Objectives

This study sought to identify homogenous echocardiographic phenotypes in community-based cohorts and assess their association with outcomes.

Background

Asymptomatic cardiac dysfunction leads to a high risk of long-term cardiovascular morbidity and mortality; however, better echocardiographic classification of asymptomatic individuals remains a challenge.

Methods

Echocardiographic phenotypes were identified using K-means clustering in the first generation of the STANISLAS (Yearly non-invasive follow-up of Health status of Lorraine insured inhabitants) cohort (N = 827; mean age: 60 ± 5 years; men: 48%), and their associations with vascular function and circulating biomarkers were also assessed. These phenotypes were externally validated in the Malmö Preventive Project cohort (N = 1,394; mean age: 67 ± 6 years; men: 70%), and their associations with the composite of cardiovascular mortality (CVM) or heart failure hospitalization (HFH) were assessed as well.

Results

Three echocardiographic phenotypes were identified as “mostly normal (MN)” (n = 334), “diastolic changes (D)” (n = 323), and “diastolic changes with structural remodeling (D/S)” (n = 170). The D and D/S phenotypes had similar ages, body mass indices, cardiovascular risk factors, vascular impairments, and diastolic function changes. The D phenotype consisted mainly of women and featured increased levels of inflammatory biomarkers, whereas the D/S phenotype, consisted predominantly of men, displayed the highest values of left ventricular mass, volume, and remodeling biomarkers. The phenotypes were predicted based on a simple algorithm including e′, left ventricular mass and volume (e′VM algorithm). In the Malmö cohort, subgroups derived from e′VM algorithm were significantly associated with a higher risk of CVM and HFH (adjusted HR in the D phenotype = 1.87; 95% CI: 1.04 to 3.37; adjusted HR in the D/S phenotype = 3.02; 95% CI: 1.71 to 5.34).

Conclusions

Among asymptomatic, middle-aged individuals, echocardiographic data-driven classification based on the simple e′VM algorithm identified profiles with different long-term HF risk. (4th Visit at 17 Years of Cohort STANISLAS-Stanislas Ancillary Study ESCIF [STANISLASV4]; NCT01391442)

中文翻译：

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机器学习衍生的超声心动图表型预测无症状个体的心力衰竭发病率

目标

本研究旨在确定基于社区的队列中的同质超声心动图表型，并评估它们与结果的关联。

背景

无症状的心功能不全导致长期心血管发病率和死亡率的高风险；然而，对无症状个体进行更好的超声心动图分类仍然是一个挑战。

方法

在第一代 STANISLAS（洛林投保居民健康状况的年度无创随访）队列（N = 827；平均年龄：60 ± 5 岁；男性：48% )，并评估了它们与血管功能和循环生物标志物的关联。这些表型在马尔默预防项目队列（N = 1,394；平均年龄：67 ± 6 岁；男性：70%）中得到了外部验证，它们与心血管死亡率 (CVM) 或心力衰竭住院 (HFH) 的复合相关性为也进行了评估。

结果

三种超声心动图表型被确定为“大部分正常 (MN)” (n = 334)、“舒张期变化 (D)” (n = 323) 和“伴随结构重塑的舒张期变化 (D/S)” (n = 170) . D 和 D/S 表型具有相似的年龄、体重指数、心血管危险因素、血管损伤和舒张功能变化。D 表型主要由女性组成，炎症生物标志物水平升高，而 D/S 表型主要由男性组成，显示左心室质量、体积和重塑生物标志物的最高值。基于包括 e'、左心室质量和体积的简单算法（e'VM 算法）预测表型。在马尔默队列中，

结论

在无症状的中年个体中，基于简单 e'VM 算法的超声心动图数据驱动分类识别出具有不同长期 HF 风险的特征。（第 17 年队列 STANISLAS-Stanislas 辅助研究 ESCIF [STANISLASV4] 的第四次访问；NCT01391442）