Phenomapping for Novel Classification of Heart Failure with Preserved Ejection Fraction

By Sanjiv J. Shah, Daniel H. Katz, S. Selvaraj, Michael A. Burke, C. Yancy, M. Gheorghiade, R. Bonow, Chiang-Ching Huang, Rahul C. Deo. 2014

919 citations recorded by the source corpus.

Background— 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 patients with HFpEF and performed detailed clinical, laboratory, ECG, 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 making up a novel classification of HFpEF. All phenomapping analyses were performed by investigators 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 black; 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 (eg, phenogroup 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]). The HFpEF phenogroup classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107). Conclusions— Phenomapping results in a novel classification of HFpEF. Statistical learning algorithms applied to dense phenotypic data may allow improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.

Subfields: Cardiology and Cardiovascular Medicine

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Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “[49] clustered patients with preserved ejection fraction into”
• Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction (2017, positive): “, younger patients with moderate diastolic dysfunction and relatively normal BNP as a distinct HFpEF archetype [7]).”
• Artificial Intelligence in Cardiovascular Imaging (2019, neutral): “(29) Heart failure with preserved ejection fraction Agglomerative Hierarchical clustering and model-based clustering Hierarchical clustering to conceptualize similar and redundant features of phenotypic features.”
• Artificial Intelligence in Precision Cardiovascular Medicine. (2017, background): “(7) created an unsupervised learning model across 46 different variables to identify intrinsic structures within patients with HFpEF; they identified 3 distinct groups.”
• Machine Learning and Decision Support in Critical Care (2016, methodology): “[101] used an iterative approach incorporating singular value decomposition to impute missing data under the assumption that data were MAR.”