{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T09:50:26Z","timestamp":1788861026877,"version":"build-2803163510"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Postoperative re-fractures in elderly osteoporotic vertebral compression fracture (OVCF) patients with comorbidities pose a major clinical challenge, with rates up to 52%. Traditional risk models overlook complex underlying diseases interactions in elderly patients. This study pioneers a machine learning (ML) framework for this high-risk group, integrating multidimensional factors to predict re-fractures and identify novel predictors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We analyzed 560 OVCF patients with comorbidities who underwent percutaneous vertebroplasty (PVP). Fourteen characteristic variables\u2014including scoliosis, chronic kidney disease (CKD), mental disorders, and cardiovascular comorbidities\u2014were selected using feature engineering. Six ML models [Random Forest (RF), XGBoost, support vector machine (SVM), etc.,] were trained and validated. Model performance was rigorously assessed via AUC-ROC, precision-recall curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) values provided interpretable risk quantification.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The RF model achieved superior predictive performance (test AUC = 0.88, sensitivity = 0.77, specificity = 0.87), outperforming conventional approaches. Notably, we identified scoliosis (SHAP = 0.14), mental disorders (0.12), and CKD (0.10) as the three top risk factors, with biomechanical and comorbidity interactions playing pivotal roles. DCA confirmed high clinical utility, with RF providing the greatest net benefit across risk thresholds.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This pioneering study establishes ML as a transformative tool for re-fracture prediction in OVCF patients with underlying diseases, uncovering previously underappreciated risk factors. Our findings highlight the critical need for integrated management of spinal deformity, mental health, and renal function in this vulnerable population. This ML framework offers a paradigm shift in personalized risk stratification and postoperative care.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1616923","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T05:27:58Z","timestamp":1751520478000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Machine learning-driven prediction of risk factors for postoperative re-fractures in elderly OVCF patients with underlying diseases: model development and validation"],"prefix":"10.3389","volume":"12","author":[{"given":"Bao","family":"Qi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingquan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyang","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"101775","DOI":"10.1016\/j.berh.2022.101775","article-title":"Risk assessment tools for osteoporosis and fractures in 2022.","volume":"36","author":"Carey","year":"2022","journal-title":"Best Pract Res Clin Rheumatol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1302\/0301-620X.93B9.26152","article-title":"Treatment of painful osteoporotic vertebral compression fractures: A brief review of the evidence for percutaneous vertebroplasty.","volume":"93","author":"Muijs","year":"2011","journal-title":"J Bone Joint Surg Br"},{"key":"B3","doi-asserted-by":"publisher","first-page":"46","DOI":"10.7812\/TPP\/12-037","article-title":"Evaluation and management of vertebral compression fractures.","volume":"16","author":"Alexandru","year":"2012","journal-title":"Perm J"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.3174\/ajnr.A2148","article-title":"Percutaneous vertebroplasty is not a risk factor for new osteoporotic compression fractures: Results from VERTOS II.","volume":"31","author":"Klazen","year":"2010","journal-title":"AJNR Am J Neuroradiol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1097\/YCO.0000000000000768","article-title":"Machine learning, artificial intelligence and the prediction of dementia.","volume":"35","author":"Merkin","year":"2022","journal-title":"Curr Opin Psychiatry"},{"key":"B6","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1136\/svn-2023-002332","article-title":"Development of machine learning-based models to predict 10-year risk of cardiovascular disease: A prospective cohort study.","volume":"8","author":"You","year":"2023","journal-title":"Stroke Vasc Neurol"},{"key":"B7","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.semcancer.2023.01.006","article-title":"Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective.","volume":"89","author":"Huang","year":"2023","journal-title":"Semin Cancer Biol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1186\/s13018-024-05062-7","article-title":"Evaluation