A CT-enhanced multi-modal framework for predicting early conversion to arthroplasty in patients with intracapsular hip fractures

Résumé fourni par la source

Intracapsular hip fractures are a leading cause of disability in young and middle-aged adults and are associated with high failure and reoperation rates. Accurate preoperative prediction of conversion to arthroplasty is crucial for quantifying individual prognosis and potentially informing individualized, clinician-led treatment decisions. Yet models built solely on structured electronic health records (EHRs) are insufficient. Here, we introduce MMHIP, trained on a four-center development cohort ( n = 951) and externally tested in three independent centers ( n = 240). To our knowledge, this is the largest multi-center cohort integrating 3D CT and long-term follow-up scans for this task. Advancing beyond conventional approaches, MMHIP integrates preoperative pelvic CT with EHR data, achieving AUROC 0.920 in development and 0.873 on external validation, significantly outperforming representative image-only and EHR-only baselines ( p < 0.05). Incorporating CT markedly improved discrimination over EHR-based models, with absolute AUROC gains of 0.150–0.252 in development and 0.151–0.221 externally. Using a development-derived PPV-targeted operating threshold, MMHIP stratified patients into lower- and higher-predicted conversion-risk groups. At this threshold, MMHIP achieved a PPV of 0.901, sensitivity of 0.654, specificity of 0.980, and NPV of 0.910 in the development set; when applied unchanged to the external testing set, it maintained a PPV of 0.900, sensitivity of 0.529, specificity of 0.984, and NPV of 0.886, outperforming the single-modal comparators in threshold-based risk stratification. These findings support MMHIP as an interpretable preoperative risk-stratification model that may provide clinically relevant prognostic information for individualized, clinician-led treatment decisions.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.