Abstract T P26: Combining Clinical and Imaging Data to Develop a Highly Predictive Model of Outcomes in the MR RESCUE Trial
Background: Identifying patient characteristics that predict outcomes in acute ischemic stroke may assist in triaging those who are candidates for endovascular therapies. We sought to identify predictors of outcome in the overall Mechanical Retrieval and Recanalization of Stroke Clots Using Embolectomy (MR RESCUE) cohort and compare results to the previously validated Totaled Health Risks in Vascular Events (THRIVE) score. Methods: MR RESCUE randomized 118 acute ischemic stroke patients with multimodal imaging to embolectomy or standard care within 8 hours of onset. For this analysis, we investigated 17 baseline variables (e.g. age, predicted core volume, time to enrollment) and 8 intermediate variables (e.g. hemorrhagic transformation, day 7 recanalization, final infarct volume) with the potential to impact outcomes (day 90 mRS). The baseline variables were analyzed employing bivariate and multivariate methods (random forest and logistic regression). Two models were developed, one including only significant baseline variables, and the second also incorporating significant intermediate variables. Results: A multivariate model (Table) employing only baseline covariates achieved an overall accuracy (C statistic) of 85% in predicting poor outcome (day 90 mRS 3-6) compared to 80.5% for the THRIVE score. A second model (Table) adding significant intermediate variables achieved 89% accuracy in predicting day 90 mRS. Conclusions: In the MR RESCUE trial, advanced imaging variables, including predicted core volume and site of vessel occlusion, contributed to a highly accurate multivariable model of outcome. In the development phase, this model achieved higher accuracy than the THRIVE score. Future studies are needed to validate this model in an independent cohort.