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. 2023 Jun 1;4(4):316-324.
doi: 10.1093/ehjdh/ztad037. eCollection 2023 Aug.

Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning

Hafiz Naderi  1   2 , Julia Ramírez  1   3 , Stefan van Duijvenboden  1   4 , Esmeralda Ruiz Pujadas  5 , Nay Aung  1   6   2 , Lin Wang  7 , Choudhary Anwar Ahmed Chahal  2   8   9 , Karim Lekadir  5 , Steffen E Petersen  1   6   2   10   11 , Patricia B Munroe  1   6
Affiliations

Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning

Hafiz Naderi et al. Eur Heart J Digit Health. .

Abstract

Aims: Left ventricular hypertrophy (LVH) is an established, independent predictor of cardiovascular disease. Indices derived from the electrocardiogram (ECG) have been used to infer the presence of LVH with limited sensitivity. This study aimed to classify LVH defined by cardiovascular magnetic resonance (CMR) imaging using the 12-lead ECG for cost-effective patient stratification.

Methods and results: We extracted ECG biomarkers with a known physiological association with LVH from the 12-lead ECG of 37 534 participants in the UK Biobank imaging study. Classification models integrating ECG biomarkers and clinical variables were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% training and 20% test sets for performance evaluation. Ten-fold cross validation was applied with further validation testing performed by separating data based on UK Biobank imaging centres. QRS amplitude and blood pressure (P < 0.001) were the features most strongly associated with LVH. Classification with logistic regression had an accuracy of 81% [sensitivity 70%, specificity 81%, Area under the receiver operator curve (AUC) 0.86], SVM 81% accuracy (sensitivity 72%, specificity 81%, AUC 0.85) and RF 72% accuracy (sensitivity 74%, specificity 72%, AUC 0.83). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical variables alone. Validation testing by UK Biobank imaging centres demonstrated robustness of our models.

Conclusion: A combination of ECG biomarkers and clinical variables were able to predict LVH defined by CMR. Our findings provide support for the ECG as an inexpensive screening tool to risk stratify patients with LVH as a prelude to advanced imaging.

Keywords: Cardiovascular magnetic resonance imaging; Cardiovascular screening; Electrocardiogram; Left ventricular hypertrophy; Machine learning.

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Conflict of interest statement

Conflicts of interest: S.E.P. provides consultancy to and owns stock of Cardiovascular Imaging Inc., Calgary, Alberta, Canada.

Figures

Graphical Abstract
Graphical Abstract
Created using BioRender.com. AUC: area under the receiver operator curve, LVH: left ventricular hypertrophy.
Figure 1
Figure 1
Flow diagram illustrating the steps involved in UK Biobank participant selection, ECG biomarker extraction and machine learning. Abbreviations: ECG: electrocardiogram, LV: left ventricle, LVH: left ventricular hypertrophy.
Figure 2
Figure 2
Ranking of the top 40 features using Chi-square feature selection.
See this image and copyright information in PMC

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