Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imaging - Zihang Wang; Zihang Wang; Chang Yan; Chang Yan; Wenqing Yuan; Wenqing Yuan; Shuangyan Jiang; Shuangyan Jiang; Yongxiang Jiang; Ting Chen (Frontiers in Neurology)
ObjectiveThis study aimed to develop and validate a multivariate logistic regression model for predicting intracranial aneurysm (IA) rupture by integrating clinical data, aneurysm morphology, and parent artery characteristics using high-resolution vessel wall imaging (HR-VWI).MethodsA retrospective analysis was conducted on 298 patients with 386 aneurysms. Patients were randomly divided into training (n = 308) and validation (n = 78) sets. Key predictors, including aneurysm size, shape, aneurysm wall and parent artery wall enhancement, were identified through univariate analysis and then used to build the prediction model using multivariate logistic regression. The model was visualized as a nomogram and compared to PHASES and ELAPSS scores.ResultsThe logistic regression model demonstrated superior predictive performance with an area under the curve of 0.814, which was significantly higher than PHASES and ELAPSS scores (p < 0.05). The model revealed strong calibration and good agreement between predicted and observed rupture probabilities.ConclusionThe multivariate model based on HR-VWI, which incorporates aneurysm and parent artery features, provides a more accurate prediction of IA rupture risk than conventional scoring systems, offering a valuable tool for clinical decision-making.