Faculty Collaboration Database

Detection of Left Ventricular Hypertrophy Using Bayesian Additive Regression Trees: The MESA. J Am Heart Assoc 2019 Mar 05;8(5):e009959

Date

03/05/2019

Pubmed ID

30827132

Pubmed Central ID

PMC6474924

DOI

10.1161/JAHA.118.009959

Scopus ID

2-s2.0-85062384504 (requires institutional sign-in at Scopus site)   28 Citations

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%; 95% CI , 18.3%-39.7%), followed by Sokolow-Lyon- LVH (21.7%; 95% CI , 12.0%-31.5%), Peguero-Lo Presti (14.5%; 95% CI , 6.2%-22.8%), Cornell voltage product (10.1%; 95% CI , 3.0%-17.3%), and Cornell voltage (5.8%; 95% CI , 0.3%-11.3%). The specificity was >93% for all criteria. During a median follow-up of 12.3 years, 591 deaths, 492 cardiovascular disease events, and 332 coronary heart disease events were observed. In adjusted Cox models, both BART - LVH and cardiac magnetic resonance imaging- LVH were associated with mortality (hazard ratio [95% CI ], 1.88 [1.45-2.44] and 2.21 [1.74-2.81], respectively), cardiovascular disease events (hazard ratio [95% CI ], 1.46 [1.08-1.98] and 1.91 [1.46-2.51], respectively), and coronary heart disease events (hazard ratio [95% CI ], 1.72 [1.20-2.47] and 1.96 [1.41-2.73], respectively). These associations were stronger than associations observed with traditional ECG - LVH criteria. Conclusions Our new BART - LVH criteria have superior diagnostic/prognostic ability to traditional ECG - LVH criteria and similar performance to cardiac magnetic resonance imaging- LVH for predicting events.

Author List

Sparapani R, Dabbouseh NM, Gutterman D, Zhang J, Chen H, Bluemke DA, Lima JAC, Burke GL, Soliman EZ

Author

Rodney Sparapani PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Aged
Aged, 80 and over
Bayes Theorem
Decision Trees
Diagnosis, Computer-Assisted
Electrocardiography
Female
Humans
Hypertrophy, Left Ventricular
Machine Learning
Magnetic Resonance Imaging
Male
Middle Aged
Predictive Value of Tests
Reproducibility of Results
United States
Ventricular Function, Left
Ventricular Remodeling