Internal Validation of a Clinical Prediction Model for Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

Delayed cerebral ischemia (DCI) is an important cause of secondary neurological injury after aneurysmal subarachnoid hemorrhage (aSAH). This retrospective single-center study developed and evaluated prediction models for DCI using data from 680 adult patients with aSAH treated between July 2022 and December 2024. Patients were divided using an outcome-stratified 8:2 split into a training cohort of 544 patients and a hold-out internal-validation cohort of 136 patients. DCI occurred in 175 patients in the training cohort and 42 patients in the internal-validation cohort. Predictor selection was performed in the training cohort using least absolute shrinkage and selection operator regression with 10-fold cross-validation. Six variables were retained: age, cerebral edema, hypoalbuminemia, modified Fisher grade, Hunt-Hess grade, and World Federation of Neurological Surgeons grade. Logistic regression, extreme gradient boosting, light gradient boosting machine, support vector machine, and k-nearest neighbor models were compared. Logistic regression showed an area under the receiver operating characteristic curve of 0.832 (95% confidence interval, 0.758-0.906) in the internal-validation cohort. Its calibration slope, calibration intercept, and Brier score were 0.98, 0.02, and 0.168, respectively, although confidence intervals for these calibration estimates were unavailable. The findings represent preliminary performance in a single hold-out internal-validation cohort. Incomplete reproducibility records, the absence of the model intercept, the lack of resampling-based optimism correction, and the absence of external validation currently prevent patient-level probability calculation and clinical implementation.