Construction of a prognostic prediction model of endovascular mechanical thrombectomy for acute anterior circulation ischemic stroke based on collateral circulation score and clinical features

Objective To investigate the prognostic factors influencing patients who were diagnosed with acute ischemic stroke with anterior circulation large vessel occlusion (AIS-ACLVO) and underwent endovascular mechanical thrombectomy, and to construct a novel scoring model that incorporates collateral circulation score and clinical features for predicting patient prognosis. Methods This retrospective study included 159 patients with AIS-ACLVO who were admitted to the Emergency Stroke Green Channel or Neurology Department of Xuzhou Central Hospital and underwent endovascular mechanical thrombectomy from January 2021 to December 2023. According to the 90-day modified Rankin Scale (mRS) score, the patients were divided into two groups: a good prognosis group (mRS scores 0-2, n=68) and a poor prognosis group (mRS scores 3-6, n=91). Univariate analysis was used to identify factors influencing treatment outcomes, and the Boruta algorithm was applied to screen predictive variables related to the outcome indicators. Multivariate logistic regression was used to determine the independent risk factors for 90-day clinical prognosis of AIS-ACLVO patients after endovascular mechanical thrombectomy, and a prediction model was constructed. The model's discrimination was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), and its calibration was evaluated using calibration curves. A nomogram was constructed. Results Univariate and Boruta algorithm analysis showed that age, smoking history, blood glucose level at admission, baseline National Institutes of Health Stroke Scale (NIHSS) score, lymphocyte ratio, Alberta Stroke Program Early CT Score (ASPECTS), Tan score, and the interval from onset to admission were closely related to prognosis.The above indicators were incorporated into the multivariate logistic regression analysis, which demonstrated that smoking history, baseline NHISS score, blood glucose level at admission, ASPECTS, Tan score, and the interval from onset to admission were independent risk factors for the 90-day clinical prognosis of patients with AIS-ACLVO after endovascular mechanical thrombectomy. The established prediction model showed an AUC of 0.918 (95%CI: 0.876~0.960,P<0.001), with a sensitivity of 0.901, a specificity of 0.794, and a maximum Youden index of 0.695. The calibration curve of the model approximated with the ideal curve. Decision curve analysis suggested that the model has clinical utility. Conclusions Smoking history, baseline NHISS score, blood glucose level at admission, ASPECTS, Tan score, and the interval from onset to admission are independent risk factors for 90-day clinical prognosis of AIS-ACLVO patients after endovascular mechanical thrombectomy. The predictive model based on collateral circulation score and clinical features has certain predictive value for assessing the 90-day clinical prognosis of AIS-ACLVO patients after endovascular mechanical thrombectomy.