ORIGINAL RESEARCH article
Front. Med.
Sec. Geriatric Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1664219
Risk prediction of osteoporotic vertebral compression fractures in postmenopausal osteoporotic women by machine learning modeling
Provisionally accepted
- The First Affiliated Hospital of Soochow University, Suzhou, China
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Background. Osteoporosis in postmenopausal women is characterized by significant bone mass loss due to reduced oestrogen, leading to an increased risk of osteoporotic vertebral compression fractures (OVCF). Comprehensive risk prediction models for diagnosing and predicting fracture risk in this population are still lacking. Objective. This study aims to identify key risk factors for OVCF in postmenopausal osteoporotic women and develop a machine learning model to predict OVCF risk by integrating clinical, biological, and musculoskeletal data. Methods. This retrospective case-control study included 486 postmenopausal women diagnosed with osteoporosis between 2015 and 2018. The patients were divided into a non-fracture group (Group A) and a vertebral fracture group (Group B) based on whether they developed OVCF during the subsequent 5 years of treatment and follow-up. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for OVCF. Furthermore, a comprehensive risk prediction model was constructed using multiple machine learning algorithms. Results. Among the 486 postmenopausal women, 269 (55.35%) experienced OVCF. Low bone mineral density (BMD), chronic inflammation, and sarcopenia were identified as independent risk factors, while regular anti-osteoporotic treatment was associated with a reduced fracture incidence. The Balanced Bagging machine learning model demonstrated an accuracy of 98.98%, a sensitivity of 98.24%, a specificity of 100.00%, and the model’s F1 score was 0.99.The deployed model outputs calibrated, patient-specific probabilities with case-level explanations and supports dynamic re-scoring as new BMD/CTx/NLR results become available, enabling personalised risk management in routine care. Conclusion. The development of OVCF in postmenopausal osteoporotic women is influenced by a combination of bone metabolism, inflammatory processes, and muscle health. The machine learning model developed in this study provides a reliable and accurate tool for personalized OVCF risk prediction, allowing clinicians to optimize prevention and treatment strategies. Future large-scale prospective studies are required to validate these findings and enhance the model’s predictive capabilities.
Keywords: Osteoporotic vertebral compression fracture, Percutaneous kyphoplasty, postoperative refracture, Osteoporosis, postmenopausal women, machine learning.
Received: 14 Jul 2025; Accepted: 25 Aug 2025.
Copyright: © 2025 Sun, Jing, Yang, Sun, Tang, Mi, Zong, Yan, Yang and Qiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yusen Qiao, The First Affiliated Hospital of Soochow University, Suzhou, China
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