and analysis of risk factors for adverse events of the fractured vertebra post-percutaneous kyphoplasty: A retrospective cohort study using multiple machine learning models.","volume":"19","author":"Zhao","year":"2024","journal-title":"J Orthop Surg Res"},{"key":"B9","doi-asserted-by":"publisher","first-page":"3919","DOI":"10.1007\/s00586-023-07843-w","article-title":"A nomogram prediction model for refracture in elderly patients with osteoporotic vertebral compression fractures after percutaneous vertebroplasty.","volume":"32","author":"Ju","year":"2023","journal-title":"Eur Spine J"},{"key":"B10","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1186\/s12893-024-02427-x","article-title":"Predicting osteoporotic fractures post-vertebroplasty: A machine learning approach with a web-based calculator.","volume":"24","author":"Cai","year":"2024","journal-title":"BMC Surg"},{"key":"B11","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1186\/s12874-020-00948-6","article-title":"Multiple imputation by predictive mean matching in cluster-randomized trials.","volume":"20","author":"Bailey","year":"2020","journal-title":"BMC Med Res Methodol"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1348803","DOI":"10.3389\/fpubh.2023.1348803","article-title":"A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: A cross-sectional study.","volume":"11","author":"Li","year":"2023","journal-title":"Front Public Health"},{"key":"B13","doi-asserted-by":"publisher","first-page":"104151","DOI":"10.1016\/j.compbiomed.2020.104151","article-title":"Machine learning model for predicting malaria using clinical information.","volume":"129","author":"Lee","year":"2021","journal-title":"Comput Biol Med"},{"key":"B14","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1186\/s12933-023-01985-3","article-title":"Machine learning in precision diabetes care and cardiovascular risk prediction.","volume":"22","author":"Oikonomou","year":"2023","journal-title":"Cardiovasc Diabetol"},{"key":"B15","doi-asserted-by":"publisher","first-page":"g7594","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement.","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ"},{"key":"B16","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1001\/jama.2015.37","article-title":"Decision curve analysis.","volume":"313","author":"Fitzgerald","year":"2015","journal-title":"JAMA"},{"key":"B17","doi-asserted-by":"publisher","first-page":"4512","DOI":"10.1038\/s41467-022-31384-3","article-title":"Explaining a series of models by propagating Shapley values.","volume":"13","author":"Chen","year":"2022","journal-title":"Nat Commun"},{"key":"B18","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1186\/s40001-023-01361-7","article-title":"Development and testing of a random forest-based machine learning model for predicting events among breast cancer patients with a poor response to neoadjuvant chemotherapy.","volume":"28","author":"Jin","year":"2023","journal-title":"Eur J Med Res"},{"key":"B19","doi-asserted-by":"publisher","first-page":"101811","DOI":"10.1016\/j.artmed.2020.101811","article-title":"Random Forest enhancement using improved Artificial Fish Swarm for the medial knee contact force prediction.","volume":"103","author":"Zhu","year":"2020","journal-title":"Artif Intell Med"},{"key":"B20","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/s12873-022-00582-z","article-title":"Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest).","volume":"22","author":"Zhang","year":"2022","journal-title":"BMC Emerg Med"},{"key":"B21","doi-asserted-by":"publisher","first-page":"103465","DOI":"10.1016\/j.jbi.2020.103465","article-title":"A hybrid sampling algorithm combining M-SMOTE and ENN based on Random forest for medical imbalanced data.","volume":"107","author":"Xu","year":"2020","journal-title":"J Biomed Inform"},{"key":"B22","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees.","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"key":"B23","doi-asserted-by":"publisher","first-page":"104813","DOI":"10.1016\/j.compbiomed.2021.104813","article-title":"Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP.","volume":"137","author":"Wang","year":"2021","journal-title":"Comput Biol Med"},{"key":"B24","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1186\/s12911-023-02377-z","article-title":"Machine learning-based models for the prediction of breast cancer recurrence risk.","volume":"23","author":"Zuo","year":"2023","journal-title":"BMC Med Inform Decis Mak"},{"key":"B25","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1186\/s13018-024-04779-9","article-title":"A study on vertebral refracture and scoliosis after percutaneous kyphoplasty in patients with osteoporotic vertebral compression fractures.","volume":"19","author":"Qi","year":"2024","journal-title":"J Orthop Surg Res"},{"key":"B26","doi-asserted-by":"publisher","first-page":"705","DOI":"10.12200\/j.issn.1003-0034.2021.08.004","article-title":"[Analysis of related risk factors for the refracture of adjacent vertebral body after percutaneous kyphoplasty].","volume":"34","author":"Fang","year":"2021","journal-title":"Zhongguo Gu Shang."