Machine learning-based risk predictive model for postoperative refractures in patients with osteoporotic vertebral compression fractures: a systematic review and critical appraisal
Abstract
OBJECTIVE: To systematically evaluate the application of machine learning (ML)-based predictive models in assessing the risk of refractures following vertebral augmentation (VA) in patients with osteoporotic vertebral compression fractures (OVCFs). The methodology and performance of these models will be assessed, and key predictors will be identified. This will provide a reference for constructing high-quality predictive models in the future. METHODS: A systematic search was conducted to identify relevant studies using eight electronic databases for the time period from database inception to February 28, 2026. Two reviewers independently conducted literature searches, screening, and data extraction. Eligible studies include all retrospective or prospective studies of ML models that have been developed or validated in patients with OVCFs and that predict refracture after VA. Model bias and applicability were evaluated using the Predictive Model Risk of Bias Assessment Tool (PROBAST). The quality of transparent reporting was assessed using the Multivariate Predictive Models for Individual Prognosis or Diagnosis-Artificial Intelligence (TRIPOD + AI). RESULTS: A total of 14 studies were included, involving 73 postoperative refractures prediction models for patients with OVCFs, constructed using 25 ML algorithms. The incidence of refractures ranged from 10.6% to 64.4%. The high-frequency predictors network diagram showed that 13 appeared at least twice and more, with age, body…
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