Phenomapping for novel classification of heart failure with preserved ejection fraction.
Phenomapping for novel classification of heart failure with preserved ejection fraction.
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DOI：
10.1161/circulationaha.114.010637
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发表时间：
2015-01-20
期刊：
影响因子：
37.8
通讯作者：
Deo RC
中科院分区：
文献类型：
--
作者：
Shah SJ;Katz DH;Selvaraj S;Burke MA;Yancy CW;Gheorghiade M;Bonow RO;Huang CC;Deo RC
射血分数保留性心力衰竭（HFpEF）是一种异质性临床综合征，需要改进表型分类。我们试图评估使用密集表型数据的无偏聚类分析（“表型作图”）是否可以鉴定表型不同的HFpEF类别。我们前瞻性研究了397例HFpEF患者，并对研究参与者进行了详细的临床、实验室、心电图和超声心动图表型分析。我们使用了几种统计学习算法，包括表型数据（67个连续变量）的无偏分层聚类分析和基于惩罚模型的聚类，以定义和表征包含HFpEF新分类的互斥组。所有的phenomapping分析都是对临床结果不知情的，并且使用考克斯回归来证明phenomapping的临床有效性。平均年龄为65±12岁，62%为女性，39%为非洲裔美国人，合并症常见。尽管所有患者均符合已发表的HFpEF诊断标准，但现象图谱分析将研究参与者分为3个不同的组，这些组在临床特征、心脏结构/功能、侵入性血流动力学和结局（例如，表型组#3具有增加的HF住院风险[风险比4.2，95%CI 2.0-9.1]，即使在调整传统风险因素后[P<0.001]）。HFpEF表型组分类，包括其分层风险的能力，在前瞻性验证队列中成功复制（n=107）。Phenomapping导致HFpEF的新分类。应用于密集表型数据的统计学习算法可以允许改进异质性临床综合征的分类，最终目标是定义治疗上同质的患者亚类。
Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (“phenomapping”) could identify phenotypically distinct HFpEF categories. We prospectively studied 397 HFpEF patients and performed detailed clinical, laboratory, electrocardiographic, and echocardiographic phenotyping of the study participants. We used several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering to define and characterize mutually exclusive groups comprising a novel classification of HFpEF. All phenomapping analyses were performed blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65±12 years, 62% were female, 39% were African-American, and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (e.g., pheno-group #3 had an increased risk of HF hospitalization [hazard ratio 4.2, 95% CI 2.0–9.1] even after adjustment for traditional risk factors [P<0.001]). The HFpEF pheno-group classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107). Phenomapping results in novel classification of HFpEF. Statistical learning algorithms, applied to dense phenotypic data, may allow for improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.