A personalized BEST: characterization of latent clinical classes of nonischemic heart failure that predict outcomes and response to bucindolol.
A personalized BEST: characterization of latent clinical classes of nonischemic heart failure that predict outcomes and response to bucindolol.
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DOI：
10.1371/journal.pone.0048184
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Lowes BD
中科院分区：
文献类型：
--
作者：
Kao DP;Wagner BD;Robertson AD;Bristow MR;Lowes BD
射血分数降低的心力衰竭患者（HFREF）是异质的，我们识别可能对治疗有反应的患者的能力有限。我们提出了一种使用高维临床表型和潜在类别分析来识别疾病亚型的方法，该方法可能有助于 HFREF 的个性化预后和治疗。 β 受体阻滞剂生存评估试验中共有 1121 名非缺血性 HFREF 患者根据 27 种临床特征进行分类。潜在类别分析用于生成两个潜在类别模型，LCM A 和 B，以识别 HFREF 亚型。 LCM A 由与 HF 发病机制相关的特征组成，而 LCM B 由 HF 进展和严重程度的标志物组成。还计算了所有患者的西雅图心力衰竭模型 (SHFM) 评分。比较不同 HFREF 亚型的死亡率、左心室射血分数 (LVEF) 的改善（定义为 12 个月后 LVEF 增加 ≥5% 且最终 LVEF 为 35%），以及布吲洛尔对这两种结果的影响。对包括 LCM 亚型和 SHFM 评分组合在内的模型在预测死亡率和 LVEF 反应方面的性能进行了估计，随后使用留一交叉验证和来自多中心口服卡维地洛心力衰竭评估试验的数据进行了验证。使用 LCM A 鉴定出总共 6 种亚型，使用 LCM B 鉴定出 5 种亚型。几种亚型类似于熟悉的临床表型。不同亚型的预后、左心室射血分数 (LVEF) 的改善以及布吲洛尔治疗的效果存在显着差异。通过将两个潜在类别模型添加到 SHFM，预测结果得到改善，包括 1 年死亡率和 LVEF 反应结果。高维表型分析和潜在类别分析相结合，确定了 HFREF 的亚型，对预后和对特定疗法的反应有影响，从而可以深入了解疾病机制。这些亚型可能有助于制定个性化治疗计划。
Heart failure patients with reduced ejection fraction (HFREF) are heterogenous, and our ability to identify patients likely to respond to therapy is limited. We present a method of identifying disease subtypes using high-dimensional clinical phenotyping and latent class analysis that may be useful in personalizing prognosis and treatment in HFREF. A total of 1121 patients with nonischemic HFREF from the β-blocker Evaluation of Survival Trial were categorized according to 27 clinical features. Latent class analysis was used to generate two latent class models, LCM A and B, to identify HFREF subtypes. LCM A consisted of features associated with HF pathogenesis, whereas LCM B consisted of markers of HF progression and severity. The Seattle Heart Failure Model (SHFM) Score was also calculated for all patients. Mortality, improvement in left ventricular ejection fraction (LVEF) defined as an increase in LVEF ≥5% and a final LVEF of 35% after 12 months, and effect of bucindolol on both outcomes were compared across HFREF subtypes. Performance of models that included a combination of LCM subtypes and SHFM scores towards predicting mortality and LVEF response was estimated and subsequently validated using leave-one-out cross-validation and data from the Multicenter Oral Carvedilol Heart Failure Assessment Trial. A total of 6 subtypes were identified using LCM A and 5 subtypes using LCM B. Several subtypes resembled familiar clinical phenotypes. Prognosis, improvement in LVEF, and the effect of bucindolol treatment differed significantly between subtypes. Prediction improved with addition of both latent class models to SHFM for both 1-year mortality and LVEF response outcomes. The combination of high-dimensional phenotyping and latent class analysis identifies subtypes of HFREF with implications for prognosis and response to specific therapies that may provide insight into mechanisms of disease. These subtypes may facilitate development of personalized treatment plans.
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影响因子：
27.4
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Grayson PC;Maksimowicz-McKinnon K;Clark TM;Tomasson G;Cuthbertson D;Carette S;Khalidi NA;Langford CA;Monach PA;Seo P;Warrington KJ;Ytterberg SR;Hoffman GS;Merkel PA;Vasculitis Clinical Research Consortium
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影响因子：
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影响因子：
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