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Updated: Jan 28, 2026

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

Rodney Sparapani1,2, Noura M Dabbouseh2,3, David Gutterman2,3

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

Journal of the American Heart Association
|March 5, 2019
PubMed
Summary

A new machine-learning criterion for left ventricular hypertrophy (LVH) called Bayesian Additive Regression Trees (BART)-LVH demonstrates superior diagnostic and prognostic capabilities compared to traditional ECG criteria. This advanced method shows similar performance to cardiac MRI for predicting cardiovascular events.

Keywords:
ECGensemble predictive modelingleft ventricular hypertrophynonparametric machine learning

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Machine Learning in Healthcare

Background:

  • Left ventricular hypertrophy (LVH) is a significant risk factor for cardiovascular disease.
  • Traditional electrocardiogram (ECG) criteria for diagnosing LVH have limitations in sensitivity and specificity.
  • Development of novel, accurate methods for LVH detection is crucial for risk stratification.

Purpose of the Study:

  • To develop and validate a new LVH criterion using Bayesian Additive Regression Trees (BART), a machine-learning technique.
  • To compare the diagnostic and prognostic performance of the new BART-LVH criterion against traditional ECG-LVH criteria and cardiac MRI.

Main Methods:

  • Utilized the Multi-Ethnic Study of Atherosclerosis (MESA) cohort (n=4714) free of cardiovascular disease at baseline.
  • Employed BART to predict LV mass from ECG and participant characteristics, with cardiac MRI serving as the gold standard.
  • Randomly split the cohort into training (n=3774) and validation (n=940) sets for model development and testing.

Main Results:

  • In the validation set, BART-LVH exhibited the highest sensitivity (29.0%) for LVH detection compared to traditional ECG criteria (e.g., Sokolow-Lyon-LVH at 21.7%).
  • All criteria demonstrated high specificity (>93%).
  • During a median follow-up of 12.3 years, BART-LVH and cardiac MRI-LVH were significantly associated with increased risk of mortality, cardiovascular disease events, and coronary heart disease events, outperforming traditional ECG criteria.

Conclusions:

  • The novel BART-LVH criterion offers superior diagnostic and prognostic accuracy for identifying LVH compared to conventional ECG-based methods.
  • BART-LVH demonstrates comparable performance to cardiac MRI in predicting adverse cardiovascular outcomes.
  • This machine-learning approach represents a promising advancement in cardiovascular risk assessment and management.