ORIGINAL RESEARCH article
Front. Med.
Sec. Geriatric Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1616923
Machine Learning-Driven Prediction of risk factors for Postoperative Refractures in elderly OVCF patients with underlying diseases: Model Development and Validation
Provisionally accepted
- 1Affiliated Hospital of Jining Medical University, Jining, Shandong Province, China
- 2China Medical University, Shenyang, China
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Background: 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, mental health, and renal function in this vulnerable population. This ML framework offers a paradigm shift in personalized risk stratification and postoperative care.
Keywords: OVCF, Refracture, machine learning, risk factor, Underlying diseases
Received: 23 Apr 2025; Accepted: 09 Jun 2025.
Copyright: © 2025 Qi, Kong, Wu, Zhang, Wei, Meng, Wang and Li. 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:
Chunyang Meng, Affiliated Hospital of Jining Medical University, Jining, 272000, Shandong Province, China
Hong Wang, Affiliated Hospital of Jining Medical University, Jining, 272000, Shandong Province, China
Qingwei Li, China Medical University, Shenyang, China
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