},{"key":"B27","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1186\/s13018-021-02722-w","article-title":"Risk factors for secondary fractures to percutaneous vertebroplasty for osteoporotic vertebral compression fractures: A systematic review.","volume":"16","author":"Mao","year":"2021","journal-title":"J Orthop Surg Res"},{"key":"B28","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1186\/s12888-018-1909-2","article-title":"Association between depression and the risk for fracture: A meta-analysis and systematic review.","volume":"18","author":"Qiu","year":"2018","journal-title":"BMC Psychiatry"},{"key":"B29","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.bone.2012.05.018","article-title":"Antidepressant medications and osteoporosis.","volume":"51","author":"Rizzoli","year":"2012","journal-title":"Bone"},{"key":"B30","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1001\/jamapsychiatry.2017.0449","article-title":"Association of mental disorders and related medication use with risk for major osteoporotic fractures.","volume":"74","author":"Bolton","year":"2017","journal-title":"JAMA Psychiatry"},{"key":"B31","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.3390\/jcm11072021","article-title":"Machine learning algorithms: Prediction and feature selection for clinical refracture after surgically treated fragility fracture.","volume":"11","author":"Shimizu","year":"2022","journal-title":"J Clin Med"},{"key":"B32","doi-asserted-by":"publisher","first-page":"e207","DOI":"10.1097\/j.pbj.0000000000000207","article-title":"Risk factors for refracture after proximal femur fragility fracture.","volume":"8","author":"Louren\u00e7o","year":"2023","journal-title":"Porto Biomed J"},{"key":"B33","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.bone.2014.04.017","article-title":"Epidemiology and mortality of hip fracture among patients on dialysis: Taiwan National Cohort Study.","volume":"64","author":"Lin","year":"2014","journal-title":"Bone"},{"key":"B34","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/s00586-002-0512-9","article-title":"Load shift of the intervertebral disc after a vertebroplasty: A finite-element study.","volume":"12","author":"Baroud","year":"2003","journal-title":"Eur Spine J"},{"key":"B35","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.jvir.2007.09.008","article-title":"New vertebral osteoporotic compression fractures after percutaneous vertebroplasty: Retrospective analysis of risk factors.","volume":"19","author":"Lin","year":"2008","journal-title":"J Vasc Interv Radiol"},{"key":"B36","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1007\/s00586-021-07064-z","article-title":"Establishment and validation of a nomogram and web calculator for the risk of new vertebral compression fractures and cement leakage after percutaneous vertebroplasty in patients with osteoporotic vertebral compression fractures.","volume":"31","author":"Li","year":"2022","journal-title":"Eur Spine J"},{"key":"B37","doi-asserted-by":"publisher","first-page":"4417","DOI":"10.3892\/etm.2019.7518","article-title":"Relationship between senile osteoporosis and cardiovascular and cerebrovascular diseases.","volume":"17","author":"Hu","year":"2019","journal-title":"Exp Ther Med"},{"key":"B38","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.drugalcdep.2019.01.025","article-title":"The effect of alcohol on osteoporosis: A systematic review and meta-analysis.","volume":"197","author":"Cheraghi","year":"2019","journal-title":"Drug Alcohol Depend"},{"key":"B39","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1186\/s13018-024-04907-5","article-title":"Analysis of the impact of underlying diseases in the elderly on postoperative re-fractures after osteoporotic compression fractures.","volume":"19","author":"Qi","year":"2024","journal-title":"J Orthop Surg Res"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1616923\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T05:28:00Z","timestamp":1751520480000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1616923\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,3]]},"references-count":39,"alternative-id":["10.3389\/fmed.2025.1616923"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2025.1616923","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,3]]},"article-number":"1616923"}}