Journal
JOURNAL OF CARDIOVASCULAR TRANSLATIONAL RESEARCH
Volume 10, Issue 3, Pages 275-284Publisher
SPRINGER
DOI: 10.1007/s12265-017-9739-z
Keywords
Hypertension; Machine learning; Speckle-tracking echocardiography; Cardiacmechanics; Heart failure with preserved ejection fraction
Funding
- National Institutes of Health (NIH) [R01 HL107577]
- National Institutes of Health [R01 HL55673]
- National Heart, Lung, and Blood Institute [HL54471, HL54472, HL54473, HL54495, HL54496, HL54497, HL54509, HL54515]
- NIH [HL127028]
- American Heart Association [16SFRN28780016, 15CVGPSD27260148]
- Alpha Omega Alpha Carolyn L. Kuckein Research Fellowship
Ask authors/readers for more resources
We sought to evaluate whether unbiased machine learning of dense phenotypic data (phenomapping) could identify distinct hypertension subgroups that are associated with the myocardial substrate (i.e., abnormal cardiac mechanics) for heart failure with preserved ejection fraction (HFpEF). In the HyperGEN study, a population- and family-based study of hypertension, we studied 1273 hypertensive patients utilizing clinical, laboratory, and conventional echocardiographic phenotyping of the study participants. We used machine learning analysis of 47 continuous phenotypic variables to identify mutually exclusive groups constituting a novel classification of hypertension. The phenomapping analysis classified study participants into 2 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, and indices of cardiac mechanics (e.g., phenogroup #2 had a decreased absolute longitudinal strain [12.8 +/- 4.1 vs. 14.6 +/- 3.5%] even after adjustment for traditional comorbidities [p < 0.001]). The 2 hypertension phenogroups may represent distinct subtypes that may benefit from targeted therapies for the prevention of HFpEF.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available