A Predictive Model for the Risk of Postsurgery Pain Recurrence in the V1 Branch of the Trigeminal Nerve
Authors: Xie Keyue 
Published: October 2024

BACKGROUND: The factors influencing pain recurrence following V1 trigeminal nerve surgery are still unknown. OBJECTIVE: We aimed to analyze the risk factors affecting pain recurrence following surgery in the V1 branch of the trigeminal nerve, construct a nomogram-based therapeutic efficacy prediction model using logistic regression analysis, and validate the model’s predictive performance. STUDY DESIGN: A retrospective study. SETTING: This study was performed at the Affiliated Hospital of Jiaxing University, People’s Republic of China. METHODS: Data were retrospectively collected from 131 patients with trigeminal neuralgia and V1 branch algesia who underwent either radiofrequency thermocoagulation through the supraorbital foramen or percutaneous balloon compression at the Pain Department of the Affiliated Hospital of Jiaxing University from March 2017 through January 2021. The patients were randomly divided into a training group (n = 92) and a testing group (n = 39) in a 7:3 ratio. A least absolute shrinkage and selection operator (LASSO) regression was used to screen independent predictive factors. The outcome variable was whether the patient experienced pain recurrence within 2 years postsurgery. Those results were used to construct a nomogram-based predictive model, followed by a multivariate logistic regression analysis. The feasibility of the nomogram-based predictive model was evaluated by the validation group. Finally, the predictive model’s discrimination ability, accuracy, and clinical usability were evaluated using a receiver operating characteristic curve, calibration curves, and decision curve analysis, respectively. RESULTS: The results indicate that among the total 131 patients, 76 patients did not experience pain recurrence within 2 years postsurgery, while 55 patients suffered a pain recurrence. The results of the LASSO regression, combined with a multivariate logistic regression analysis, showed that age, pre-Numeric Rating Scale score, and surgery type were the influencing factors for patients with V1 branch pain who experienced pain recurrence within 2 years postsurgery (P < 0.05). From this data a nomogram-based predictive model was established. The area under the curve of the nomogram-based predictive model for the training group was found to be 0.890 (95% CI, 0.818 - 0.961); in the test group it was 0.857 (95% CI, 0.748 - 0.965) in the test group. The Hosmer-Lemeshow goodness-of-fit test revealed an excellent fit (P > 0.05), while the decision curve analysis showed that the net benefit of using the nomogram-based predictive model to predict the risk of recurrence after 2 years was higher when the patient’s threshold probability was 0 to 0.990. LIMITATIONS: This was a single-center study. CONCLUSION: A high-precision nomogram-based predictive model was successfully established and validated (with predictive variables including age, pre-Numeric Rating Scale score, and surgery type). We envisage this model will help improve the early identification and screening of high-risk patients for postsurgery pain recurrence of the V1 trigeminal nerve branch. KEY WORDS: Trigeminal neuralgia, risk factors, prediction model