Why the study?
Does phenomapping using statistical learning algorithms improve the classification of HFpEF into therapeutically homogeneous subclasses?
Does phenomapping using statistical learning algorithms improve the classification of HFpEF into therapeutically homogeneous subclasses?
Phenomapping using statistical learning algorithms offers a novel approach to classify the heterogeneous HFpEF syndrome into potentially therapeutically homogeneous subclasses.
May refine HFpEF subclassification for trials; leaves open therapeutic utility pending prospective validation.
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.
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Shah et al. (2014) studied this question.
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