ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2026
Machine learning-based screening model for osteoporosis in Chinese postmenopausal women: a nationwide study with prospective fracture validation.
Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study develops a machine learning model to predict osteoporosis in Chinese postmenopausal women. The model was trained using the largest nationwide cohort and externally validated. It showed AUCs of 0.744-0.798 and demonstrated prognostic value for 5-year fracture risk, supporting population-level screening where DXA resources are limited. PURPOSE: Osteoporosis is highly prevalent among Chinese postmenopausal women, yet underdiagnosis remains due to limited DXA availability. We aimed to develop and validate a machine learning-based model for osteoporosis prediction using easily obtainable variables.
methodsThe model was trained using data from the largest nationwide cohort (China Osteoporosis Prevalence Study, n = 6,574) and externally validated in two independent cohorts: the China Vertebral and Osteoporosis Study (n = 1,758) and the Peking Vertebral Fracture Study baseline (n = 1,439). Five predictors selected via LASSO regression were used to train the prediction model. Model performance was evaluated by area under the curve (AUC) and calibration plot. To evaluate its ability to predict future fractures, the model was evaluated in a 5-year follow-up cohort (n = 795). A web-based calculator was developed for public use.
resultsThe model showed strong discrimination (AUC: 0.798 in the training cohort; 0.775 in the internal validation cohort; 0.750 and 0.744 in the external validation cohorts) and good calibration across all cohorts. A rule-out threshold (≥ 0.098) demonstrated high sensitivity (94.7%, 89.3%) and negative predictive value (91.3%, 92.7%) in two external cohorts. The model also demonstrated prognostic value for fracture risk stratification. In the 5-year follow-up, individuals classified as high risk had a significantly higher cumulative incidence of clinical fractures (HR 1.78; 95% CI, 1.06-2.99; P = 0.026), as well as higher rates of all fractures (15.9% vs. 9.5%; P = 0.015) and non-traumatic fractures (7.8% vs. 3.9%; P = 0.041).
conclusionsThis model demonstrated good performance in predicting osteoporosis among Chinese postmenopausal women and may facilitate two-step screening strategies.
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