Open AccessDetection of Left Ventricular Hypertrophy Using Bayesian Additive Regression Trees: The MESA (Multi‐Ethnic Study of Atherosclerosis)
Author(s) -
Sparapani Rodney,
Dabbouseh Noura M.,
Gutterman David,
Zhang Jun,
Chen Haiying,
Bluemke David A.,
Lima Joao A. C.,
Burke Gregory L.,
Soliman Elsayed Z.
Publication year - 2019
Publication title -
journal of the american heart association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.494
H-Index - 85
ISSN - 2047-9980
DOI - 10.1161/jaha.118.009959
Subject(s) - medicine , left ventricular hypertrophy , cardiology , hazard ratio , proportional hazards model , magnetic resonance imaging , myocardial infarction , coronary artery disease , mesa , cardiac magnetic resonance imaging , confidence interval , radiology , blood pressure , computer science , programming language
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.