PREDICTING THE PATTERN OF ANATOMICAL LEFT VENTRICULAR HYPERTROPHY USING ELECTROPHYSIOLOGICAL BIOMARKERS FROM THE ECG QRS COMPLEX IN HYPERTENSIVE INDIVIDUALS FROM UK BIOBANK. (June 2022)

• Record Type: Journal Article
• Authors: Naderi, Hafiz; Ramírez, Julia; Van Duijvenboden, Stefan; Aung, Nay; Fung, Kenneth; Young, William; Petersen, Steffen; Munroe, Patricia
• Is Part Of: Journal of hypertension. Volume 40(2022)Supplement 1
• Page Start: e5
• Publication Date: 2022-06
• Subjects: Hypertension -- Periodicals; Hypertension -- Periodicals; 616.132005
• DOI: 10.1097/01.hjh.0000835328.40233.c1
• Languages: English
• ISSNs: 1473-5598

Abstract

Abstract : Objective: Hypertension has progressive end-organ effects such as left ventricular hypertrophy (LVH), an established independent predictor of cardiovascular morbidity and mortality. Four distinct LVH phenotypes with varying prognostic implications have been described using cardiac magnetic resonance (CMR) LV mass to volume ratio; normal LV, LV remodelling, eccentric LVH and concentric LVH. Current electrocardiogram (ECG) criteria can detect LVH but their ability to differentiate between LVH phenotypes is unclear. Design and method: As a preliminary analysis, 5, 065 participants in the UK Biobank were categorised into the 4 CMR-defined LVH phenotypes. The 12 lead ECG of each participant was analysed using MATLAB to derive ECG biomarkers known to have an association with LVH (QRS duration, QRS ascending and descending slopes, Q wave, R wave, S wave and QRS wave amplitudes). ANOVA compared differences in ECG biomarkers across LVH phenotypes. Univariate logistic regression was used to test association of each ECG biomarker with the LVH phenotypes. ECG biomarkers with a significant association (P < 0.05) were included in the multinomial logistic regression model. Results: In combination, the set of 7 ECG biomarkers detected a difference (P < 2.2e-16) across the LVH phenotypes. Using logistic regression, QRS duration, S wave and QRS waves amplitude were able to differentiate between the 4 LVH phenotypes. With normotensive group as a reference, there was a significant association between S wave amplitude and normal LV (Odds Ratio 1.80, P 1.46e-03), QRS wave amplitude and normal LV (OR 2.23, P 9.01e-05), global QRS duration with eccentric LVH (OR 1.05, P 4.45e-06) and concentric LVH (OR 1.03, P 1.02e-05). Conclusions: We identified a set of ECG markers from the QRS complex that can differentiate between the 4 LVH phenotypes, providing support for the ECG to identify subclinical LVH identified using CMR. We are currently extending our analyses to the full 45, 000 UK Biobank imaging cohort for validation.