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

Area of Science:

• Neurosurgery
• Pain Medicine
• Medical Informatics

Background:

• Trigeminal neuralgia (TN) is a common cause of facial pain, with microvascular decompression (MVD) as the primary surgical intervention.
• Predicting pain recurrence post-MVD is crucial for managing patient expectations and optimizing treatment strategies.

Purpose of the Study:

• To develop and validate a practical, preoperative assessment system for predicting pain recurrence after MVD in TN patients.
• To create an online dynamic nomogram integrating clinical and imaging factors for personalized risk assessment.

Main Methods:

• Retrospective analysis of 56 patients with primary unilateral TN undergoing MVD.
• Development of a multivariate logistic model incorporating preoperative factors.
• Validation using concordance index (C-index), ROC curves, bootstrap resampling, calibration curves, and decision curve analysis (DCA).

Main Results:

• Numeric rating scale, response to analgesics, and neurovascular contact on MRI were independent predictors of pain recurrence.
• The dynamic nomogram demonstrated high predictive accuracy (C-index=0.973, AUC=0.973) and good calibration.
• DCA confirmed the clinical utility of the nomogram in guiding surgical decisions.

Conclusions:

• The developed online dynamic nomogram is a reliable tool for predicting the risk of pain recurrence in TN patients following MVD.
• This system offers a valuable resource for clinicians to personalize treatment and improve patient outcomes.
• The integration of clinical and imaging data provides a robust approach to managing facial pain recurrence.