Phenotyping of heart failure with preserved ejection faction using electronic health records and echocardiography.

European heart journal open Pub Date : 2023-12-14 eCollection Date: 2024-01-01 DOI:10.1093/ehjopen/oead133

Morgane Pierre-Jean, Benjamin Marut, Elizabeth Curtis, Elena Galli, Marc Cuggia, Guillaume Bouzillé, Erwan Donal

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引用次数: 0

Abstract

Aims: Patients presenting symptoms of heart failure with preserved ejection fraction (HFpEF) are not a homogenous population. Different phenotypes can differ in prognosis and optimal management strategies. We sought to identify phenotypes of HFpEF by using the medical information database from a large university hospital centre using machine learning.

Methods and results: We explored the use of clinical variables from electronic health records in addition to echocardiography to identify different phenotypes of patients with HFpEF. The proposed methodology identifies four phenotypic clusters based on both clinical and echocardiographic characteristics, which have differing prognoses (death and cardiovascular hospitalization).

Conclusion: This work demonstrated that artificial intelligence-derived phenotypes could be used as a tool for physicians to assess risk and to target therapies that may improve outcomes.

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利用电子健康记录和超声心动图对保留射血功能型心力衰竭进行表型分析。

目的：出现射血分数保留型心力衰竭（HFpEF）症状的患者并不是一个单一的群体。不同的表型在预后和最佳治疗策略上可能存在差异。我们试图利用一个大型大学医院中心的医疗信息数据库，通过机器学习来识别 HFpEF 的表型：除了超声心动图外，我们还探索了利用电子健康记录中的临床变量来识别高频心动过速患者的不同表型。所提出的方法根据临床和超声心动图特征识别出四个表型集群，它们的预后（死亡和心血管住院）各不相同：这项研究表明，人工智能衍生的表型可作为医生评估风险和针对性治疗的工具，从而改善预后。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European heart journal open

CiteScore

2.80

自引率

0.00%

发文量