Abstract
Heart failure with preserved ejection fraction (HFpEF) is a complex, heterogeneous syndrome in need of improved classification given its high morbidity and mortality and few effective treatment options. HFpEF represents an ideal setting to examine the utility and feasibility of a precision medicine approach. This article (based on the 20th annual Feigenbaum Lecture, presented at the 2019 American Society of Echocardiography Scientific Sessions) describes the utility of echocardiography as a "digital biopsy" and how deep quantitative echocardiographic phenotyping, coupled with machine learning, can be used to identify novel HFpEF phenotypes. The cellular and ultrastructural basis of abnormal speckle-tracking echocardiography- (STE-) based measurements of cardiac mechanics can provide a window into cardiomyocyte calcium homeostasis. STE-based measurements of longitudinal strain can thus inform the extent of myocardial involvement in patients with HFpEF, which may help to determine responsiveness to cardiac-specific HF medications. However, classifying the complex, systemic, multiorgan nature of HFpEF appropriately likely requires more advanced methods. Using unsupervised machine learning, HFpEF can be classified into three distinct phenogroups with differing clinical and echocardiographic characteristics and outcomes: (1) natriuretic peptide deficiency syndrome; (2) extreme cardiometabolic syndrome; and (3) right ventricle-cardio-abdomino-renal syndrome. Each can be probed to determine their biological basis. The goal of improved classification of HFpEF is to match the right patient with the right treatment, with the hope of improving the track record of HFpEF clinical trials. This article emphasizes the central role of echocardiography in advancing precision medicine and illustrates the integration of basic, translational, clinical, and population research in echocardiography with the goal of better understanding the pathobiology of a complex cardiovascular syndrome.