Minimal subphenotyping model for acute heart failure with preserved ejection fraction

Abstract

Aims

Application of the latent class analysis to acute heart failure with preserved ejection fraction (HFpEF) showed that the heterogeneous acute HFpEF patients can be classified into four distinct phenotypes with different clinical outcomes. This model‐based clustering required a total of 32 variables to be included. However, this large number of variables will impair the clinical application of this classification algorithm. This study aimed to identify the minimal number of variables for the development of optimal subphenotyping model.

Methods and results

This study is a post hoc analysis of the PURSUIT‐HFpEF study (N = 1095), a prospective, multi‐referral centre, observational study of acute HFpEF [UMIN000021831]. We previously applied the latent class analysis to the PURSUIT‐HFpEF dataset and established the full 32‐variable model for subphenotyping. In this study, we used the Cohen's kappa statistic to investigate the minimal number of discriminatory variables needed to accurately classify the phenogroups in comparison with the full 32‐variable model. Cohen's kappa statistic of the top‐X number of discriminatory variables compared with the full 32‐variable derivation model showed that the models with ≥16 discriminatory variables showed kappa value of >0.8, suggesting that the minimal number of discriminatory variables for the optimal phenotyping model was 16. The 16‐variable model consists of C‐reactive protein, creatinine, gamma‐glutamyl transferase, brain natriuretic peptide, white blood cells, systolic blood pressure, fasting blood sugar, triglyceride, clinical scenario classification, infection‐triggered acute decompensated HF, estimated glomerular filtration rate, platelets, neutrophils, GWTG‐HF (Get With The Guidelines‐Heart Failure) risk score, chronic kidney disease, and CONUT (Controlling Nutritional Status) score. Characteristics and clinical outcomes of the four phenotypes subclassified by the minimal 16‐variable model were consistent with those by the full 32‐variable model. The four phenotypes were labelled based on their characteristics as ‘rhythm trouble’, ‘ventricular‐arterial uncoupling’, ‘low output and systemic congestion’, and ‘systemic failure’, respectively.

Conclusions

The phenotyping model with top 16 variables showed almost perfect agreement with the full 32‐variable model. The minimal model may enhance the future clinical application of this clustering algorithm.