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

Summary

An exploratory machine-learning (ML) framework using perioperative data was evaluated to predict contrast-induced encephalopathy (CIE) after neurointerventional procedures. The Naive Bayes model showed the most balanced descriptive performance, and contrast-to-eGFR ratio (CGR) was identified as an important candidate predictor for individualized risk assessment.

Abstract

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.

Introduction

Over the past two decades, minimally invasive interventional techniques have fundamentally transformed the treatment paradigm for cerebrovascular diseases¹. Neurointerventional procedures—with their advantages of high efficacy and minimal invasiveness—have become important treatment options for selected cerebrovascular disorders². However, as the complexity of interventional procedures increases, so do the doses of contrast agents used during procedures, the injection pressures, and the duration of contrast-agent exposure. Consequently, contrast-agent–related complications have gradually come under incr....

Protocol

The study protocol was reviewed and approved by the Ethics Committee of the General Hospital of Northern Theater Command (Approval No. Ethics Y (2026) 75). Although the clinical data were derived from patients treated between January 2024 and December 2025, the present study was conducted as a retrospective analysis of existing clinical records. The ethics approval obtained in 2026 covered retrospective data extraction, de-identification, analysis, and publication of these previously collected clinical data. No prospective intervention or patient enrollment was performed before ethics approval. The requirement for written informed consent was waived because of the ret....

Results

Patient enrollment and CIE determination

A total of 161 patients who underwent neurointerventional procedures were included in this study, among whom 12 (7.5%) developed CIE, while 149 (92.5%) did not. The detailed patient screening and grouping process is shown in Figure 2. The diagnosis of CIE was established based on the temporal association between symptom onset and neurointerventional procedures, combined with exclusionary imaging findings. R.......

Discussion

CIE is a rare but potentially serious complication following neurointerventional procedures, characterized by acute neurological dysfunction that occurs shortly after exposure to contrast agents. Although the condition is typically reversible, in some cases it can lead to persistent neurological deficits or even life-threatening outcomes, underscoring the importance of early risk identification^17,18,19,

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Electronic medical-record system	Northern Theater General Hospital	Institutional clinical system	Used for retrospective data extraction Collection of demographic and clinical-history variables
Laboratory information system	Northern Theater General Hospital	Institutional laboratory system	Used for retrospective laboratory-data extraction Collection of serum creatinine, eGFR, lipid profile, and other laboratory data
Digital subtraction angiography system	Philips	Allura Xper FD 20 (with Interventional Workstation R1.3.2)	Used during routine clinical procedures Neurointerventional procedure and recording of contrast use
CT Scanner	Philips	Ingenuity CT	Used for postoperative imaging assessment Postoperative cranial CT evaluation
OnekeyAI structured-data analysis platform	OnekeyAI	20240916	Used as a research tool; no role in clinical decision-making Structured-data preprocessing, model construction, model evaluation, and figure generation
Python	Python Software Foundation	3.7.12	Open-source programming language Data preprocessing, statistical analysis, model development, and visualization
scikit-learn	scikit-learn developers	1.0.2	Open-source Python machine-learning library LASSO, Naive Bayes, SVM, KNN, MLP, ROC analysis, and performance metrics