Hemodynamic prediction of postoperative recurrence in trigeminal neuralgia associated with vertebrobasilar dolichoectasia: development of a nomogram model

Abstract

Trigeminal neuralgia caused by vertebrobasilar dolichoectasia (VBD-TN) demonstrates a higher postoperative recurrence rate following microvascular decompression (MVD) than typical vascular compression. This study aimed to develop a clinical prediction model incorporating hemodynamic parameters to support individualized decision-making in clinical practice. Clinical data from patients with VBD-TN prior to MVD were collected. Hemodynamic models were constructed based on imaging data to analyze the impact angle, wall shear stress, and vessel wall pressure. Univariate logistic regression, LASSO regression and multivariate logistic regression were used for variable selection, and a nomogram model was constructed. Model performance was evaluated using the receiver operating characteristic (ROC) curve, Hosmer–Lemeshow (H–L) goodness-of-fit test, and decision curve analysis (DCA). Four predictors were included in the final nomogram model: age, history of hypertension, impact angle and maximum wall shear stress at the neurovascular contact (NVC) site. The model achieved an AUC of 0.956 (95% CI: 0.888–1.000) in the development cohort and 0.937 (95% CI: 0.885–0.959) upon internal validation, with good calibration and clinical utility demonstrated across the 10%–60% threshold range according to decision curve analysis (DCA). Incorporating hemodynamic factors enables more accurate prediction of postoperative recurrence in VBD-TN, offering substantial clinical value for individualized risk assessment.