November 14, 2014Circulation1,132 citationsOpen Access
Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction
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Structured PICO
Does phenomapping using statistical learning algorithms improve the classification of HFpEF into therapeutically homogeneous subclasses?
P
Population
Patients with Heart Failure with Preserved Ejection Fraction (HFpEF)
I
Intervention
Phenomapping (statistical learning algorithms applied to dense phenotypic data)
O
Outcome
Classification of heterogeneous clinical syndromes into therapeutically homogeneous patient subclasses
Phenomapping using statistical learning algorithms offers a novel approach to classify the heterogeneous HFpEF syndrome into potentially therapeutically homogeneous subclasses.
Abstract
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.