Novel predictive nomograms for delayed cerebral ischemia and poor neurological function for patients after the Aneurysmal Subarachnoid Hemorrhage: a retrospective study

Objective: Aneurysmal subarachnoid hemorrhage (aSAH) is a severe stroke subtype often complicated by Delayed Cerebral Ischemia (DCI) and poor neurological outcomes. This study aims to develop novel predictive models for DCI and functional recovery following aSAH. Methods: A total of 195 cases with aSAH from Taian City Central Hospital, hospitalized between September 2019 and January 2023, were retrospectively analyzed. Patients were categorized into two groups: the DCI group and the non-DCI group. Additionally, patients were further categorized into the good outcome group and the poor outcome group based on modified Rankin scale scores assessed three months after discharge. We compared baseline characteristics among the different groups to identify key variables for subsequent multivariable logistic regression analysis. The nomogram was used to visualize the model. The Calibration Curve, Decision Curve Analysis (DCA) curve, and Receiver Operating Characteristic (ROC) curve were employed to evaluate the performance of these models. Results: < 0.05). This model achieved an AUC of 0.837, with a sensitivity of 95.5% and specificity of 61.0%. The calibration curve indicated a strong alignment between the predicted probabilities and the actual outcomes. Additionally, the DCA curve suggested that early intervention could provide substantial benefits. Conclusions: We developed two nomograms to predict DCI and 3-month outcome after aSAH. Hypertension and Hunt-Hess grade predicted DCI, while age, Hunt-Hess grade, and ND predicted poor outcome. Both models showed stable discrimination on internal validation.