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Volume 31, Nº 6, November and December 2018


DOI: http://www.dx.doi.org/10.5935/2359-4802.20180047


Phenotype Mapping of Heart Failure with Preserved Ejection Fraction

Evandro Tinoco Mesquita

Debora Carvalho Grion

Miguel Camargo Kubrusly

Bernardo Barcelos Fernandes

Fumagalli Silva

Érico Araújo Reis Santos


failure with preserved ejection fraction (HFPEF) has become the main phenotypic model of heart failure (HF) in community and referral patients in Brazil and in the world. Despite advances in the development of new drugs for HF treatment, there has been no significant improvement in mortality of this condition. According to many studies, this can be explained by the heterogeneous nature of HF physiopathology, whose basic mechanisms may result in different clinical presentations, culminating in the emerging of different phenogroups in this syndrome. In this context, phenotype mapping of HFPEF has emerged as a possible solution, since it enables the development of clinical trials that establish specific therapeutic strategies for each phenotypic profile. New technologies in the field of artificial intelligence have enabled the assessment of a large volume of data and infer intrinsic patterns and different outcomes. Thereby, it is possible to obtain mutually exclusive categories of HFPEF, with a phenotype mapping of the syndrome and grouping of patients according to their phenotypic features. Besides, other diseases can have the same clinical phenotype but different pathophysiological basis, the so called “phenocopies”. These tools enable the analysis and categorization of the wide spectrum of heart failure, contributing to solve the dilemmas of the treatment of this syndrome.

Keywords: Heart Failure / physiopathology; Stroke Volume; Phenotype; Machine Learning; Artificial Intelligence.