Development and validation of the CARP score: A novel composite frailty model for total shoulder arthroplasty

CARP评分的开发与验证:一种用于全肩关节置换术的新型综合脆弱性模型

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Abstract

OBJECTIVE: As total shoulder arthroplasty (TSA) becomes more common, current risk prediction tools often fail to reflect the physiological complexity of elderly surgical patients. Although tools such as the modified Frailty Index (mFI-5), Risk Analysis Index (RAI), and Geriatric Nutritional Risk Index (GNRI) show moderate prognostic utility, no validated composite score integrates multiple frailty domains to optimize perioperative risk stratification. This study developed and validated a novel frailty-based score-the Combined ASA-RAI-Preoperative Acute Severe Condition (CARP) score-and evaluated its predictive accuracy against existing indices in patients undergoing TSA for primary osteoarthritis. METHODS: Using CPT code 23472, we identified TSA cases from 2015-2021 in the ACS-NSQIP database. Inclusion required complete frailty, comorbidity, and 30-day outcome data. The CARP score incorporated ASA class, RAI, and acute physiologic stress variables. Outcomes included 30-day major complications, readmission, reoperation, extended length of stay (eLOS), and non-home discharge. We assessed predictive performance using multivariable logistic regression, AUC, and internal bootstrap validation. RESULTS: Among 14,150 patients (mean age 68.9), major complications occurred in 0.78%, readmissions in 2.2%, and non-home discharges in 6.2%. CARP score demonstrated superior predictive accuracy for major complications (AUC 0.720), readmission (0.666), and eLOS (0.675), outperforming ASA, RAI, GNRI, and mFI-5. All CARP components were independently associated with adverse outcomes. Bootstrap validation confirmed model robustness. CONCLUSIONS: CARP scores outperform traditional indices in predicting adverse 30-day outcomes after TSA. Its multidomain design enables better risk stratification, with potential to enhance surgical planning, resource use, and patient counseling. External validation is recommended.

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