Machine-Learning Prediction of Contrast-Induced Encephalopathy After Neurointerventional Procedures.

CIE is a rare yet severe complication following neurointerventional procedures, for which reliable noninvasive predictive tools remain lacking. This study aimed to develop and validate a ML model for predicting CIE using perioperative clinical and procedural data, and to evaluate the predictive value of the CGR for CIE. This was a single-center retrospective study that consecutively enrolled 161 patients who underwent neurointerventional procedures in the Department of Neurosurgery at the Northern Theater General Hospital between January 2024 and December 2025. Candidate perioperative predictors of CIE were identified using univariate analysis combined with LASSO regression. Based on the selected variables, five ML models-Naive Bayes, support vector machine (SVM), k-nearest neighbors (KNN), LightGBM, and multilayer perceptron (MLP)-were trained and optimized. Model performance was comprehensively evaluated using the area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA). Among the evaluated models, the Naive Bayes model showed the most favorable descriptive performance in the internal test set, with an AUC of 0.952 (95% CI: 0.843-1.000), a sensitivity of 100%, and a specificity of 90.3%; however, these metrics should be interpreted cautiously because the dataset was markedly imbalanced and the internal test cohort was small and contained only a very limited number of CIE events. The DCA results suggested potential clinical net benefit across selected threshold probabilities. Feature-importance analysis indicated that CGR was among the highest-ranked candidate predictors associated with CIE risk in this dataset. The Naive Bayes model evaluated in this study may provide a preliminary risk-stratification framework for perioperative assessment of CIE after neurointerventional procedures. CGR emerged as an important predictive feature and may aid individualized risk stratification. Further external validation is warranted before clinical application.