Differences in Cardiac Mechanics and Exercise Physiology Among Heart Failure With Preserved Ejection Fraction Phenomapping Subgroups

Unsupervised machine learning (phenomapping) has been used successfully to identify novel subgroups (phenogroups) of heart failure with preserved ejection fraction (HFpEF). However, further investigation of pathophysiological differences between HFpEF phe-nogroups is necessary to help determine potential treatment options. We performed speckle-tracking echocardiography and cardiopulmonary exercise testing (CPET) in 301 and 150 patients with HFpEF, respectively, as part of a prospective phenomapping study (median age 65 [25th to 75th percentile 56 to 73] years, 39% Black individuals, 65% female). Linear regression was used to compare strain and CPET parameters by phe-nogroup. All indicies of cardiac mechanics except for left ventricular global circumferen-tial strain worsened in a stepwise fashion from phenogroups 1 to 3 after adjustment for demographic and clinical factors. After further adjustment for conventional echocardio-graphic parameters, phenogroup 3 had the worst left ventricular global longitudinal, right ventricular free wall, and left atrial booster and reservoir strain. On CPET, phenogroup 2 had the lowest exercise time and absolute peak oxygen consumption (VO2), driven primar-ily by obesity, whereas phenogroup 3 achieved the lowest workload, relative peak oxygen consumption (VO2), and heart rate reserve on multivariable-adjusted analyses. In conclu-sion, HFpEF phenogroups identified by unsupervised machine learning analysis differ in the indicies of cardiac mechanics and exercise physiology. (c) 2023 Elsevier Inc. All rights reserved. (Am J Cardiol 2023;193:102-110)