Profiling heart failure with preserved or mildly reduced ejection fraction by cluster analysis.

Background: Significant knowledge gaps remain regarding the heterogeneity of heart failure (HF) phenotypes, particularly among patients with preserved or mildly reduced left ventricular ejection fraction (HFp/mrEF). Our aim was to identify HF subtypes within the HFp/mrEF population.

Methods: K-prototypes clustering algorithm was used to identify different HF phenotypes in a cohort of 2 570 patients diagnosed with HFmrEF or HFpEF. This algorithm employs the k-means algorithm for quantitative variables and k-modes for qualitative variables.

Results: We identified three distinct phenotypic clusters: Cluster A (n = 850, 33.1%), characterized by a predominance of women with low comorbidity burden; Cluster B (n = 830, 32.3%), mainly women with diabetes mellitus and high comorbidity; and Cluster C (n = 890, 34.5%), primarily men with a history of active smoking and respiratory comorbidities. Significant differences were observed in baseline characteristics and one-year mortality rates across the clusters: 18% for Cluster A, 33% for Cluster B, and 26.4% for Cluster C (P < 0.001). Cluster B had the shortest median time to death (90 days), followed by Clusters C (99 days) and A (144 days) (P < 0.001). Stratified Cox regression analysis identified age, cancer, respiratory failure, and laboratory parameters as predictors of mortality.

Conclusion: Cluster analysis identified three distinct phenotypes within the HFp/mrEF population, highlighting significant heterogeneity in clinical profiles and prognostic implications. Women were classified into two distinct phenotypes: low-risk women and diabetic women with high mortality rates, while men had a more uniform profile with a higher prevalence of respiratory disease.