Detection of Left Ventricular Hypertrophy Using Bayesian Additive Regression Trees: The MESA.

ABSTRACT: Background We developed a new left ventricular hypertrophy ( LVH ) criterion using a machine-learning technique called Bayesian Additive Regression Trees ( BART ). Methods and Results This analysis included 4714 participants from MESA (Multi-Ethnic Study of Atherosclerosis) free of clinically apparent cardiovascular disease at enrollment. We used BART to predict LV mass from ECG and participant characteristics using cardiac magnetic resonance imaging as the standard. Participants were randomly divided into a training set (n=3774) and a validation set (n=940). We compared the diagnostic/prognostic performance of our new BART - LVH criteria with traditional ECG - LVH criteria and cardiac magnetic resonance imaging- LVH . In the validation set, BART - LVH showed the highest sensitivity (29.0%

SUBMITTER: Sparapani R 

PROVIDER: S-EPMC6474924 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature