Hemodynamic parameters as biomarkers for predicting microvascular decompression efficacy in classic trigeminal neuralgia.

Abstract

The aim of this study was to evaluate the role of hemodynamic parameters in predicting the efficacy of microvascular decompression (MVD) in patients with classic trigeminal neuralgia (CTN) using computational fluid dynamics (CFD). Patients with unilateral CTN were recruited from May 2022 to December 2023. Preoperative time-of-flight MR angiography was used to identify neurovascular compression sites. CFD simulations were performed to analyze hemodynamic parameters such as peak systolic flow (PSF), peak systolic pressure drop (PSPD), maximum wall shear stress (WSS), and oscillatory shear index (OSI). Logistic regression analysis was used to develop predictive models for MVD efficacy. Fifty-six patients were included (28 in the effective MVD group and 28 in the ineffective MVD group). The effective group exhibited significantly lower PSF (mean 0.202 [SD 0.136] vs 0.306 [SD 0.142] ml/sec, p = 0.007) and higher PSPD (mean 33.239 [SD 20.122] vs 22.864 [SD 15.624] Pa, p = 0.036), maximum WSS (median 3.231 [interquartile range (IQR) 2.084-4.359] vs 2.197 [IQR 1.592-3.445] Pa, p = 0.024), and OSI (median 0.001 [IQR 0.001-0.002] vs 0.001 [IQR 0.001-0.001], p = 0.029). Logistic regression analysis identified PSF and maximum WSS as significant predictors of MVD efficacy. The developed prediction models showed high accuracy, with model 2 (using the backward logistic regression method) achieving an area under the receiver operating characteristic curve of 0.920 and both sensitivity and specificity of 90%. Hemodynamic parameters, particularly PSF and maximum WSS, significantly predict MVD efficacy in CTN. Integrating these parameters into clinical practice could improve surgical outcomes and guide personalized treatment strategies.