Factors influencing acute pain after percutaneous vertebroplasty in patients with thoracolumbar fractures and its predictive model creation and validation

This study aims to analyze the risk factors for acute pain after percutaneous vertebroplasty in patients with thoracolumbar spine fracture and create a predictive model for validation. Clinical data of thoracolumbar spine fracture patients admitted to our hospital from January 2023 to December 2024 were retrospectively collected, and the visual analog score was used to assess the pain within 48 hours after the operation, and a visual analog score of >3 was defined as acute pain. Independent risk factors were screened by univariate and multivariate logistic regression analyses, and the model was visualized using a nomogram. The performance of the model was assessed by calculating the area under the curve from the receiver operating characteristic curve, and the model fit was verified using the Hosmer-Lemeshow goodness-of-fit test. To improve the reliability of the validation results, Bootstrap combined with 10-fold cross-validation was used for internal validation, and calibration curve and decision curve analyses were applied to assess the clinical utility of the model. Two hundred ninety-four patients were included, of which 186 (63.27%) experienced acute pain after surgery. Univariate and multifactorial logistic regression analyses showed that 5 independent risk factors were associated with acute postoperative pain: body mass index > 24 kg/m2 (odds ratio [OR], 1.834; 95% confidence interval [CI], 1.230-4.324), number of fractured vertebra > 1 (OR, 3.902; 95% CI. 1.873-9.423), unsatisfactory cement distribution (OR, 3.004; 95% CI, 1.483-6.837), vertebral compression height > 4 mm (OR, 3.319; 95% CI, 1.376-5.766), and fracture site in lumbar spine (OR, 1.457; 95% CI, 1.137-2.769). The occurrence of acute pain after percutaneous transluminal vertebroplasty in patients with thoracolumbar spine fracture is associated with a variety of factors, and the prediction model constructed in this study has good prediction accuracy, which can help to identify high-risk patients at an early stage and intervene.