P.136 Evaluating outcome prediction models in endovascular treatment for acute ischemic stroke using baseline, treatment and post-treatment variables

Abstract

Background: Predicting outcomes after endovascular treatment (EVT) for acute ischemic stroke with baseline variables remains a challenge. We assessed the performance of stroke outcome prediction models for EVT in acute ischemic stroke in an iterative fashion using baseline, treatment-related and post-treatment variables. Methods: Data from the ESCAPE-NA1 trial were used to build 4 outcome prediction models using multi-variable logistic regression: Model 1 included baseline variables only that are available prior to treatment decision-making, model 2 included additional treatment-related variables, model 3 additional early post-treatment variables, and model 4 additional late post-treatment variables. The primary outcome was 90-day modified Rankin Scale score 0-2. Model performance was compared using the area under the curve (AUC). Results: Among 1,105 patients, good outcome was achieved by 666 (60.3%). When using baseline variables only (model 1), the AUC was 0.74 (95%CI:0.71-0.77); this iteratively improved when treatment and post-treatment variables were added to the models (model 2: AUC 0.77,95%CI: 0.74-0.80, model 3: AUC 0.80,95%CI:0.77-0.83, model 4: AUC 0.82, 95%CI:0.79-0.85). Conclusions: Predicting EVT outcomes using baseline variables alone is inaccurate in one in four patients, and may be inappropriate for patient selection. Even the most comprehensive models with treatment-related and post-treatment factors involve considerable uncertainty.