A comprehensive prognostic model for delayed cerebral ischemia in rupture intracranial aneurysms after surgical clipping

Abstract

Background and Objectives: Delayed cerebral ischemia (DCI), one of the most common complications following aneurysmal subarachnoid hemorrhage (SAH), was strongly related to poor patient outcomes. Identifying predictive factors for its occurrence is crucial for improving patient care and outcomes. Our research aimed to explore risk factors for delayed cerebral ischemia in aneurysm patients after surgical clipping and developed a prediction model. Methods: The datasets used in this study are available from the corresponding author upon reasonable request. Patients demographics, aneurysm features, comorbidities, clinical manifestations, imaging features, blood pressure on admission，incidence of DCI, and interventions were recorded. SAH patients were randomly assigned to the training or validation cohort based on a ratio of 7:3, which was implemented as internal validations for the final predictive models. The predictive ability was assessed by the area under the receiver operating characteristic (ROC) curve. Results: A total of 272 patients were included in our research. The final logistic model included 7 independent predictors (Age, Smoking, Drinking, WFNS, Fisher, MAP on Second postoperative day, and Na) and was developed as a simple-to-use nomogram. The training set and validation set model's C-index are 0.844 and 0.766, demonstrating moderate predictive ability with regard to risks of DCI. Conclusion: We developed a model with seven independent risk factors to predict the incidence of DCI. We focused on exploring the postoperative status in DCI patients. Elevated blood pressure on the second postoperative day may indicate the early occurrence of microvascular spasm.