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

ConclusionsThe “Early” XGBoost and the “Late” XGBoost allowed us to predict futile recanalization before and after EVT accurately. Our study suggests that including peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 h was the most important prognostic factor for futile recanalization.
Source: Frontiers in Neurology - Category: Neurology Source Type: research