Diagnostic and prognostic value of ECG-predicted hypertension mediated left ventricular hypertrophy using machine learning

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Abstract Background Hypertension is the commonest cause of left ventricular hypertrophy (LVH). Using cardiac magnetic resonance (CMR) imaging, four distinct hypertension mediated LVH phenotypes have been reported: normal LV, LV remodelling, eccentric LVH and concentric LVH. Early detection of hypertensives with LVH can enable timely initiation of therapy however a CMR based screening strategy is costly, particularly in low resource settings. The electrocardiogram (ECG) is an inexpensive screening tool and can detect LVH but its capacity to discriminate between the 4 phenotypes is unknown. Purpose To classify hypertension mediated LVH phenotypes from the ECG using machine learning (ML) and test for associations of ECG predicted LVH phenotypes with incident cardiovascular outcomes. Methods ECG markers with a known physiological association with LVH (including QRS markers, Sokolow-Lyon, and Cornell) were extracted from the 12-lead ECG of 20,439 hypertensive participants in UK Biobank. Three classification models, logistic regression, support vector machine (SVM) and random forest, were trained in 80% of the participants using extracted ECG markers and clinical variables. The remaining 20% individuals were included in the test set for performance measurement. External validation was sought in 877 hypertensives from the Study of Health in Pomerania (SHIP). Subsequently, we tested for associations between the four ECG-predicted LVH phenotypes and incident major adverse cardiovascular events (MACE) and heart failure using Cox proportional hazard regression in the UK Biobank test set with median follow-up of 4.2 years. Results Among UK Biobank participants (mean age ± standard deviation 65 ± 7.4 years), 23,190 had normal LV, 894 LV remodelling, 218 eccentric and 103 concentric LVH. Classification performance of the three models was comparable, but SVM showed consistent results (accuracy 0.79, sensitivity 0.59, specificity 0.87, AUC 0.69) and superior prediction of eccentric LVH (AUC 0.86). In SHIP (55 ± 12.7 years), 704 had normal LV, 134 LV remodelling, 12 eccentric and 24 concentric LVH. In the external validation performed in SHIP, SVM had accuracy of 0.75 (sensitivity 0.51, specificity 0.85, AUC 0.65), with superior prediction of eccentric LVH (AUC 0.76), as shown in Figure 1. Ventricular rate and QRS amplitude (P<0.001) were the features most strongly associated with LVH. In the test set of UK Biobank, setting normal LV as the reference group, the ECG predicted eccentric LVH group was associated with heart failure (Hazard Ratio 3.42, confidence interval 1.06-9.86), there were no associations with MACE (Figure 2). Conclusions ECG-based ML classifiers were able to discriminate the four hypertension mediated LVH phenotypes with external validation demonstrating robustness. This automated approach enhances capabilities of non-specialists and potentially represents an accessible screening strategy for the early detection of hypertensives with LVH.

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