Predicting residual pain after vertebral augmentation in vertebral compression fractures: a systematic review and critical appraisal of risk prediction models - Siyi Wang; Mingpeng Shi; Xue Zhou; Jianan Yu; Mingze Han; Xianshuai Zhang; Zhenhua Li; Xinhua Chen (BMC Musculoskeletal Disorders)
Abstract Background Patients with vertebral compression fractures may experience unpredictable residual pain following vertebral augmentation. Clinical prediction models have shown potential for early prevention and intervention of such residual pain. However, studies focusing on the quality and accuracy of these prediction models are lacking. Therefore, we systematically reviewed and critically evaluated prediction models for residual pain following vertebral augmentation. Methods We systematically searched eight databases (PubMed, Embase, Web of Science, CNKI, WanFang, VIP, and SinoMed) for studies that developed and/or validated risk prediction models for residual pain after vertebral augmentation. The limitations of existing models were critically assessed using the PROBAST tool. We performed a descriptive analysis of the models' characteristics and predictors. Extracted C-statistics were combined using a weighted approach based on the Restricted Maximum Likelihood (REML) method to represent the models' average performance. All statistical analyses were performed using R 4.3.1 and STATA 17 software. Results Fifteen models were evaluated, involving 4802 patients with vertebral compression fractures post-vertebral augmentation. The overall pooled C-statistic was 0.87, with a 95% CI of 0.83 to 0.89 and a prediction interval ranging from 0.72 to 0.94. The models included 35 different predictors, with posterior fascia injury (PFI), bone mineral density (BMD), and intravertebral vacuum cleft (IVC) being the most common. Most models were rated high risk due to concerns about population selection and modeling methodology, yet their clinical applicability remains promising. Conclusion The development and validation of current models exhibit a certain risk of bias, and our study highlights these existing flaws and limitations. Although these models demonstrate satisfactory predictive performance and clinical applicability, further external validation is needed to confirm their accuracy in clinical practice. Clinicians can utilize these models alongside relevant risk factors to predict and prevent residual pain after vertebral augmentation, or to formulate personalized treatment plans.