Phenotyping left ventricular systolic dysfunction in asymptomatic individuals for improved risk stratification

Author:

Rauseo Elisa12ORCID,Abdulkareem Musa123,Khan Abbas45,Cooper Jackie1,Lee Aaron M1,Aung Nay12ORCID,Slabaugh Gregory G456,Petersen Steffen E1236ORCID

Affiliation:

1. William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London , Charterhouse Square, London EC1M 6BQ , UK

2. Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust , West Smithfield, London EC1A 7BE , UK

3. Health Data Research UK , 215 Euston Rd, London NW1 2BE , UK

4. School of Electronic Engineering and Computer Science, Queen Mary University of London , UK

5. Digital Environment Research Institute, Queen Mary University of London , UK

6. Alan Turing Institute , British Library, 96 Euston Rd, London NW1 2DB , UK

Abstract

Abstract Aims Left ventricular systolic dysfunction (LSVD) is a heterogeneous condition with several factors influencing prognosis. Better phenotyping of asymptomatic individuals can inform preventative strategies. This study aims to explore the clinical phenotypes of LVSD in initially asymptomatic subjects and their association with clinical outcomes and cardiovascular abnormalities through multi-dimensional data clustering. Methods and results Clustering analysis was performed on 60 clinically available variables from 1563 UK Biobank participants without pre-existing heart failure (HF) and with left ventricular ejection fraction (LVEF) < 50% on cardiovascular magnetic resonance (CMR) assessment. Risks of developing HF, other cardiovascular events, death, and a composite of major adverse cardiovascular events (MACE) associated with clusters were investigated. Cardiovascular imaging characteristics, not included in the clustering analysis, were also evaluated. Three distinct clusters were identified, differing considerably in lifestyle habits, cardiovascular risk factors, electrocardiographic parameters, and cardiometabolic profiles. A stepwise increase in risk profile was observed from Cluster 1 to Cluster 3, independent of traditional risk factors and LVEF. Compared with Cluster 1, the lowest risk subset, the risk of MACE ranged from 1.42 [95% confidence interval (CI): 1.03–1.96; P < 0.05] for Cluster 2 to 1.72 (95% CI: 1.36–2.35; P < 0.001) for Cluster 3. Cluster 3, the highest risk profile, had features of adverse cardiovascular imaging with the greatest LV re-modelling, myocardial dysfunction, and decrease in arterial compliance. Conclusions Clustering of clinical variables identified three distinct risk profiles and clinical trajectories of LVSD amongst initially asymptomatic subjects. Improved characterization may facilitate tailored interventions based on the LVSD sub-type and improve clinical outcomes.

Funder

London Medical Imaging and Artificial Intelligence Centre

UK Research and Innovation

National Institute for Health Research

Biomedical Research Centre

British Heart Foundation

Academy of Medical Sciences

mini-Centre for Doctoral Training

Faculty of Science and Engineering

Queen Mary University of London

Publisher

Oxford University Press (OUP)

Subject

Cardiology and Cardiovascular Medicine,Radiology, Nuclear Medicine and imaging,General Medicine

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