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Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction JOURNAL OF CARDIOVASCULAR TRANSLATIONAL RESEARCH Katz, D. H., Deo, R. C., Aguilar, F. G., Selvaraj, S., Martinez, E. E., Beussink-Nelson, L., Kim, K. A., Peng, J., Irvin, M. R., Tiwari, H., Rao, D. C., Arnett, D. K., Shah, S. J. 2017; 10 (3): 275-284

Abstract

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?

View details for DOI 10.1007/s12265-017-9739-z

View details for Web of Science ID 000405852400005

View details for PubMedID 28258421