Construction and Validation of a Risk Prediction Model for Recurrence of Trigeminal Neuralgia After Percutaneous Balloon Compression Based on Machine Learning
Objective To analyze the risk factors for recurrence of trigeminal neuralgia(TN)after percutaneous balloon compression(PBC),construct a recurrence risk prediction mod-el,and verify its prediction effect.Methods Retrospective collection of data from January 2020 to December 2023 on 317 TN patients treated with PBC at the Fourth Affiliated Hospital of Harbin Medical University was conducted to form the modeling group.Univariate analysis and logistic re-gression analysis were employed to screen for risk factors for TN recurrence after PBC.Three ma-chine learning algorithms(Logistic regression,random forest,and XGBoost)were utilized in R software to construct predictive models,and their performance was compared.The optimal algo-rithm for predicting TN recurrence after PBC was determined,followed by external validation of the model using data from 131 patients treated at the First Affiliated Hospital of Harbin Medical University from January 2020 to December 2023 as the validation group.Results The logistic re-gression analysis indicated that a disease duration＞5 years,non-typical pain,non-pear-shaped balloon,compression time＞120 seconds,and delayed disappearance of pain are independ-ent risk factors for the TN recurrence following PBC(P＜0.05).On the other hand,facial numb-ness serves as a protective factor against recurrence in patients undergoing this procedure(OR=0.289,95％CI:0.143～0.582).Furthermore,the random forest model exhibited superior perform-ance compared to the other two predictive models,with areas under the ROC curve of 0.824(95％CI:0.774～0.873)for the modeling group and 0.835(95％CI:0.763～0.892)for the validation group,indicating its greater efficacy in predicting postoperative recurrence.Conclusion The mod-el based on the random forest algorithm is the optimal predictive model for TN recurrence after PBC.It is beneficial for clinical screening of high-risk groups for recurrence of trigeminal neural-gia post-surgery.This can provide a reference for medical staff to take targeted preventive meas-ures early.
Trigeminal NeuralgiaPercutaneous Balloon CompressionRecurrenceMa-chine LearningPredictive Model
NETL
NSTL
万方数据
