Machine Learning Algorithms for Prediction of Survival by Stress Echocardiography in Chronic Coronary Syndromes.

ABSTRACT: Stress echocardiography (SE) is based on regional wall motion abnormalities and coronary flow velocity reserve (CFVR). Their independent prognostic capabilities could be better studied with a machine learning (ML) approach. The study aims to assess the SE outcome data by conducting an analysis with an ML approach. We included 6881 prospectively recruited and retrospectively analyzed patients with suspected (n = 4279) or known (n = 2602) coronary artery disease submitted to clinically driven dipyridamole SE. The outcome measure was all-cause death. A random forest survival model was implemented to model the survival function according to the patient's characteristics; 1002 patients recruited by a single, independent center formed the external validation cohort. During a median

SUBMITTER: Cortigiani L 

PROVIDER: S-EPMC9504503 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature