Phenomapping Heart Failure with Preserved Ejection Fraction Using Machine Learning Cluster Analysis : Prognostic and Therapeutic Implications - 26/05/21

Doi : 10.1016/j.hfc.2021.02.010

Elena Galli, MD, PhD ^a, Corentin Bourg, MD ^a, Wojciech Kosmala, MD, PhD ^b, Emmanuel Oger, MD, PhD ^c, Erwan Donal, MD, PhD ^a,^⁎
^a University of Rennes, CHU Rennes, INSERM, LTSI–UMR 1099, Rennes F-35000, France
^b Cardiology Department, Wroclaw Medical University, Wroclaw, Poland
^c University of Rennes, EA 7449 REPERES [Pharmacoepidemiology and Health Services Research], Rennes, France

^∗Corresponding author. Service de Cardiologie, Hôpital Pontchaillou, CHU Rennes, Rennes F-35033, France.Service de CardiologieHôpital PontchaillouCHU RennesRennesF-35033France

Résumé

Heart failure with preserved ejection fraction (HFpEF) is characterized by a high rate of hospitalization and mortality (up to 84% at 5 years), which are similar to those observed for heart failure with reduced ejection fraction (HFrEF). These epidemiologic data claim for the development of specific and innovative therapies to reduce the burden of morbidity and mortality associated with this disease. Compared with HFrEF, which is due to a primary myocardial damage (eg ischemia, cardiomyopathies, toxicity), a heterogeneous etiologic background characterizes HFpEF. The authors discuss these phenotypes and specificities for defining therapeutic strategies that could be proposed according to phenotypes.

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Keywords : Heart failure with preserved ejection fraction, Machine learning, Phenomapping, Prognosis, Precision medicine, Targeted treatment