ArticleBMC musculoskeletal disorders2025
Evaluation and analysis of risk factors for fractured vertebral recompression post-percutaneous kyphoplasty: a retrospective cohort study based on logistic regression analysis.
Article in BMC musculoskeletal disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 2 of them syntheses that pooled it.
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Who cites it
2 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction models for subsequent vertebral fractures after percutaneous vertebroplasty or kyphoplasty: a systematic review and critical appraisal.BMC musculoskeletal disorders · 2026Pooled it
- Artificial intelligence for prediction of clinical response and therapeutic value in interventional pain management: a scoping review.Frontiers in digital health · 2026Pooled it
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVertebral recompression after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fractures (OVCFs) may lead to recurrent pain, deformity, and neurological impairment, compromising prognosis and quality of life.
objectiveTo identify independent risk factors for postoperative recompression and develop predictive models for risk assessment.
methodsWe retrospectively analyzed 284 OVCF patients treated with PKP, grouped by recompression status. Predictors were screened using univariate and correlation analyses. Multicollinearity was assessed using variance inflation factor (VIF). A multivariable logistic regression model was constructed and validated via 10-fold cross-validation and temporal validation.
resultsFive independent predictors were identified: incomplete anterior cortex (odds ratio [OR] = 9.38), high paravertebral muscle fat infiltration (OR = 218.68), low vertebral CT value (OR = 0.87), large Cobb change (OR = 1.45), and high vertebral height recovery rate (OR = 22.64). The logistic regression model achieved strong performance: accuracy 97.67%, precision 97.06%, recall 97.06%, F1 score 97.06%, specificity 98.08%, area under the receiver operating characteristic curve (AUC) 0.998. Machine learning models (e.g., random forest) were also evaluated but did not outperform logistic regression in accuracy or interpretability.
conclusionFive imaging-based predictors of vertebral recompression were identified. The logistic regression model showed excellent predictive accuracy and generalizability, supporting its clinical utility for early risk stratification and personalized decision-making in OVCF patients undergoing PKP.
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