Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome requiring improved phenotypic classification. Previous studies have identified subphenotypes of HFpEF, but the lack of exercise assessment is a major limitation. This study sought to identify distinct pathophysiologic clusters of HFpEF based on clinical characteristics, and resting and exercise assessments.
Methods: A total of 265 patients with HFpEF underwent ergometry exercise stress echocardiography with simultaneous expired gas analysis. Cluster analysis was performed by the K-prototype method with 21 variables (10 clinical and resting echocardiographic variables and 11 exercise echocardiographic parameters). Pathophysiological features, exercise tolerance, and prognosis were compared among phenogroups.
Results: Three distinct phenogroups were identified: Phenogroup 1 (n=112, 42%) was characterized by preserved biventricular systolic reserve and cardiac output augmentation. Phenogroup 2 (n=58, 22%) was characterized by a high prevalence of atrial fibrillation, increased pulmonary arterial and right atrial pressures, depressed RV systolic functional reserve, and impaired right ventricular-pulmonary artery coupling during exercise. Phenogroup 3 (n=95, 36%) was characterized by the smallest body mass index, ventricular and vascular stiffening, impaired LV diastolic reserve, and worse exercise capacity. Phenogroups 2 and 3 had higher rates of composite outcomes of all-cause mortality or HF events than phenogroup 1 (log-rank p=0.02).
Conclusion: Exercise echocardiography-based cluster analysis identified three distinct phenogroups of HFpEF, with unique exercise pathophysiological features, exercise capacity, and clinical outcomes.
Keywords: exercise; heart failure with preserved ejection fraction; machine learning; phenotyping; stress echocardiography.
Copyright © 2024. Published by Elsevier Inc.