The establishment and validation of a risk appraisal model for cement leakage after percutaneous vertebral augmentation for osteoporotic vertebral compression fractures based on a meta-analysis.
meta_analysis · Level I
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- Record sourced from PubMed, PMID 41442043.
- Also identified by DOI 10.1007/s00586-025-09664-5.
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Abstract
Percutaneous vertebral augmentation (PVA) represents a mainstream minimally invasive surgical procedure for the treatment of osteoporotic vertebral compression fractures (OVCF). However, postoperative complications often arise from bone cement leakage (CL). This study aimed to conduct a comprehensive meta-analysis to identify risk factors for CL and established a risk appraisal model based on the findings. We systematically reviewed seven major databases up to September 2024 to identify studies examining CL following PVA treatment in OVCF. Meta-analysis was performed using RevMan 5.4 software. The results were used to establish a risk appraisal model for CL. The meta-analysis identified several factors, including Cobb angle, Bone mineral density, Cement viscosity, Cement volume injected, Stage of bone cement injection, Cortical defect, Fracture severity, Intravertebral vacuum cleft, Basivertebral foramen sign and Number of vertebral fractures. The risk appraisal model constructed type Logit(P) = - 0.621 + 0.412X <sub>Cobb angle</sub> + 0.842X <sub>BMD</sub> + 0.842X <sub>Cement viscosity</sub> + 0.495X <sub>Cement volume injected</sub> + 0.432X <sub>Stage of bone cement injection</sub> + 0.775X <sub>Cortical defect</sub> + 0.438X <sub>Fracture severity</sub> + 0.358X <sub>IVC</sub> + 1.015X <sub>Basivertebral foramen sign</sub> + 0.742X <sub>Number of vertebral fractures</sub>. The receiver operating characteristic curve (ROC) curve showed an area under curve (AUC) of 0.831, and the Hosmer-Lemeshow test showed χ<sup>2</sup> = 5.933, which is a good predictor of CL. The risk appraisal model was validated to have a moderately effective predictive value and can serve as a valuable tool for clinicians to assess leakage risk and improve patient management strategies during surgical interventions.