[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120511-en":3,"doc-seo-120511-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120511,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Risk prediction of osteoporotic vertebral compression fractures in postmenopausal osteoporotic women by machine learning modelling","Postmenopausal osteoporosis increases the risk of osteoporotic vertebral compression fractures (OVCF) through declining estrogen, but robust, population-specific risk prediction tools remain limited. A retrospective case-control study of 486 postmenopausal women (2015–2018) identified independent OVCF risks using logistic regression and built a machine-learning prediction model that integrates clinical, biological, and musculoskeletal data, then assessed performance and deployment readiness for personalized risk management.","OPEN ACCESS  \nEDITED BY  \nQ. Wang,  \nGuangzhou University of Chinese Medicine, China  \nREVIEWED BY  \nTawika Kaewchur,  \nChiang Mai University, Thailand Cesar Libanati,  \nIndependent Researcher, Fort Lauderdale, FL, United States  \n*CORRESPONDENCE  \nQi Yan  \n [yanqispine@126.com](yanqispine@126.com)[ ](yanqispine@126.com)Huilin Yang  \n [hlyang@suda.edu.cn](hlyang@suda.edu.cn)[ ](hlyang@suda.edu.cn)Yusen Qiao  \n [qiaoyusen8612@suda.edu.cn](qiaoyusen8612@suda.edu.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 14 July 2025  \nACCEPTED 25 August 2025  \nPUBLISHED 12 September 2025  \nCITATION  \nSun X, Jing P, Yang Y, Sun H, Tang W, Mi J, Zong P, Yan Q, Yang H and Qiao Y (2025) Risk prediction of osteoporotic vertebral compression fractures in postmenopausal osteoporotic women by machine learning modelling.  \nFront. Med. 12:1664219.  \ndoi: 10.3389/fmed.2025.1664219  \nCOPYRIGHT  \n© 2025 Sun, Jing, Yang, Sun, Tang, Mi, Zong, Yan, Yang and Qiao. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) 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.  \nTYPE Original Research PUBLISHED 12 September 2025 DOI 10.3389/fmed.2025.1664219  \nRisk prediction of osteoporotic vertebral compression fracturesin postmenopausal osteoporotic women by machine learning modelling  \nXiao Sun 1†, Pengrui Jing 1†, Yuqing Yang 2†, Haifu Sun 1, Wenxiang Tang3, Jian Mi 1, Pengju Zong 1, Qi Yan 1*, Huilin Yang 1* and Yusen Qiao 1*  \n1 Department of Orthopaedics, The First Affiliated Hospital of Soochow University, Suzhou, China,  \n2 Department of Otorhinolaryngology, The First Affiliated Hospital of Soochow University, Suzhou, China, 3 Department of Orthopaedics, The Second Affiliated Hospital of Soochow University, Suzhou, China  \nBackground: 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.  \nObjective: 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.  \nMethods: 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.  \nResults: 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 personalized risk management in routine care.  \nConclusion: The development of OVCF in postmenopausal osteoporotic women is influenced by a combination of bone","cbCainfGLdZCurTG","https://ap.wps.com/l/cbCainfGLdZCurTG","pdf",1336816,1,9,"English","en",105,"# Background\n# Objective\n# Methods\n## Study design and participants\n## Statistical and machine learning modelling\n# Results\n## Independent risk factors\n## Model performance and calibration\n# Conclusion\n# Keywords and Highlights","[{\"question\":\"What was the main goal of this study?\",\"answer\":\"To identify key risk factors for OVCF in postmenopausal osteoporotic women and develop a machine learning model to predict OVCF risk using integrated clinical, biological, and musculoskeletal data.\"},{\"question\":\"How were patients grouped and how long were they followed?\",\"answer\":\"The study included 486 postmenopausal women and divided them into a non-fracture group and a vertebral fracture group based on whether OVCF developed during subsequent 5 years of treatment and follow-up.\"},{\"question\":\"Which factors were identified as independent risk factors for OVCF?\",\"answer\":\"Low bone mineral density (BMD), chronic inflammation (elevated NLR), and sarcopenia were identified as independent risk factors, while regular anti-osteoporotic treatment was associated with reduced fracture incidence.\"}]","Risk prediction of osteoporotic vertebral compression fractures in postmenopausal osteoporotic women by machine learning modelling | 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