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/m2; mean age at menopause 47.3 ± 6.0 years). Mean 12-month BMD increases were + 15,35 % at the lumbar spine, +12,42 % at the femoral neck, and + 8,62 % at the total hip. The proportion achieving a ≥ 10 % gain was 39 %, 38.1 %, and 31.7 %, respectively. SHAP analysis identified consistent predictors of response: lower baseline BMD, higher phosphate levels, and younger age at menopause were associated with greater gains, whereas elevated PTH and alkaline phosphatase predicted a reduced response. Patients who had not received corticosteroids or NSAIDs in the six months prior to treatment initiation, typically for pain or inflammation, also showed greater increases in BMD. 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.