In International journal of cardiology ; h5-index 68.0
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 : 066 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.
Mohebi Reza, Liu Yuxi, Murphy Sean P, Gaggin Hanna K, Januzzi James L
2023-Mar-02
HFpEF, Machine learning, Phenotype, Precision medicine
