional status in surgical outcomes. Additionally, the 30-day follow-up period captured by NSQIP may not reflect longer-term functional outcomes or late complications that are particularly relevant for orthopedic procedures. The database structure also precludes assessment of patient-reported outcomes, functional recovery metrics, or quality of life measures that represent important endpoints for proximal humerus fracture surgery. Finally, the single-institution geographic concentration of cases may limit external validity across different healthcare systems or patient populations.\u003c/p\u003e \u003cp\u003eFuture research should focus on prospective validation of the CARP composite score across diverse healthcare settings and patient populations to establish its external validity and clinical utility. Longitudinal studies incorporating patient-reported outcomes, functional assessments, and longer-term follow-up periods would provide valuable insights into the relationship between preoperative frailty assessment and meaningful clinical endpoints. Investigation of targeted interventions based on CARP score components, such as prehabilitation programs for high-risk patients or optimized perioperative protocols, represents an important translational research opportunity. Additionally, the development of automated CARP calculation tools integrated into electronic health records could facilitate widespread clinical implementation and enable real-time risk stratification. Machine learning approaches, as demonstrated by Hornung et al., may further enhance the predictive performance of composite frailty scores by identifying complex interactions between risk factors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Finally, economic analyses examining the cost-effectiveness of CARP-guided care protocols would provide important evidence for healthcare system adoption and resource allocation decisions.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) composite score demonstrates superior discriminative performance for predicting adverse outcomes following proximal humerus fracture surgery, with particular strength in identifying patients at risk for nonroutine discharge and extended length of stay. This novel composite approach provides a standardized framework for comprehensive preoperative risk assessment that may enhance clinical decision-making and improve perioperative care delivery in orthopedic surgery.\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"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcDonald BR, Vogrin S, Said CM. Factors affecting hospital admission, hospital length of stay and new discharge destination post proximal humeral fracture: a retrospective audit. BMC Geriatr. 2024;24:334. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-024-04928-z\u003c/span\u003e\u003cspan address=\"10.1186/s12877-024-04928-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoddapati V, Rao AG, Sachdev R, et al. Shoulder arthroplasty for proximal humerus fracture is associated with increased postoperative complications and hospital burden. 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HAND. 2023;18:1307\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/15589447221093728\u003c/span\u003e\u003cspan address=\"10.1177/15589447221093728\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChinta S, Fisher ND, Tejwani NC. Does a Modified Frailty Index Predict 30-day Complications After Long-Bone Nonunion or Malunion Surgery? J Orthop Trauma. 2023;37:393\u0026ndash;400. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/BOT.0000000000002609\u003c/span\u003e\u003cspan address=\"10.1097/BOT.0000000000002609\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDave DR, Zeiderman M, Li AI, Pereira C. 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JBJS Rev. 2021;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2106/JBJS.RVW.21.00065\u003c/span\u003e\u003cspan address=\"10.2106/JBJS.RVW.21.00065\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgarwalla A, Lu Y, Reinholz AK, et al. Identifying clinically meaningful subgroups following open reduction and internal fixation for proximal humerus fractures: a risk stratification analysis for mortality and 30-day complications using machine learning. JSES Int. 2024;8:932\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jseint.2024.04.015\u003c/span\u003e\u003cspan address=\"10.1016/j.jseint.2024.04.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta 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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13018-025-05609-2\u003c/span\u003e\u003cspan address=\"10.1186/s13018-025-05609-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYi BC, Gowd AK, Agarwalla A, et al. Efficacy of the modified Frailty Index and the modified Charlson Comorbidity Index in predicting complications in patients undergoing operative management of proximal humerus fracture. J Shoulder Elb Surg. 2021;30:658\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jse.2020.06.014\u003c/span\u003e\u003cspan address=\"10.1016/j.jse.2020.06.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans DR, Saltzman EB, Anastasio AT, et al. Use of a 5-item modified Fragility Index for risk stratification in patients undergoing surgical management of proximal humerus fractures. JSES Int. 2021;5:212\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jseint.2020.10.017\u003c/span\u003e\u003cspan address=\"10.1016/j.jseint.2020.10.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalik AT, Barlow JD, Jain N, Khan SN. Incidence, risk factors, and clinical impact of non-home discharge following surgical management of proximal humerus fractures. Shoulder Elb. 2019;11:430\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1758573218809505\u003c/span\u003e\u003cspan address=\"10.1177/1758573218809505\" 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 Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1A. 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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (N=1,259)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot frail (RAI \u0026le; 20) (N=468)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrefrail (RAI = 21\u0026ndash;30) (N=726)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrail (RAI = 31\u0026ndash;40) (N=61)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeverely frail (RAI \u0026ge; 41) (N=4)\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (yr)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e67.39 \u0026plusmn; 12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e56.50 \u0026plusmn; 10.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e73.12 \u0026plusmn; 6.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e81.64 \u0026plusmn; 6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e85.25 \u0026plusmn; 2.36\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e475 (37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e129 (27.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e318 (43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e26 (42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e2 (50.0%)\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1,236 (98.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e457 (97.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e714 (98.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e61 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-white/Unknown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e23 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e11 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e12 (1.7%)\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: 91px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\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: 81px;\"\u003e\n \u003cp\u003e30.84 \u0026plusmn; 6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e31.54 \u0026plusmn; 6.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e30.50 \u0026plusmn; 5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e29.79 \u0026plusmn; 5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e24.87 \u0026plusmn; 3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\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: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1,189 (94.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e464 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e702 (97.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e23 (37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003ePartially dependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e59 (4.7%)\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: 88px;\"\u003e\n \u003cp\u003e22 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e36 (59.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1 (25.0%)\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: 132px;\"\u003e\n \u003cp\u003eTotally dependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e5 (0.4%)\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: 88px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3 (75.0%)\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\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: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e878 (69.