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| PAN Bin,LI Xin,LI Gen.Analysis of risk factors associated with subsequent vertebral compression fracture after percutaneous kyphoplasty and the development and validation of a predictive model[J].Chinese Journal of Spine and Spinal Cord,2023,(1):19-26. |
| Analysis of risk factors associated with subsequent vertebral compression fracture after percutaneous kyphoplasty and the development and validation of a predictive model |
| Received:May 12, 2022 Revised:November 06, 2022 |
| English Keywords:Percutaneous kyphoplasty Subsequent vertebral compression fracture Risk factors Predictive model |
| Fund:徐州医科大学附属医院优秀中青年及青苗人才（2021107406）；江苏省中医药科技发展计划项目（MS2021102）；徐州市重点研发计划（社会发展）（KC21177） |
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| English Abstract: |
| 【Abstract】 Objectives: To analyse the risk factors affecting subsequent vertebral compression fractures(SVCF) after percutaneous kyphoplasty(PKP), and to develop and validate a predictive model. Methods: The clinical data of 415 patients treated with PKP in the Department of Orthopedics, the Affiliated Hospital of Xuzhou Medical University were retrospectively analyzed. 24 patients(5.8%) had SVCF after surgery, and the follow-up period was 16-30 months. According to whether the patients had SVCF after operation, the patients were divided into SVCF group and non-SVCF group. Patients′ gender, age, body mass index(BMI), bone mineral density(BMD), history of clinical comorbidities(history of hypertension, history of diabetes), bone cement injection dosage, bone cement leakage, number of initial surgical segments, Cobb angle and anterior vertebral height(AVH) before and after surgery were analyzed by univariate analysis. Based on LASSO regression, risk factors significantly associated with SVCF after PKP were selected. Then, the selected risk factors were included in multivariate Logistic regression analysis. Finally, a prediction model was developed according to the results of multivariate Logistic regression analysis, and the model was visualized by drawing a nomogram to predict the risk probability of SVCF after PKP. The enhanced bootstrap method was used for internal validation of the model. Calibration curve analysis and receiver operating characteristic(ROC) curve were drawn to evaluate the model performance. Decision curve analysis(DCA) and clinical impact curve(CIC) were used to evaluate the clinical utility of the model. Results: There were significant differences in age, BMI, BMD, bone cement injection dosage and bone cement leakage between the SVCF group and the non-SVCF group(P<0.05). LASSO regression analysis showed that age, BMI, BMD, bone cement injection dosage and bone cement leakage were closely related to SVCF after PKP. Multivariate Logistic regression analysis showed that age, BMI, BMD, bone cement leakage and bone cement injection dosage were independent risk factors for SVCF after PKP. The predictive factors in the visual prediction model(nomogram) included age, BMI, BMD, bone cement injection volume and bone cement leakage. The deviation-corrected curve of the model fitted well with the apparent curve after enhanced bootstrap method testing, and the area under the ROC curve was 0.945 with a 95% confidence interval of 0.897-0.993. As shown by the DCA curve, the maximum benefit was obtained in the threshold range of 0.1-0.9. CIC showed that the predictive model can effectively identify high-risk patients with SVCF within the threshold probability. Conclusions: Age, BMD, BMI, bone cement injection dosage and bone cement leakage are independent risk factors for SVCF, and the prediction model developed based on these five risk factors can accurately predict the risk probability of SVCF after PKP and identify the high-risk group in the early postoperative period to focus. |
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