Clustering analysis based on automated electrocardiographic measurements to identify prognostically distinct phenotypes in patients hospitalized for heart failure: a retrospective cohort study. (4th February 2022)

• Record Type: Journal Article
• Authors: Chan, J S K; Zhou, J; Li, A; Tan, M; Wong, W T; Ciobanu, A; Gkouziouta, A; Letsas, K; Liu, T; Liu, Y; Zhang, Q; Tse, G
• Is Part Of: European heart journal. Volume 43(2022)Supplement 1
• Publication Date: 2022-02-04
• Subjects: Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehab849.044
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Funding Acknowledgements: Type of funding sources: None. Background: Heart failure (HF) is a heterogeneous disease with complex structural and electrophysiological derangements of the heart. Attempts to classify HF from the electrophysiological perspective are lacking. Purpose: To use electrocardiographic (ECG) data for phenotypic classification of patients with HF. Methods: In this retrospective cohort study, all adult patients hospitalized for HF during 2010-2016 at a tertiary center were included. Automated measurements of the first ECG obtained during the index admission were recorded. K-means clustering using premorbid conditions and selected ECG measurements were used to classify the cohort into four mutually exclusive clusters. The primary (all-cause and cardiovascular mortality) and secondary (ventricular arrhythmia (VA)) outcomes were compared between clusters using Cox regression analysis. Results: In total, 2849 patients (1363 males, age 75.1 ± 13.4 years) were included. Over a mean follow-up period of 5.37 ± 4.10 years, all-cause and cardiovascular mortality occurred in 2071 (72.7%) and 600 (21.1%) patients respectively, while VA occurred in 110 patients (3.9%). Cluster 1 was characterised by a low heart rate and low ventricular activation time (VAT). Cluster 2 was characterised by old age, low absolute QRS area, and high QTc and QT dispersion. Cluster 3 was characterised by young age, and left ventricular hypertrophy (LVH), and few had history of VA. Cluster 4 was characterised by wide QRS, hypertension, ischaemic heart disease, high VAT, and high absolute T wave area. Cluster 4 had the highest and cluster 1 the lowest risks of all-cause (hazard ratio (HR) 2.96 [1.09, 1.50], p = 0.003; Figure A) and cardiovascular mortality (HR 2.90 [1.15, 2.11], p = 0.004; Figure B). Meanwhile, cluster 2 had the highest risk of VA (HR 2.23 [1.09, 3.85], p = 0.025; Figure C) while clusters 1 and 3 similarly had the lowest risks. Conclusion: HF presents with clinically and electrophysiologically distinct phenotypes. Clustering analysis is useful in identifying HF phenotypes which are prognostically significant.