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

Journal: Journal of the American Heart Association
Published Date: Mar 5, 2019

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.

Authors

  • Rodney Sparapani
    1 Institute for Health and Equity Division of Biostatistics Medical College of Wisconsin Milwaukee WI.
  • Noura M Dabbouseh
    2 Cardiovascular Center Medical College of Wisconsin Milwaukee WI.
  • David Gutterman
    2 Cardiovascular Center Medical College of Wisconsin Milwaukee WI.
  • Jun Zhang
    First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
  • Haiying Chen
    5 Division of Public Health Sciences Department of Biostatistical Sciences Wake Forest School of Medicine Winston Salem NC.
  • David A Bluemke
    From the Department of Radiology (B.A.-V.), Bloomberg School of Public Health (E.G.), and Department of Medicine, Cardiology and Radiology (J.A.C.L.), Johns Hopkins University, Baltimore, MD; George Washington University, DC (X.Y.); Office of Biostatistics, NHLBI, NIH, Bethesda, MD (C.O.W.); Department of Preventive Medicine, Northwestern University Medical School, Chicago, IL (K.L.); Department of Cardiology, Wake Forest University Health Sciences, Winston-Salem, NC (W.G.H.); Department of Biostatistics, University of Washington, Seattle (R.M.); Department of Radiology, UCLA School of Medicine, Los Angeles, CA (A.S.G.); Division of Epidemiology and Community Health, University of Minnesota, Minneapolis (A.R.F.); Departments of Medicine and Epidemiology, Columbia University, New York, NY (S.S.); and Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD (D.A.B.).
  • João A C Lima
    From the Department of Radiology (B.A.-V.), Bloomberg School of Public Health (E.G.), and Department of Medicine, Cardiology and Radiology (J.A.C.L.), Johns Hopkins University, Baltimore, MD; George Washington University, DC (X.Y.); Office of Biostatistics, NHLBI, NIH, Bethesda, MD (C.O.W.); Department of Preventive Medicine, Northwestern University Medical School, Chicago, IL (K.L.); Department of Cardiology, Wake Forest University Health Sciences, Winston-Salem, NC (W.G.H.); Department of Biostatistics, University of Washington, Seattle (R.M.); Department of Radiology, UCLA School of Medicine, Los Angeles, CA (A.S.G.); Division of Epidemiology and Community Health, University of Minnesota, Minneapolis (A.R.F.); Departments of Medicine and Epidemiology, Columbia University, New York, NY (S.S.); and Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD (D.A.B.). jlima@jhmi.edu.
  • Gregory L Burke
    8 Division of Public Health Sciences Wake Forest School of Medicine Winston Salem NC.
  • Elsayed Z Soliman
    9 Epidemiological Cardiology Research Center Department of Epidemiology and Prevention Wake Forest School of Medicine Winston Salem NC.

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