Differences in Repolarization Heterogeneity Among Heart Failure With Preserved Ejection Fraction Phenotypic Subgroups

Suzanne K. Oskouie, Stuart B. Prenner, Sanjiv J. Shah, Andrew J. Sauer*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Heart failure with preserved ejection fraction (HFpEF) is a highly heterogeneous syndrome associated with multiple medical comorbidities and pathophysiologic pathways or phenotypes. We recently developed a phenomapping method combining deep phenotyping with machine learning analysis to classify HFpEF patients into 3 clinically distinct phenotypic subgroups (phenogroups) with different clinical outcomes. Phenogroup #1 was younger with lower B-type natriuretic peptide levels, phenogroup #2 had the highest prevalence of obesity and diabetes mellitus, and phenogroup #3 was the oldest with the most factors for chronic kidney disease, the most dysfunctional myocardial mechanics, and the highest adverse outcomes. The pathophysiological differences between these phenogroups, however, remain incompletely described. We sought to evaluate whether these 3 groups differ on the basis of repolarization heterogeneity, which has previously been linked to adverse outcomes in HFpEF. The T-peak to T-end (TpTe) interval, a well-validated index of repolarization heterogeneity, was measured by 2 readers blinded to each other and all other clinical data on the electrocardiograms of 201 HFpEF patients enrolled in a systematic observational study. TpTe duration was associated with higher B-type natriuretic peptide level (p = 0.006), increased QRS-T angle (p = 0.008), and lower septal e′ velocity (p = 0.007). TpTe duration was greatest in phenogroup #3 (100.4 ± 24.5 ms) compared with phenogroups #1 (91.2 ± 17.3 ms) and #2 (90.2 ± 17.0 ms) (p = 0.0098). On multivariable analyses, increased TpTe was independently associated with the high-risk phenogroup #3 classification. In conclusion, repolarization heterogeneity is a marker of a specific subset of HFpEF patients identified using unsupervised machine learning analysis and therefore may be a key pathophysiologic marker in this subset of HFpEF patients.

Original languageEnglish (US)
Pages (from-to)601-606
Number of pages6
JournalAmerican Journal of Cardiology
Volume120
Issue number4
DOIs
StatePublished - Aug 15 2017

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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