| Sumario: | • Combines 3763 MIMIC-IV cases with 558 external validations to enhance clinical generalizability. • Assesses comorbidity risk based on admission indicators.. • Eliminates AI black-box effects via quantifiable feature contribution analysis. • Deploys interactive calculator for real-time risk assessment at bedside. Among traumatic-fracture patients admitted to intensive care units (ICUs), those with substantial chronic comorbidities recover more slowly and die more often than their counterparts without such conditions. The age-adjusted Charlson Comorbidity Index (aCCI) quantifies this burden, yet clinicians still lack a tool that can identify—at the point of ICU admission—which fracture patients are likely to have a high aCCI. To fill this gap, we used a large electronic health-record repository to develop and externally validate an interpretable machine-learning model that predicts severe comorbidity burden in this population. We extracted 3 763 adult fracture cases from MIMIC-IV (2008–2019) and split them 3:1 into training and internal validation sets. High comorbidity (aCCI ≥ 7) was defined as the optimal cut-off derived from one-year survival analysis. Nine key predictors emerged from the intersection of LASSO, SVM-RFE, and random-forest importance. Eleven candidate algorithms underwent grid-search hyperparameter tuning with 10-fold cross-validation, and their performance was compared to identify the optimal model, while SHAP clarified model logic. External validation in two Chinese tertiary centres (n = 558) confirmed generalisability, and the final model was deployed as a bedside Shiny calculator. XGBoost achieved the best internal discrimination (AUROC = 0.84; AUPRC = 0.76) and exhibited excellent calibration and net benefit across clinically relevant thresholds. In external validation, AUROC values were 0.88 (Hainan) and 0.83 (Guangdong). The interactive calculator delivers patient-specific risk explanations in real time. An XGBoost-based, SHAP-interpretable model accurately predicts high aCCI in ICU fracture patients and generalises across institutions. The readily accessible web tool can help clinicians identify high-risk individuals early, personalise management, and allocate resources more efficiently.
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