Machine Learning-Based Identification of Target Groups for Thrombectomy in Acute Stroke.

ABSTRACT: Whether endovascular thrombectomy (EVT) improves functional outcome in patients with large-vessel occlusion (LVO) stroke that do not comply with inclusion criteria of randomized controlled trials (RCTs) but that are considered for EVT in clinical practice is uncertain. We aimed to systematically identify patients with LVO stroke underrepresented in RCTs who might benefit from EVT. Following the premises that (i) patients without reperfusion after EVT represent a non-treated control group and (ii) the level of reperfusion affects outcome in patients with benefit from EVT but not in patients without treatment benefit, we systematically assessed the importance of reperfusion level on functional outcome prediction using machine learning in patients with LVO stroke treated with EVT in clinical

SUBMITTER: Quandt F 

PROVIDER: S-EPMC10159968 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature