A risk model for prediction of residual back pain after percutaneous kyphoplasty in patients with osteoporotic vertebral compression fracture

Background Severe residual back pain (RBP) after percutaneous kyphoplasty (PKP) significantly impacts postoperative prognosis and quality of life in patients. This study aims to identify the risk factors for RBP in patients with osteoporotic vertebral compression fractures (OVCF) following PKP, and to establish and validate a risk prediction model for RBP occurrence after PKP, so as to deepen our understanding of the risk of RBP after PKP, and improve clinical management strategies. Methods 647 patients with OVCF who had PKP surgery from 2018 to 2020 were retrospectively analyzed. 569 cases were used for training the model, and 78 for external validation. The study focused on RBP occurrence after PKP. A nomogram for risk prediction was constructed and the model was tested for accuracy and clinical applicability. Additionally, bootstrap sampling (1000 times) was used for internal validation. Results Based on the model training set, multivariate logistic regression analysis showed that relatively young age, bone mineral density, history of trauma, low back fascia edema, high platelet distribution width value, low serum chlorine value, and no recovery of middle vertebral height were independent risk factors for RBP after PKP (P ≤ 0.05). Calibration curves of the model training and validation sets were between the standard curve and the acceptable line. The Hosmer-Lemeshow goodness-of-fit test indicated that the model training and validation sets were χ ²  = 6.354 and χ ²  = 7.240, respectively (P = 0.608 and 0.511). The clinical decision-making curve showed that the threshold probability interval of the net benefit value of the model was 6.3–82.3% for the training set, 8.7–55.6%, and 72.5–81.3% for the validation set. Conclusion Each independent risk factor and the combined model had good predictive ability, while the combined model had a more vital predictive ability. The constructed nomogram model for predicting RBP risk showed good diagnostic efficacy, accuracy, and clinical applicability and provided a scientific rationale and guidance for clinical prevention and treatment. Trial registration Clinical trianumber not applicable Study design Retrospective casecontrol study.