Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy.

ABSTRACT:

Background and purpose

Futile recanalization occurs when the endovascular thrombectomy (EVT) is a technical success but fails to achieve a favorable outcome. This study aimed to use machine learning (ML) algorithms to develop a pre-EVT model and a post-EVT model to predict the risk of futile recanalization and to provide meaningful insights to assess the prognostic factors associated with futile recanalization.

Methods

Consecutive acute ischemic stroke patients with large vessel occlusion (LVO) undergoing EVT at the National Advanced Stroke Center of Nanjing First Hospital (China) between April 2017 and May 2021 were analyzed. The baseline characteristics and peri-interventional characteristics were assessed using four ML algorithms. The predictive performance was evaluated

SUBMITTER: Lin X 

PROVIDER: S-EPMC9437637 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature