Norgard Nicholas B, Hempel Carolyn
University of Missouri Kansas City School of Medicine, 2411 Holmes St, Kansas City, MO, 64108, USA.
University at Buffalo School of Pharmacy and Pharmaceutical Sciences, 216 Kapoor Hall, Buffalo, NY, 14203, USA.
Curr Heart Fail Rep. 2017 Feb;14(1):1-6. doi: 10.1007/s11897-017-0314-3.
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.
心力衰竭（HF）是一种具有高度异质性的疾病状态，这使得治疗过程变得复杂。识别更精确的HF表型将有助于开发更具针对性的治疗方法并改善患者预后。本综述探讨了HF治疗中精准医学的未来发展。
HF并非具有统一发病机制的连续疾病谱，而是具有不同潜在病理生理特征的表型。挑战在于建立临床表型特征以指导治疗。表型映射是一种将机器学习算法应用于临床数据集的过程，已被用于识别表型上不同且具有临床意义的HF组。随着强大技术扩展我们的知识，未来的分析或许能够利用基因、表观遗传、蛋白质组和代谢组测量来编制更全面的表型概况。识别特定HF患者的临床特征，这些特征对特定治疗具有独特或不成比例的反应，将有助于更直接地选择最佳治疗方法，减少试错性用药，并有助于避免药物不良反应。
