Towards Precision in HF Pharmacotherapy

Heart failure (HF) is a disease state with great heterogeneity, which complicates the therapeutic process. Identifying more precise HF phenotypes will allow for the development of more targeted therapies and improvement in patient outcomes. This review explores the future for precision medicine in HF treatment.

Rather than a continuous disease spectrum with a uniform pathogenesis, HF has phenotypes with different underlying pathophysiologic features. The challenge is to establish clinical phenotypic characterizations to direct therapy. Phenomapping, a process of using machine learning algorithms applied to clinical data sets, has been used to identify phenotypically distinct and clinically meaningful HF groups. As powerful technologies extend our knowledge, future analyses may be able to compile more comprehensive phenotypic profiles using genetic, epigenetic, proteomic, and metabolomic measurements. Identifying clinical characterizations of particular HF patients that would be uniquely or disproportionately responsive to a specific treatment would allow for more direct selection of optimal therapy, reduce trial-and-error prescribing, and help avoid adverse drug reactions.