Machine Learning to Predict Long and Short Term Fracture Risk in Postmenopausal Women

Abstract

Purpose Fractures in older adults are a significant cause of morbidity and mortality, particularly for post-menopausal women with osteoporosis. Prevention is key for managing fractures in this population and may include identifying individuals at high fracture risk and providing therapeutic treatment to mitigate risk. This study aimed to develop a machine learning fracture risk prediction tool to overcome the limitations of existing methods by incorporating additional risk factors and providing short-term risk predictions. Methods We developed a machine learning model to predict risk of major osteoporotic fractures and femur (hip) fractures in a retrospective cohort of post-menopausal women. Models were trained to generate predictions at 3, 5, and 10 year prediction windows. The model used only ICD codes, basic demographics, vital sign measurements, lab results and medication usage from a proprietary national longitudinal electronic health record repository to make predictions. Results The algorithms obtained area under the receiver operating characteristic values of 0.83, 0.81, and 0.79 for prediction of major osteoporotic fractures at 3, 5, and 10 year windows, respectively. The algorithms also obtained AUROC values of 0.79, 0.75, and 0.75 for prediction of femur fractures at 3, 5, and 10 year windows, respectively. For all models, when sensitivity was fixed at 0.80, average specificity was 0.615. Conclusion Machine learning clinical decision support may inform clinical efforts at early detection of high-risk individuals, mitigating their risk and for establishing clinical research cohorts with well-defined patient populations.