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. 2020 Jan;22(1):148-158.
doi: 10.1002/ejhf.1621. Epub 2019 Oct 21.

Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis

Matthew W Segar  1 , Kershaw V Patel  1 , Colby Ayers  1 , Mujeeb Basit  1 , W H Wilson Tang  2 , Duwayne Willett  1 , Jarett Berry  1 , Justin L Grodin  1 , Ambarish Pandey  1
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Free article

Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis

Matthew W Segar et al. Eur J Heart Fail. 2020 Jan.
Free article

Abstract

Aim: To identify distinct phenotypic subgroups in a highly-dimensional, mixed-data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis.

Methods and results: The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model-based clustering analysis on 61 mixed-data phenotypic variables. Phenogroup 1 had higher burden of co-morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non-cardiac co-morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co-morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all-cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non-echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants.

Conclusions: Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes.

Keywords: Heart failure with preserved ejection fraction; Machine learning; Outcomes; Phenomapping.

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