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.

中文翻译：

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第20届Feigenbaum讲座：精准医学的超声心动图-数字活检以解构生物学。

保留射血分数（HFpEF）的心力衰竭是一种复杂的异质综合症，由于其高发病率和死亡率以及很少有有效的治疗选择，因此需要改进分类。HFpEF是检查精密医学方法的实用性和可行性的理想设置。本文（基于2019年美国超声心动图学会科学会议上发表的第20届年度Feigenbaum演讲）描述了超声心动图作为``数字活检''的实用性，以及如何使用深度定量超声心动图表型以及机器学习来识别新型HF​​pEF表型。基于异常斑点跟踪超声心动图（STE）的心脏力学测量的细胞和超微结构基础可为了解心肌细胞钙稳态提供一个窗口。因此，基于STE的纵向应变测量可以告知HFpEF患者的心肌受累程度，这可能有助于确定对心脏特异性HF药物的反应性。但是，对HFpEF的复杂，系统，多器官性质进行适当分类可能需要采用更高级的方法。使用无监督机器学习，HFpEF可以分为三个不同的表型，具有不同的临床和超声心动图特征和结果：（1）利钠肽缺乏综合征；（2）严重的心脏代谢综合征；（3）右心室－腹－腹－肾综合征。可以探测每种以确定其生物学基础。改善HFpEF分类的目的是使合适的患者与合适的治疗相匹配，希望改善HFpEF临床试验的记录。本文强调了超声心动图在先进医学中的核心作用，并阐明了超声心动图中基础，转化，临床和人群研究的整合，目的是更好地了解复杂的心血管综合征的病理生物学。