[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119629-en":3,"doc-seo-119629-105":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},119629,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Assessment of the risk of osteoporotic bone fracture in postmenopausal women using machine learning methods","Osteoporosis management focuses on preventing osteoporotic fractures by identifying postmenopausal women at higher future risk. A multicenter study applied machine learning to two independent cohorts (HURH and Camargo) with 8–10 years of clinical follow-up, building prediction models in HURH and validating them externally in Camargo. The models predicted fracture risk separately for women with osteoporosis and for general postmenopausal women, using variable groupings that emphasized clinically accessible measures. Key predictors included prior fracture, DXA data, vitamin D, and PTH levels, achieving AUC values of 0.92 and 0.88, respectively.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nAssessment of the risk of osteoporotic bone fracture in postmenopausal women using machine learning methods  \nRicardo Usategui-Martín1,10􀀍, Jorge Mateo2,3,10, Francisco Campillo-Sánchez4, Ana M. Torres2,3, Ángela Ruiz deTemiño5, Judith Gil6, Marta Martín-Millán7,8, José Luís Hernandez7,8 & José Luís Pérez-Castrillón5,9􀀍  \nThe main objective of osteoporosis management is to prevent osteoporotic fractures. Using machine learning methods, new risk variables can be identified to enhance the ability to identify women with osteoporosis who are at an increased risk of bone fracture. A multicenter study using machine learningbased methods was conducted in two independent cohorts of postmenopausal women (HURH and Camargo Cohorts), with clinical follow-up periods ranging from 8 to 10 years. The prediction models were developed in the HURH Cohort and validated using the Camargo Cohort, an independent external group of postmenopausal women. This study developed machine learning models to predict the risk of osteoporotic bone fractures. One is for postmenopausal women with osteoporosis, and the other is for general postmenopausal women. For each of these, two variable grouping options were used. The aggregation with the most predictive power included variables that are generally most accessible in medical practice. For postmenopausal women with osteoporosis, theAUC was 0.92, and for general postmenopausal women, it was 0.88. The results highlighted the significance of the previous fracture, DXA data, vitamin D levels, and PTH levels in predicting future fractures. Machine learning should be used to identify postmenopausal women at increased risk of fractures. This study summarizes that previous fractures, DXA, PTH, and vitamin D play crucial roles in identifying these women.  \nKeywords Osteoporosis, Bone fracture, Machine learning, Parathormone, PTH, Postmenopausal  \nOsteoporosis is the most common bone disorder worldwide, characterized by low bone mineral density (BMD), reduced bone mass, alterations in bone microarchitecture, and an increased risk of osteoporotic fractures. Osteoporosis is a silent, progressive disease with dramatic clinical and economic consequences. It has been reported that one in three postmenopausal women has osteoporosis, and the majority will have a bone fracture at some point in life. Osteoporotic bone fractures are associated with increased morbidity, disability, and mortality, and a worse quality of life1–4.  \nPreventing the appearance of osteoporotic bone fractures is the principal therapeutic objective in the management of osteoporosis5. Therefore, it is crucial to identify patients at higher risk of suffering fractures to prevent their occurrence. In this sense, various algorithms have been developed to identify patients with a higher risk of suffering the disease and/or suffering bone fractures6. The most widely used is FRAX, which provides risk stratification by combining various risk factors. The procedure used to perform it was the statistical analysis  \n1Department of Cell Biology, Faculty of Medicine, Unit of excellence-IOBA, University of Valladolid, Valladolid 47005, Spain. 2Medical Analysis Expert Group, Castilla-La Mancha Institute of Health Research (IDISCAM), Toledo 45071, Spain. 3Medical Analysis Expert Group, Institute of Technology, University of Castilla- La Mancha, Cuenca 16071, Spain. 4Department of Gynecology, Ramón y Cajal University Hospital, Madrid 28034, Spain.  \n5Department of Internal Medicine, Río Hortega University Hospital, Valladolid 47012, Spain. 6Department of Internal Medicine, Río Carrión General Hospital, 34005 Palencia, Spain. 7Department of Medicine and Psychiatry, University of Cantabria, Santander 39005, Spain. 8Department of Internal Medicine, Hospital Marqués de Valdecilla-IDIVAL, Santander 39008, Spain. 9Department of Medicine, Faculty of Medicine, University of Valladolid, Valladolid 4700","cbCaio1TUnBBwsst","https://ap.wps.com/l/cbCaio1TUnBBwsst","pdf",3345560,1,10,"English","en",105,"# Background\n## Burden of osteoporosis and fracture risk\n## Need for risk identification\n# Methods\n## Machine learning premise and classification approaches\n## Multicenter study design and cohorts\n## Model development and external validation\n# Results\n## Predictive performance\n## Most informative clinical variables\n# Implications\n## Clinical use for fracture risk stratification","[{\"question\":\"What was the main objective of this study?\",\"answer\":\"To develop machine learning models that identify postmenopausal women at increased risk of osteoporotic bone fractures using new risk variables.\"},{\"question\":\"How were the machine learning models developed and validated?\",\"answer\":\"Models were developed in the HURH cohort and validated externally using the Camargo cohort, with 8–10 years of clinical follow-up.\"},{\"question\":\"Which variables were most significant for predicting future fractures?\",\"answer\":\"Previous fracture, DXA data, vitamin D levels, and PTH levels were highlighted as key predictors.\"}]","Assessment of the risk of osteoporotic bone fracture in postmenopausal women using machine learning methods | 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was the main objective of this study?","Question",{"text":75,"@type":76},"To develop machine learning models that identify postmenopausal women at increased risk of osteoporotic bone fractures using new risk variables.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine learning models developed and validated?",{"text":80,"@type":76},"Models were developed in the HURH cohort and validated externally using the Camargo cohort, with 8–10 years of clinical follow-up.",{"name":82,"@type":73,"acceptedAnswer":83},"Which variables were most significant for predicting future fractures?",{"text":84,"@type":76},"Previous fracture, DXA data, vitamin D levels, and PTH levels were highlighted as key 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