Clinical Implications of Chronic Heart Failure Phenotypes Defined by Cluster Analysis

BACKGROUND Classification of chronic heart failure (HF) is based on criteria that may not adequately capture disease heterogeneity. Improved phenotyping may help inform research and therapeutic strategies. OBJECTIVE This study used cluster analysis to explore clinical phenotypes in chronic HF patients. METHODS A cluster analysis was performed on 45 baseline clinical variables from 1,619 participants in HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training), evaluating exercise training versus usual care in chronic systolic HF. Association between identified clusters and clinical outcomes was performed using Cox proportional hazards modeling. Differential associations between clinical outcomes and exercise testing were examined using interaction testing. RESULTS Ranging in size from 248 to 773, four clusters were identified whose patients varied considerably along measures of age, sex, race, symptoms, comorbidities, HF etiology, socioeconomic status, quality of life, cardiopulmonary exercise testing parameters, and biomarker levels. Differential associations were observed for hospitalization and mortality risks between and within clusters. To illustrate, compared with cluster 1, risk of all-cause mortality/all-cause hospitalization ranged from 0.65 (0.54 to 0.78) for cluster 4 to 1.02 (0.87 to 1.19) for cluster 3. However, for all-cause mortality, cluster 3 had disproportionately lower risk 0.61 (0.44 to 0.86). Evidence suggested differential effects of exercise treatment on changes in peak VO2 and clinical outcomes between clusters (p for interaction <0.04). CONCLUSIONS Cluster analysis of clinical variables identified 4 distinct phenotypes of chronic HF. Our findings underscore the high degree of disease heterogeneity that exists within chronic HF patients and a need for improved phenotyping.