Aims: Approximately 50% of patients with heart failure have preserved (≥50%) ejection fraction (HFpEF). Improved understanding of the phenotypic heterogeneity of HFpEF might facilitate development of targeted therapies and interventions.
Methods: This retrospective study characterized a cohort of patients with HFpEF based on similar clinical profiles and evaluated 1-year heart failure related hospitalization. Enrolment, medical and pharmacy data were used to identify patients newly diagnosed with heart failure enrolled in a Medicare Advantage Prescription Drug or commercial healthcare plan. To identify only those patients with HFpEF, we used natural language processing techniques of ejection fraction values abstracted from a linked free-text clinical notes data source. The study population comprised 1515 patients newly identified with HFpEF between 1 January 2011 and 31 December 2015.
Results: Using unsupervised machine learning, we identified three distinguishable patient clusters representing different phenotypes: cluster-1 patients had the lowest prevalence of heart failure comorbidities and highest mean age; cluster-2 patients had higher prevalence of metabolic syndrome and pulmonary disease, despite younger mean age; and cluster-3 patients had higher prevalence of cardiac arrhythmia and renal disease. Cluster-3 had the highest 1-year heart failure related hospitalization rates. Within-cluster analysis, prior use of diuretics (cluster-1 and cluster-2) and age (cluster-2 and cluster-3) was associated with 1-year heart failure related hospitalization. Combination therapy was associated with decreased 1-year heart failure related hospitalization in cluster-1.
Conclusion: This study demonstrated that clustering can be used to characterize subgroups of patients with newly identified HFpEF, assess differences in heart failure related hospitalization rates at 1 year and suggest patient subtypes may respond differently to treatments or interventions.