Abstract:Objective: To explore the related risk factors influencing the recurrence after percutaneous minimally invasive surgery for multi-branch pain of the trigeminal nerve. Methods: A total of 252 patients with trigeminal multi-branch neuralgia who underwent percutaneous minimally invasive surgery in the Pain Department of the Affiliated Hospital of Jiaxing University from April 2016 to June 2022 were retrospectively included. Among them, 123 cases were in the radiofrequency thermocoagulation group and 129 cases were in the balloon compression group. After the operation, a dedicated person conducted regular follow-up for the two groups of patients, and recorded the Numerical rating scale (NRS) of pain in the two groups before the operation (T0), immediately after the operation (T1), at 3 months (T2), at 6 months (T3), at 12 months (T4), and at 15 months (T5). Recurrence-free survival rate, Barrow Neurological Institute (BNI) score. The recurrence survival curve was plotted using the Kaplan-Meier method, and the effective rate and cumulative recurrence rate were calculated. Univariate and multivariate Cox regression analyses were used to determine the risk factors related to postoperative recurrence; Draw a nomogram, construct a recurrence prediction model, and verify the guiding role of recurrence factors after minimally invasive surgery for multi-branch trigeminal nerve pain in clinical practice. Results: In the multivariate Cox regression analysis, three variables (side, surgical method, and disease duration) were independent risk factors for postoperative recurrence. Based on these risk factors, a nomogram was constructed to predict the 1-year, 2-year and 3-year survival periods of patients after TN surgery. Then, the calibration curve and the area under the receiver operating curve (AUC) are used to evaluate the accuracy and discriminative power of the prediction model. The time ROC curve can reflect the predictive effect of a certain indicator on the outcome at different time points. The results show that this risk model has good sensitivity and specificity in predicting survival risks. The calibration curve indicates that there is a good consistency between the prediction and the actual observation. The decision curve shows that this model has good clinical applicability. Conclusion: Multivariate Cox analysis showed that disease duration, surgical method and side were independent risk factors for postoperative recurrence. The results of the time ROC curve showed that this model had a good predictive effect at 1 year, 2 years and 3 years after the operation