Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction
Will clustering analysis using dense phenotypic data (“phenomapping”) identify phenotypically distinct heart failure with preserved ejection fraction (HFpEF) categories?
The study cohort was comprised of 397 HFpEF patients who underwent detailed clinical, laboratory, electrocardiographic, and echocardiographic phenotyping. The investigators utilized several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering to define and characterize mutually exclusive groups comprising a novel classification of HFpEF. They performed phenomapping analyses blinded to clinical outcomes, and Cox regression to demonstrate the clinical validity of phenomapping.
The investigators found that the mean age of the cohort was 65 ± 12 years, 62% were female, 39% were African-American, comorbidities were common, and all patients met published criteria for the diagnosis of HFpEF. They found that phenomapping analysis classified study participants into three distinct groups that varied markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (e.g., pheno-group #3 had an increased risk of HF hospitalization [hazard ratio, 4.2; 95% confidence interval, 2.0-9.1] even after adjustment for traditional risk factors [p < 0.001]). Using a prospective validation cohort (n = 107), the study investigators were able to successfully replicate the ability of the HFpEF pheno-group classification to stratify risk.
The authors concluded that phenomapping results in novel classification of HFpEF.
This is a seminal study pertaining to diastolic HF because it takes into consideration important comorbidities such as renal function and right ventricular function to classify diastolic HF. The next step would be to validate these findings in a similar cohort to confirm the strength of this novel classification. This classification should also be of great value when designing future clinical trials of diastolic HF.
Keywords: Comorbidity, Diastole, Echocardiography, Electrocardiography, Heart Failure, Hemodynamics, Hospitalization, Risk Factors, Ventricular Function
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