Detection of Left Ventricular Hypertrophy Using Bayesian Additive Regression Trees: The MESA (Multi‐Ethnic Study of Atherosclerosis)

Author:

Sparapani Rodney12,Dabbouseh Noura M.23,Gutterman David23,Zhang Jun4,Chen Haiying5,Bluemke David A.6,Lima Joao A. C.7,Burke Gregory L.8,Soliman Elsayed Z.910

Affiliation:

1. Institute for Health and Equity Division of Biostatistics Medical College of Wisconsin Milwaukee WI

2. Cardiovascular Center Medical College of Wisconsin Milwaukee WI

3. Division of Cardiology Department of Medicine Medical College of Wisconsin Milwaukee WI

4. Department of Electrical Engineering and Computer Science University of Wisconsin–Milwaukee Milwaukee WI

5. Division of Public Health Sciences Department of Biostatistical Sciences Wake Forest School of Medicine Winston Salem NC

6. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI

7. Division of Cardiology and Department of Radiology Department of Medicine Johns Hopkins University Baltimore MD

8. Division of Public Health Sciences Wake Forest School of Medicine Winston Salem NC

9. Epidemiological Cardiology Research Center Department of Epidemiology and Prevention Wake Forest School of Medicine Winston Salem NC

10. Section on Cardiology Department of Internal Medicine Wake Forest School of Medicine Winston Salem NC

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 BARTLVH criteria with traditional ECGLVH criteria and cardiac magnetic resonance imaging– LVH . In the validation set, BARTLVH 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 BARTLVH 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 ECGLVH criteria. Conclusions Our new BARTLVH criteria have superior diagnostic/prognostic ability to traditional ECGLVH criteria and similar performance to cardiac magnetic resonance imaging– LVH for predicting events.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

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