ArticleJournal of orthopaedic surgery and research2024
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.
Article in Journal of orthopaedic surgery and research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for prediction of clinical response and therapeutic value in interventional pain management: a scoping review.Frontiers in digital health · 2026Pooled it
- Development and validation of machine learning models for predicting the risk of refracture after percutaneous kyphoplasty in OVCF patients.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Risk factors analysis for refracture after vertebroplasty in osteoporotic thoracolumbar vertebral fractures.European journal of medical research · 2025Article
- Machine learning-driven prediction of risk factors for postoperative re-fractures in elderly OVCF patients with underlying diseases: model development and validation.Frontiers in medicine · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdverse events of the fractured vertebra (AEFV) post-percutaneous kyphoplasty (PKP) can lead to recurrent pain and neurological damage, which considerably affect the prognosis of patients and the quality of life. This study aimed to analyze the risk factors of AEFV and develop and select the optimal risk prediction model for AEFV to provide guidance for the prevention of this condition and enhancement of clinical outcomes.
methodsThis work included 383 patients with primary osteoporotic vertebral compression fracture (OVCF) who underwent PKP. The patients were grouped based on the occurrence of AEFV postsurgery, and data were collected. Group comparisons and correlation analysis were conducted to identify potential risk factors, which were then included in the five prediction models. The performance indicators served as basis for the selection of the best model.
resultsMultivariate logistic regression analysis revealed the following independent risk factors for AEFV: kissing spine (odds ratio (OR) = 8.47, 95% confidence interval (CI) 1.46-49.02), high paravertebral muscle fat infiltration grade (OR = 29.19, 95% CI 4.83-176.04), vertebral body computed tomography value (OR = 0.02, 95% CI 0.003-0.13, P < 0.001), and large Cobb change (OR = 5.31, 95% CI 1.77-15.77). The support vector machine (SVM) model exhibited the best performance in the prediction of the risk of AEFV.
conclusionFour independent risk factors were identified of AEFV, and five risk prediction models that can aid clinicians in the accurate identification of high-risk patients and prediction of the occurrence of AEFV were developed.
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