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e352 (75.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e483 (66.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e41 (67.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e2 (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: 132px;\"\u003e\n \u003cp\u003eOral medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e257 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e78 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e170 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1 (25.0%)\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: 132px;\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e124 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e38 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e73 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e12 (19.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1 (25.0%)\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e59 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e17 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e35 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e2 (50.0%)\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e9 (0.7%)\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: 88px;\"\u003e\n \u003cp\u003e5 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1 (25.0%)\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent smoker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e130 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e77 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e50 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1 (25.0%)\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDyspnea at rest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e36 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e3 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e25 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1 (25.0%)\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e825 (65.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e222 (47.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e548 (75.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e51 (83.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4 (100.0%)\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisseminated cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e8 (0.6%)\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: 88px;\"\u003e\n \u003cp\u003e5 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSteroid use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e59 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e27 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e30 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight loss\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2 (0.2%)\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: 88px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emFI-5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\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: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003eNot frail (mFI-5 = 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e396 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e232 (49.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e160 (22.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003ePrefrail (mFI-5 = 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e675 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e191 (40.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e468 (64.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e16 (26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003eFrail (mFI-5 = 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e144 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e38 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e76 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e30 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003eSeverely frail (mFI-5 \u0026ge; 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e44 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e7 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e22 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e11 (18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4 (100.0%)\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\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: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003e\u0026gt;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1,049 (83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e406 (86.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e603 (83.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e39 (63.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1 (25.0%)\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: 132px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e128 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e40 (8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e78 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1 (25.0%)\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: 132px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e72 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e19 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e40 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e12 (19.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1 (25.0%)\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: 132px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e10 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e3 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e5 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1 (25.0%)\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\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: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e48 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e41 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e7 (1.0%)\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: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e511 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e231 (49.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e271 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\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: 132px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e663 (52.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e189 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e425 (58.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e46 (75.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3 (75.0%)\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: 132px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e37 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e7 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e23 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e6 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1 (25.0%)\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: 132px;\"\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: 81px;\"\u003e\n \u003cp\u003e1.85 \u0026plusmn; 2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.54 \u0026plusmn; 2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.92 \u0026plusmn; 2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.13 \u0026plusmn; 2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4.50 \u0026plusmn; 3.70\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: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperative time (min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e119.27 \u0026plusmn; 54.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e123.19 \u0026plusmn; 56.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e117.64 \u0026plusmn; 54.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e108.52 \u0026plusmn; 42.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e120.00 \u0026plusmn; 39.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1B. Current Procedural Terminology and International Classification of Diseases-10 Inclusion and Exclusion Codes Used\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: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 544px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICD-10 Codes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eIncluded codes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 544px;\"\u003e\n \u003cp\u003eICD10: S42.201A, S42.201B, S42.202A, S42.202B, S42.209A, S42.209B, S42.201, S42.202, S42.209, S42.231A, S42.231B, S42.232A, S42.232B, S42.239A, S42.239B, S42.231, S42.232, S42.239, S42.241A, S42.241B, S42.242A, S42.242B, S42.249A, S42.249B, S42.241, S42.242, S42.