Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. We aimed to derive HFpEF phenotype-based groups based on clinical features using machine learning, and to compare clinical characteristics, outcomes and treatment response across the phenogroups.
Methods: We applied model-based clustering to 11 clinical and laboratory variables collected in 970 HFpEF patients. An additional 290 HFpEF patients was enrolled as a validation cohort. During 5-year follow-up, all-cause mortality was used as the primary endpoints, and composite endpoints (all-cause mortality or HF hospitalization) were set as the secondary endpoint.
Results: We identified three phenogroups, for which significant differences in the age and gender, the prevalence of concomitant ischaemic heart disease, atrial fibrillation and type 2 diabetes mellitus, the burden of B-type natriuretic peptide level and HF symptoms. Patients with phenogroup 3 had higher all-cause mortality or composite endpoints, whereas patients in phenogroup 1 had less adverse events after 5-year follow-up. Moreover, it was indicated that beta-blockers or angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker (ACEI/ARB) use was associated with a lower risk of all-cause mortality or composite endpoints in phenogroup 3, instead of the other phenogroups. This HFpEF phenogroup classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort.
Conclusion: Machine-learning based clustering strategy is used to identify three distinct phenogroups of HFpEF that are characterized by significant differences in comorbidity burden, underlying cardiac abnormalities, and long-term prognosis. Beta-blockers or ACEI/ARB therapy is associated with a lower risk of adverse events in specific phenogroup.
Keywords: Heart failure with preserved ejection fraction; Phenogroup; Prognosis; Treatment response.
Copyright © 2020 Elsevier B.V. All rights reserved.