Key result
Machine learning models using ambulatory blood pressure data accurately classify LVH with ~0.95 AUC.
Why the study?
LVH predicts adverse cardiac outcomes but LVMI measurement currently requires transthoracic echocardiography, which is accessible only in clinical settings.
Can deep learning models accurately predict left ventricular mass index and classify left ventricular hypertrophy using ambulatory blood pressure measurements and basic clinical data?
Observational
Can deep learning models accurately predict left ventricular mass index and classify left ventricular hypertrophy using ambulatory blood pressure measurements and basic clinical data?
Deep learning models utilizing ambulatory blood pressure measurements and basic clinical data can accurately predict left ventricular mass index and classify hypertrophy, offering a potential non-invasive, out-of-clinic tool for cardiovascular risk assessment.
Answers are grounded in this paper and cite their sources.
ML models may aid non-invasive LVH assessment from routine data; hypothesis-generating and requires prospective validation before clinical use.
Left ventricular hypertrophy (LVH), clinically defined as an elevated left ventricular mass index (LVMI), is a strong predictor of adverse cardiac outcomes1,2. Currently, measurements of LVMI are performed using transthoracic echocardiography, accessible only in clinical settings. In this study, we use data from an ambulatory blood pressure monitor, combined with laboratory test results and demographics data collected once, to investigate whether LVMI and LVH may be predicted and classified accurately. We developed and tested machine learning models using clinical data. For LVMI prediction, a mean absolute error of 10.4 ± 1.5 with our test set, and 15.6 ± 1.2 with an external test set, were achieved. For the classification of LVH and concentric LVH, mean areas under the curve of ≥0.95 and ≥0.93 were achieved, respectively. This study presents a method for accurately assessing cardiovascular risk using ambulatory measurements and highlights the use of waveform data for assessing hypertrophy.
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Behnam et al. (2025) conducted an observational in Left ventricular hypertrophy. Machine learning models using ambulatory blood pressure monitor data was evaluated on LVMI prediction and classification of LVH. Machine learning models using ambulatory blood pressure monitor data, laboratory tests, and demographics accurately predicted LVMI (MAE 10.4 ± 1.5) and classified LVH (AUC ≥0.95).
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