Machine learning–based prediction of hospital-associated complications after tibial fracture surgery in older patients: a nationwide Japanese database study

Older adults requiring emergency surgery for acute tibial fractures are vulnerable to hospital-associated complications (HACs), but admission-time risk stratification tools are lacking. We aimed to characterize HACs and develop both an ensemble prediction model and a simplified bedside risk score. This retrospective cohort study used the Japanese Diagnosis Procedure Combination database provided by JMDC Inc. Patients aged ≥ 65 years emergently admitted for tibial fracture (ICD-10: S82, 2014–2025) who underwent surgery within 5 days, with stay > 5 days, were included. The primary outcome was composite HACs during index hospitalization. Missing data were handled with multiple imputation. A Super Learner ensemble was developed and evaluated on held-out test data, and a simplified scorecard was derived using analysis of variance (ANOVA)-based feature selection and Weight-of-Evidence transformation. Among 53,186 admissions, 5,193 met eligibility criteria. HACs occurred in 851 patients (16.4%), most commonly delirium (7.5%) and falls/trauma (5.7%). The Super Learner achieved a test-set area under the receiver operating characteristic curve (AUC) of 0.740 (95% CI 0.707–0.772), higher than conventional linear logistic regression (0.724). The most influential predictors were Hospital Frailty Risk Score, dementia, Barthel Index, comorbidity burden, and days to surgery. The simplified 7-variable scorecard achieved a test-set AUC of 0.736 (95% CI 0.703–0.769), stratifying patients into five risk groups (HAC rate: 3.45%–38.64%). HAC risk after emergency tibial fracture surgery in older adults was driven primarily by geriatric vulnerability rather than fracture-specific factors. A simplified admission-time score may support targeted prevention, pending external validation.