Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction

Abstract

Introduction Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (“phenomapping”) could identify phenotypically distinct HFpEF categories. Methods and Results We prospectively studied 397 HFpEF patients and performed detailed clinical, laboratory, electrocardiographic, and echocardiographic phenotyping of the study participants. We used 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. All phenomapping analyses were performed blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65±12 years, 62% were female, 39% were African-American, and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed 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% CI 2.0–9.1] even after adjustment for traditional risk factors [P<0.001]). The HFpEF pheno-group classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107). Conclusions Phenomapping results in novel classification of HFpEF. Statistical learning algorithms, applied to dense phenotypic data, may allow for improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.

“Malignant” Left Ventricular Hypertrophy Identifies Subjects at High Risk for Progression to Asymptomatic Left Ventricular Dysfunction, Heart Failure, and Death: MESA (Multi‐Ethnic Study of Atherosclerosis)

“…A prospective study is needed to determine whether the same medical therapies (renin‐angiotensin aldosterone system inhibitors and beta‐blockers) that have efficacy for reducing progression to symptomatic HF in patients with asymptomatic LV systolic dysfunction would also have comparable efficacy in patients with malignant LVH. Recently, it has been suggested that it is possible to subgroup HFpEF into different phenotypes using machine learning or other algorithms 23, 24. It remains to be determined whether the malignant LVH phenotype may be also able to identify specific patient phenotypes that would be at greater risk to progress to HFpEF among the overall heterogeneous HFpEF cohort.…”

Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients

“…In medicine, these methodologies can use a data‐driven approach to re‐examine phenotyping of complex diseases such as HF 11. Recently, these methods identified distinct phenotypes of HF with reduced and preserved ejection fraction (HFrEF and HFpEF) among 1619 patients with HFrEF enrolled in the HF‐ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) clinical trial and 397 patients seen at the HF clinic at outpatient clinic of the Northwestern University HFpEF Program 12, 13…”

Low NT‐proBNP levels in overweight and obese patients do not rule out a diagnosis of heart failure with preserved ejection fraction

“…The recently updated 2016 European Society of Cardiology (ESC) guidelines mandate an elevated brain natriuretic peptide (BNP) or N‐terminal pro‐BNP (NT‐proBNP) to diagnose HF, departing from prior recommendations 1, 2, 3, 4, 5…”