Machine Learning-Based Prediction of Delayed Cerebral Ischemia and Functional Outcome in Aneurysmal Subarachnoid...

Background/Objectives: Aneurysmal subarachnoid hemorrhage (aSAH) carries high morbidity and mortality, with delayed cerebral ischemia (DCI) as a major secondary complication. Although infection has increasingly been implicated in DCI pathogenesis, the role of central nervous system (CNS) infection remains unclear. We evaluated CNS infection-related parameters for predicting DCI and functional outcome after aSAH using machine learning. Methods: This retrospective single-center study included 191 patients with confirmed aSAH treated in the intensive care unit between 2019 and 2024. Demographic, clinical, radiographic, treatment-related, microbiological, cerebrospinal fluid (CSF), and longitudinal laboratory data were extracted from electronic records. DCI was analyzed as a binary outcome, and discharge functional outcome was dichotomized using the modified Rankin Scale (0-2 vs. 3-6). Twelve feature selection methods and twelve classifiers were evaluated using five-fold cross-validation. Results: For DCI prediction, the models achieved a mean accuracy of 0.96, F1-score of 0.96, and AUC of 0.98. Key predictors included antibiotic therapy, aneurysm treatment modality, history of thrombosis, and EVD revision, along with mean albumin, red cell distribution width, INR, minimum aspartate aminotransferase, and CSF glucose, lactate, nucleated cell count, and cellular debris. For functional outcome prediction, accuracy was 0.948, F1-score 0.95, and AUC was 0.97. Predictors comprised cerebral vasospasm, number of spasmolysis procedures, pre-admission anticoagulation, CSF pathogen detection and type, aPTT, fibrinogen, aspartate aminotransferase, lactate dehydrogenase, and CSF mononuclear cell proportion, leukocyte count, and glucose concentration. Conclusions: Machine learning models integrating clinical, systemic, and CSF-derived parameters demonstrated excellent performance in predicting both DCI and functional outcome after aSAH. The identified predictors highlight the importance of the neurocritical care course, including infection-related and thromboinflammatory processes, and extend beyond traditional vasospasm-centered paradigms. These findings may inform risk stratification, although the temporal relationship between several predictors and outcome onset means the models should be regarded as hypothesis-generating rather than tools for early clinical prediction.