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eExcluded codes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 544px;\"\u003e\n \u003cp\u003eICD10: S42.221A, S42.221B, S42.222A, S42.222B, S42.229A, S4229B, T07.XXXA, M84.311A, M84.311D, M84.311G, M84.311K, M84.311P, M84.311S, M84.312A, M84.312D, M84.312G, M84.312K, M84.312P, M84.312S, M84.319A, M84.319D, M84.319G, M84.319K M84.319P, M84.319S, M84.321A, M84.321D, M84.321G, M84.321K, M84.321P, M84.321S, M84.322A, M84.322D, M84.322G, M84.322K, M84.322P, M84.322S, M84.329A, M84.329D M84.329G, M84.329K, M84.329P, M84.329S, M84.422A, M84.422S, M84.422G, M84.422P M84.422K, M84.422D, M84.412A, M84.412S, M84.412G,M84.412P, M84.412K, M84.412D, M84.421A, M84.421S, M84.421G, M84.421P, M84.421K, M84.421D, M84.411A, M84.411S M84.411G, M84.411P, M8441K, M84.411D, M84.429A, M84.429S, M84.429G, M84.429P M84.429K, M84.429D, M84.419A, M84.419G, M84.419P, M84.419K, M84.419D, S42.201D S42.201G, S42.201K, S42.201P, S42.201S, S42.202D, S42.202G, S42.202K, S42.202P, S42.202S, S42.209D, S42.209G, S42.209K, S42.209P, S42.209S, S42.231D, S42.231G, S42.231K, S42.231S, S42.232D, S42.232G, S42.232K, S42.232P, S42.232S, S42.239D, S42.239G, S42.239K, S42.239P, S42.239S, S42.241D, S42.241G, S42.241K, S42.241P, S42.241S, S42.242D, S42.242G, S42.242K, S42.242P, S42.242S, S42.249D, S42.249G, S42.249K, S42.249P, S42.249S\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. 30-day outcome measures by Risk Analysis Index (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: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (N=1,259)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot frail (RAI \u0026le; 20) (N=468)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrefrail (RAI = 21\u0026ndash;30) (N=726)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrail (RAI = 31\u0026ndash;40) (N=61)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeverely frail (RAI \u0026ge; 41) (N=4)\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: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0 (0.0%)\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: 98px;\"\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: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonroutine discharge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e135 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e12 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e89 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e20 (32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e2 (50.0%)\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: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeLOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e253 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e68 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e154 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e28 (45.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e3 (75.0%)\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: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e11 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e4 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e5 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e2 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinor complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e8 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e6 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReadmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e45 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e9 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e31 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e4 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e1 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReoperation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e21 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e14 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e2 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. 30-day outcome measures by Geriatric Nutritional Risk Index (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: 117px;\"\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=1,259)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI \u0026gt;98 (N=1,049)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI 92\u0026ndash;98 (N=128)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI 82-91 (N=72)\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=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\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: 117px;\"\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\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: 93px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonroutine discharge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e123 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e71 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e27 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e21 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e4 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\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: 117px;\"\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\u003e253 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e167 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e37 (28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e41 (56.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e8 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\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: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e11 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e2 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinor complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\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\u003e45 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e33 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e7 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e2 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e3 (30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\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: 117px;\"\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\u003e21 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e14 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e4 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e2 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Univariate logistic regression analysis of GNRI and RAI and major postoperative measures.\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: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonroutine discharge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e3.65 (2.27-5.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e5.31 (2.89-9.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e5.67 (3.23-9.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e18.85 (9.68-36.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e9.31 (2.56-33.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e40.00 (5.60-285.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeLOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.12 (1.37-3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.59 (1.15-2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e7.18 (4.38-11.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e5.04 (2.85-8.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e21.06 (4.52-98.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e18.00 (1.64-197.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.96 (0.12-7.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.80 (0.22-2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e3.57 (0.72-17.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e3.94 (0.70-22.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinor complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e3.86 (0.46-32.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e7.71 (0.67-88.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReadmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.78 (0.76-4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.28 (1.06-4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.89 (0.21-3.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e3.62 (1.15-11.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e13.20 (3.27-53.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e16.67 (1.67-166.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReoperation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e92\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.36 (0.78-7.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.81 (0.64-5.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e82-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.12 (0.48-9.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e3.08 (0.62-15.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026lt;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e8.00 (0.89-71.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ge; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNot calculable*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Not calculable due to zero events in reference or comparison group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Multivariable regression analysis for major complications.