Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning.

ABSTRACT: Background Carotid endarterectomy (CEA) is a major vascular operation for stroke prevention that carries significant perioperative risks; however, outcome prediction tools remain limited. The authors developed machine learning algorithms to predict outcomes following CEA. Methods and Results The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent CEA between 2011 and 2021. Input features included 36 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse cardiovascular events (composite of stroke, myocardial infarction, or death). The data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The

SUBMITTER: Li B 

PROVIDER: S-EPMC10757546 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature