Multivariable Prediction Model for Futile Recanalization Therapies in Patients With Acute Ischemic Stroke.

ABSTRACT:

Background and objectives

Very poor outcome despite intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) occurs in about 1 of 4 patients with ischemic stroke and is associated with a high logistic and economic burden. We aimed to develop and validate a multivariable prognostic model to identify futile recanalization therapies (FRT) in patients undergoing those therapies.

Materials and methods

Patients from a prospectively collected observational registry of a single academic stroke center treated with MT and/or IVT were included. The dataset was split into a training (N=1808, 80%) and internal validation (N=453, 20%) cohort. We used gradient boosted decision tree machine-learning models after k-NN imputation of 32 variables available at admission to predict FRT de

SUBMITTER: Meinel T 

PROVIDER: S-EPMC9519255 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature