Predictors of clinically meaningful bone mineral density gains with romosozumab: An explainable machine leaning analysis of a real-world cohort.

Objective: To evaluate the real-world effectiveness of Romosozumab in postmenopausal women with severe osteoporosis and to identify baseline clinical and biochemical predictors of clinically meaningful bone mineral density (BMD) gains (≥10 %, used for exploratory classification) using an explainable machine-learning approach. Methods: We conducted a retrospective, observational multicentre study across seven hospitals in Castilla-La Mancha, Spain. Postmenopausal women aged ≥50 years who initiated romosozumab between May 2023 and November 2024 for severe osteoporosis or high fracture risk were included. Lumbar-spine, femoral-neck, and total-hip BMD were assessed by dual-energy X-ray absorptiometry (DXA) at baseline and 12 months. Baseline biochemical variables included serum P1NP, CTX, PTH, vitamin D, calcium, phosphate, alkaline phosphatase, and creatinine. Predictors of a ≥ 10 % BMD gain were examined using elastic-net logistic regression combined with SHapley Additive exPlanations (SHAP) for model interpretability. Results: Fifty-eight women were analysed (mean ± SD age 71.7 ± 10.0 years; BMI 26.1 ± 4.8 kg/m Conclusions: Romosozumab was effective and well-tolerated in routine clinical practice, yielding meaningful and site-specific gains in BMD. Explainable machine-learning analysis identified physiologically coherent and consistent clinical predictors of ≥10 % response.