New grading scale based on early factors for predicting delayed cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage: a multicenter retrospective study

Abstract

Delayed cerebral ischemia (DCI) could lead to poor clinical outcome(s). The aim of the present study was to establish and validate a predictive model for DCI after aneurysmal subarachnoid hemorrhage (aSAH) based on clinical data. Data from a series of 217 consecutive patients with aSAH were reviewed and analyzed. Related risk factors within 72 h after aSAH were analyzed depending on whether DCI recurred. Least absolute shrinkage and selection operator (LASSO) analysis was performed to reduce data dimensions and screen for optimal predictors. Multivariable logistic regression was used to establish a predictive model and construct a nomogram. Receiver operating characteristic (ROC) and calibration curves were generated to assess the discriminative ability and goodness of fit of the model. Decision curve analysis was applied to evaluated the clinical applicability of the predictive model. LASSO regression identified 4 independent predictors, including Subarachnoid Hemorrhage Early Brain Edema Score (i.e., “SEBES”), World Federation of Neurosurgical Societies scale score (i.e., “WFNS”), modified Fisher Scale score, and intraventricular hemorrhage (IVH), which were incorporated into logistic regression to develop a nomogram. After verification, the area under the ROC curve for the model was 0.860. The calibration curve indicated that the predictive probability of the new model was in good agreement with the actual probability, and decision curve analysis demonstrated the clinical applicability of the model within a specified range. The prediction model could precisely calculate the probability of DCI after aSAH, and may contribute to better clinical decision-making and personalized treatment to achieve better outcomes.