HyTrax: Deep Sequential Modeling of Serial Musculoskeletal Measurements for Fracture Prediction in the Women's Health Initiative with External Evaluation in the Framingham Heart Study

The clinical utility of monitoring longitudinal changes in musculoskeletal trajectories, including bone mineral density (BMD), muscle strength, height, and weight for fracture prediction, remains underutilized, as current gold-standard tools such as the Fracture Risk Assessment Tool (FRAX) rely solely on cross-sectional baseline data. This study aimed to determine whether a deep learning model integrating individualized musculoskeletal trajectories improves fracture prediction accuracy compared to established static benchmarks. We developed the Hybrid Trajectory-Based model (HyTrax), a Transformer-based deep learning model that encodes sequential measurements of hip and spine BMD, grip strength, height, and weight as temporal tokens, incorporating subject-specific slopes derived from linear mixed-effects models. The model was trained and internally validated in 27,512 postmenopausal women from the Women's Health Initiative (WHI) and externally evaluated in 1,193 participants from the Framingham Heart Study (FHS). In the WHI validation set, the HyTrax + FRAX (BMD) ensemble model achieved a time-dependent Area Under the Curve (AUC) of 0.85 for Major Osteoporotic Fracture, outperforming both the longitudinal Transformer alone (AUC = 0.80) and the standard FRAX-BMD model (AUC = 0.82). The HyTrax + FRAX (BMD) ensemble model demonstrated favorable discrimination and improved risk stratification (Net Reclassification Improvement +26.5%) in WHI. Evaluation in the FHS cohort demonstrated the transportability of the longitudinal embeddings, with the HyTrax + Baseline 2 ensemble model (integrating longitudinal embeddings with clinical risk factors, BMD, and grip strength) achieving an AUC of 0.74. Explainability analyses identified early longitudinal weight fluctuations and overall height loss trajectories as important predictors of future fracture risk, alongside static factors such as age and genetic predisposition. By leveraging individualized trajectories through deep sequential modeling with baseline FRAX probability, the HyTrax + FRAX (BMD) ensemble model improved fracture discrimination over static assessments, offering a framework for incorporating repeated clinical measures into fracture prediction.