Unsupervised cluster analysis of patients with recovered left ventricular ejection fraction identifies unique clinical phenotypes

Andrew Perry, Francis Loh, Luigi Adamo, Kathleen W. Zhang, Elena Deych, Randi Foraker, Douglas L. Mann

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background Patients with heart failure (HF) with recovered ejection fraction (HFrecEF) are a recently identified cohort that are phenotypically and biologically different from HFrEF and HFpEF patients. Whether there are unique phenotypes among HFrecEF patients is not known. Methods We studied all patients at a large medical center, who had an improvement in LVEF from ≤ 35% to ≥ 50% (LVrecEF) between January 1, 2005 and December 31, 2013. We identified a set of 11 clinical variables and then performed unsupervised clustering analyses to identify unique clinical phenotypes among patients with LVrecEF, followed by a Kaplan-Meier analysis to identify differences in survival and the proportion of LVrecEF patients who maintained an LVEF ≥ 50% during the study period. Results We identified 889 patients with LVrecEF who clustered into 7 unique phenotypes ranging in size from 37 to 420 patients. Kaplan-Meier analysis demonstrated significant differences in mortality across clusters (logrank p<0.0001), with survival ranging from 14% to 87% at 1000 days, as well as significant differences in the proportion of LVrecEF patients who maintained an LVEF ≥ 50%. Conclusion There is significant clinical heterogeneity among patients with LVrecEF. Clinical outcomes are distinct across phenotype clusters as defined by clinical cardiac characteristics and comorbidities. Clustering algorithms may identify patients who are at high risk for recurrent HF, and thus be useful for guiding treatment strategies for patients with LVrecEF.

Original languageEnglish (US)
Article numbere0248317
JournalPloS one
Volume16
Issue number3 March
DOIs
StatePublished - Mar 2021
Externally publishedYes

ASJC Scopus subject areas

  • General

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