Background: End-stage (Stage D) heart failure with preserved ejection fraction (HFpEF) is a poorly characterized syndrome that has heterogeneous underlying pathophysiology. A better characterization of the various clinical profiles of Stage D HFpEF is needed.
Method: 1066 patients with Stage D HFpEF were selected from National Readmission Database. A Bayesian clustering algorithm based on a Dirichlet process mixture model was implemented. Cox proportional hazard regression model was used to relate the risk of in-hospital mortality with each identified clinical cluster.
Result: 4 distinct clinical clusters were recognized. Group 1 had a higher prevalence of obesity (84.5%) and sleep disorders (62.0%). Group 2 had a higher prevalence of diabetes mellitus (92%), chronic kidney disease (98.3%), anemia (72.6%), and coronary artery disease (59.0%). Group 3 had a higher prevalence of advanced age (82.1%), hypothyroidism (28.9%), dementia (17.0%), atrial fibrillation (63.8%) and valvular disease (30.5%) and Group 4 had a higher prevalence of liver disease (44.5%), right-sided HF (20.2%) and amyloidosis (4.5%). During 2019, 193 (18.1%) in-hospital mortality events occurred. Considering Group 1 (with mortality rate of 4.1%) as a reference, the hazard ratio of in-hospital mortality was 5.4 [95% confidence interval (CI): 2.2-13.6] for Group 2, 6.4 (95% CI: 2.6-15.8) for Group 3 and 9.1 (95% CI: 3.5-23.8) for Group 4.
Conclusion: End-stage HFpEF presents with different clinical profiles with varied upstream causes. This may help provide evidence toward the development of targeted therapies.
Keywords: HFpEF; Machine learning; Phenotype; Precision medicine.
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