Development and Validation of a Two-step Model to Predict Outcomes After Endovascular Treatment for Patients With Acute Ischemic Stroke.

Abstract

Physicians and patients are eager to know likely functional outcomes at different stages of treatment after acute ischemic stroke (AIS). The aim of this study was to develop and validate a 2-step model to assess prognosis at different time points (pre- and posttreatment) in patients with AIS having endovascular thrombectomy (EVT). The prediction model was developed using a prospective nationwide Chinese registry (ANGEL-ACT). A total of 1676 patients with AIS who underwent EVT were enrolled into the study and randomly divided into development (n=1351, 80%) and validation (n=325, 20%) cohorts. Multivariate logistic regression, least absolute shrinkage and selection operator regression, and the random forest recursive feature elimination algorithm were used to select predictors of 90-day functional independence. We constructed the model via discrimination, calibration, decision curve analysis, and feature importance. The incidence of 90-day functional independence was 46.3% and 40.6% in the development and validation cohorts, respectively. The area under the curve (AUC) for model 1 which included 5 pretreatment predictors (age, admission National Institutes for Health Stroke Scale score, admission glucose level, admission systolic blood pressure, and Alberta Stroke Program Early Computed Tomography score) was 0.699 (95% confidence interval [CI], 0.668-0.730) in the development cohort and 0.658 (95% CI, 0.592-0.723) in the validation cohort. Two treatment-related predictors (time from stroke onset to puncture and successful reperfusion) were added to model 2 which had an AUC of 0.719 (95% CI, 0.688-0.749) and 0.650 (95% CI, 0.585-0.716) in the development cohort and validation cohorts, respectively. The 2-step prediction model could be useful for predicting the functional independence in patients with AIS 90-days after EVT.