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAmerican Society of Anesthesiologists physical status class (ASA), Geriatric Nutritional Risk Index (GNRI), Risk Analysis Index (RAI), and Preoperative Acute Severe Condition (PACS).\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted odds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI Lower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI Upper\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: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePACS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eGNRI was excluded due to model convergence issues with the available sample size.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. AUC with 95% confidence interval for predictive 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 CARP (Combined GNRI-ASA-RAI-PACS). AUC; Area Under the receiver operating characteristic Curve.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\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: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI Lower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI Upper\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\u003cstrong\u003eMajor complications\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\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRef\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\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.768\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\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.611\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\u003ePACS\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: 117px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.802\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\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.715\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 complications\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\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRef\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\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.741\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\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.790\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\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.282\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\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.351\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\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRef\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\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.851\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\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.403\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\u003ePACS\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: 117px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.098\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\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.540\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\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRef\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\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.517\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\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.749\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\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\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: 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\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.126\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\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRef\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\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.851\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\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.403\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\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.098\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\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.540\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\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRef\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\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.689\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\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.806\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\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.445\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\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Internal validation of AUC analysis by bootstrap replication (100 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: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInitial AUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal Validation AUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBias-Corrected CI Lower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor complications\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: 85px;\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinor complications\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: 85px;\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReadmission\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: 85px;\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReoperation\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: 85px;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\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: 85px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e0.417\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"}],"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-6843688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6843688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eProximal humerus fractures represent a significant clinical challenge, particularly in minority patients, where accurate risk stratification remains inadequate. While individual frailty indices like the Risk Analysis Index (RAI) and modified Frailty Index-5 (mFI-5) have shown promise in predicting postoperative outcomes, no composite scoring system combining multiple validated risk assessment tools has been developed for this population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study analyzed 1,259 patients who underwent proximal humerus fracture surgery from the ACS NSQIP database (2015\u0026ndash;2021). We calculated RAI, mFI-5, Geriatric Nutritional Risk Index (GNRI), Preoperative Acute Severe Condition (PACS) scores, and ASA classification for all patients. A novel Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score was derived using multivariable regression coefficients. Primary outcomes included 30-day mortality, major complications, readmissions, reoperations, extended length of stay, and non-home discharge.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe cohort had mean age 67.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1 years with 62.3% female patients. Thirty-day mortality was 0%, major complications occurred in 0.9%, and extended length of stay affected 20.1% of patients. CARP demonstrated superior or comparable predictive performance across outcomes with AUROC values ranging from 0.546\u0026ndash;0.755. In multivariable analysis, PACS score independently predicted major complications (OR 2.96, 95% CI 1.03\u0026ndash;8.52, p\u0026thinsp;=\u0026thinsp;0.045), while GNRI independently predicted readmissions (OR 0.94, 95% CI 0.88-1.00, p\u0026thinsp;=\u0026thinsp;0.038) and reoperations (OR 0.90, 95% CI 0.82\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.036). Bootstrap validation confirmed robust internal validity with minimal optimism bias.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe novel CARP score provides enhanced risk stratification for proximal humerus fracture surgery patients, demonstrating superior predictive performance compared to individual indices and offering clinicians a comprehensive tool for preoperative decision-making and patient counseling.\u003c/p\u003e","manuscriptTitle":"Superior Predictive Performance of a Composite Frailty-Risk Index in Hispanic Patients Undergoing Proximal Humerus Fracture Surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 18:50:10","doi":"10.21203/rs.3.rs-6843688/v1","editorialEvents":[{"type":"communityComments","content":0}],"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":"2025-06-15T19:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-12 18:50:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6843688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6843688","identity":"rs-6843688","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}