TY - JOUR
T1 - Detection of Left Ventricular Hypertrophy Using Bayesian Additive Regression Trees
T2 - The MESA
AU - Sparapani, Rodney
AU - Dabbouseh, Noura M.
AU - Gutterman, David
AU - Zhang, Jun
AU - Chen, Haiying
AU - Bluemke, David A.
AU - Lima, Joao A.C.
AU - Burke, Gregory L.
AU - Soliman, Elsayed Z.
N1 - Funding Information:
This research was supported by contracts HHSN268201 500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute; and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences. Sparapani and Zhang were supported
Funding Information:
The authors thank the other investigators, the staff, and the participants of the MESA (Multi-Ethnic Study of Atherosclerosis) for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.
Publisher Copyright:
© 2019 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.
PY - 2019/3/5
Y1 - 2019/3/5
N2 - 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.
AB - 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.
KW - ECG
KW - ensemble predictive modeling
KW - left ventricular hypertrophy
KW - nonparametric machine learning
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U2 - 10.1161/JAHA.118.009959
DO - 10.1161/JAHA.118.009959
M3 - Article
C2 - 30827132
AN - SCOPUS:85062384504
VL - 8
JO - Journal of the American Heart Association
JF - Journal of the American Heart Association
SN - 2047-9980
IS - 5
M1 - e009959
ER -