Technical Risk Stratification Nomogram Model for 90‐Day Mortality Prediction in Patients With Acute Basilar Artery Occlusion Undergoing Endovascular Thrombectomy: A Multicenter Cohort Study

Abstract

Background This study aimed to establish and validate a nomogram model for predicting 90‐day mortality in patients with acute basilar artery occlusion receiving endovascular thrombectomy. Methods and Results A total of 242 patients with basilar artery occlusion undergoing endovascular thrombectomy were enrolled in our study, in which 172 patients from 3 stroke centers were assigned to the training cohort, and 70 patients from another center were assigned to the validation cohort. Univariate and multivariate logistic regression analyses were adopted to screen prognostic predictors, and those with significance were subjected to establish a nomogram model in the training cohort. The discriminative accuracy, calibration, and clinical usefulness of the nomogram model was verified in the internal and external cohorts. Six variables, including age, baseline National Institutes of Health Stroke Scale score, Posterior Circulation–Alberta Stroke Program Early CT (Computed Tomography) score, Basilar Artery on Computed Tomography Angiography score, recanalization failure, and symptomatic intracranial hemorrhage, were identified as independent predictors of 90‐day mortality of patients with basilar artery occlusion and were subjected to develop a nomogram model. The nomogram model exhibited good discrimination, calibration, and clinical usefulness in both the internal and the external cohorts. Additionally, patients were divided into low‐, moderate‐, and high‐risk groups based on the risk‐stratified nomogram model. Conclusions Our study proposed a novel nomogram model that could effectively predict 90‐day mortality of patients with basilar artery occlusion after endovascular thrombectomy and stratify patients with high, moderate, or low risk, which has a potential to facilitate prognostic judgment and clinical management of stroke.