Development and validation of the APEX-HBD SCORe: a multivariable prediction model for postoperative complications in patients undergoing shoulder arthroplasty.

BACKGROUND: The incidence of total shoulder arthroplasty (TSA) has risen significantly, driven by expanded indications. This study aims to derive and validate a model for classifying patients based on the risk of short-term complications using logistic regression (LR) and other machine learning (ML) techniques. METHODS: We analyzed de-identified data from the American College of Surgeons' NSQIP database (2005-2022), identifying 39,028 patients who underwent SA using CPT codes. The dataset was split into derivation (60%) and validation (40%) cohorts. We constructed baseline classifiers for complications using backward stepwise multivariate LR and developed the APEX-HBD SCORe, a point-based system to stratify patients into low (<5%), moderate (5-11%), and high-risk (≥19%) categories. To improve accuracy, we also developed ML models, including Gradient Boosting, AdaBoost, Random Forest, and Extra Randomized Trees, using the same predictors identified in the LR model. RESULTS: The derivation cohort (23,417 patients) reported 1,476 (6.3%) patients with complications. LR identified 11 predictors, including albumin levels, hematocrit, ASA classification, preoperative transfusion, and other relevant factors. LR achieved area under the curves of 72%, 75%, 77%, and 63% for any, medical, serious medical, and surgical complications, respectively, in the derivation cohort, and 70%, 75%, 73%, and 58% in the validation cohort-outperforming the 5-item modified frailty index. Gradient Boosting performed best among ML models, with area under the curves of 73%, 82%, 76%, and 67%. APEX-HBD SCORe risk stratification revealed a progressive increase in complication rates across categories, confirmed in the validation cohort. CONCLUSION: This 16-year analysis introduces the APEX-HBD SCORe, a validated ML-augmented tool predicting 30-day complications using 11 patient factors. It aids in patient stratification, counseling, preoperative planning, and tailored postoperative management.