Online dynamic nomogram for predicting pain recurrence after microvascular decompression in trigeminal neuralgia.

Abstract

Trigeminal neuralgia (TN) is one of the most common causes of facial pain. Microvascular decompression (MVD) is the first-choice surgical treatment. The present study aimed to develop a novel practical assessment system based on preoperative clinical and imaging factors for clinicians to predict the likelihood of pain recurrence following MVD in TN. A total of 56 patients with primary unilateral TN who underwent MVD were retrospectively analyzed. Patients were followed up to observe pain recurrence 1 year after MVD. An online dynamic nomogram was constructed for predicting the probability of pain recurrence after MVD in patients with TN based on multivariate logistic model. The concordance index (C-index) and receiver operating characteristic (ROC) were used to measure model discrimination. Bootstrap resampling was used for internal validation of the model and calibration curve was constructed. Decision curve analysis (DCA) was used to assess clinical applicability. Factors such as numeric rating scale (to score pain degree of patients with TN), response to neuroanalgesic drugs and neurovascular contact on magnetic resonance imaging were independent risk factors affecting the pain recurrence rate (all P<0.05). C-index was 0.973 (95%CI, 0.938-1.000) and the area under the ROC was 0.973 (95%CI, 0.938-1.000). Calibration curve with a 1,000 bootstrap resampling showed a good fit between dynamic nomogram prediction and actual observations. The DCA showed that at a threshold probability between 0 and 100%, this model can achieve a greater net benefit than if all patients had surgery or none had surgery. In conclusion, this online dynamic nomogram reliably predicted risk of pain recurrence in patients with TN following MVD.