A Predictive Model for the Risk of Recurrence of Cervical Spondylotic Radiculopathy After Surgery
Tóm tắt
This study aimed to analyze the risk factors affecting the recurrence of cervical spondylotic radiculopathy after surgery, construct a nomogram predictive model, and validate the model’s predictive performance using a calibration plot. In this study, 304 cervical spondylotic radiculopathy patients who underwent computed tomography (CT)-guided radiofrequency ablation (RFA) of cervical intervertebral discs or low-temperature plasma RFA for cervical radiculopathy were enrolled at the Pain Department of Jiaxing College Affiliated Hospital from January 2019 to March 2022. The patients were randomly divided into training (n = 213) and testing (n = 91) groups in a 7:3 ratio. Lasso regression analysis was used to screen for independent predictors of recurrence 1 year after surgery. A nomogram predictive model was established based on the selected factors using multiple logistic regression analysis. One year after surgery, 250 of the 304 cervical spondylotic radiculopathy patients did not have recurrences, while 54 had recurrences. Lasso regression combined with multiple logistic regression analysis revealed that duration, numbness, and the Numeric Rating Scale (NRS) were significant predictors of recurrence 1 year after surgery (P < 0.05). A nomogram predictive model was established using these variables. The area under the curve (AUC) of the nomogram predictive model for predicting recurrence in the training group was 0.918 [95% confidence interval (CI) 0.866–0.970], and the AUC in the testing group was 0.892 (95% CI 0.806–0.978). The Hosmer–Lemeshow goodness-of-fit test exhibited a good model fit (P > 0.05). Decision curve analysis (DCA) indicated that the nomogram predictive model had a higher net benefit for predicting the risk of postoperative recurrence in cervical radiculopathy patients when the threshold probability was between 0 and 0.603. This study successfully developed and validated a high-precision nomogram prediction model (predictive variables include duration, numbness, and NRS) for predicting the risk of postoperative recurrence in cervical radiculopathy patients. The model can help improve the early identification of high-risk patients and screening for postoperative recurrence.
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