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通过在电子健康记录中实现机器学习来识别慢性阻塞性肺病子组的严重急性恶化

Huan Li¹, John Huston¹, Jana Zielonka¹

¹Pulmonary, Critical Care and Sleep Medicine Section, Yale University, New Haven, Connecticut, United States.

Chronic obstructive pulmonary diseases (Miami, Fla.)

|October 18, 2024

概括

应用到电子健康记录的机器学习确定了慢性阻塞性肺病 (AECOPD) 严重急性恶化中不同的子组. 这些子组,包括心脏脏类型和埃索诺菲尔类型,表现出不同的临床特征和炎症概况.

科学领域:

肺部医学肺部医学
计算生物学计算生物学
医疗信息学医疗信息学

背景情况:

慢性阻塞性肺病 (AECOPD) 的急性恶化表现出显著的异质性.
机器学习 (ML) 在剖析COPD异质性方面表现有前途.
电子健康记录 (EHR) 提供了大量的患者数据,但它们在通过ML识别AECOPD亚组的实用性尚未得到充分证实.

研究的目的:

调查使用EHR数据来实施ML是否可以在需要住院治疗的严重AECOPD中识别相关子组.
描述已识别的AECOPD亚组之间的临床和生物区别.

主要方法:

来自耶鲁-纽黑文医院的住院AECOPD患者的两个队列 (非COVID-19和COVID-19) 的回顾性分析.
应用k-means集群对EHR衍生的数据进行患者子组识别.
确定子组的验证和炎症标记物的分析 (细胞因子,C反应蛋白).

主要成果:

在非COVID队列中确定了三个不同的亚组:参考,心和eosinophilic.
乙酸性亚组呈现出较轻的AECOPD严重程度和较短的住院时间.
心脏脏子组在住院期间和住院后死亡率最高 (分别为5%和30%).
在COVID-19队列中重新总结了子组特征和炎症概况 (IL-1β,IL-2R,IL-8),突出了明显的炎症差异.

结论:

通过将ML与EHR数据集成,可以有效地识别严重AECOPD中的特定临床和生物子组.
这些发现强调了基于确定的患者子组的个性化治疗策略的潜力.

关键词:

慢性肺炎的急性恶化集群集群是指一个集群的集群.电子健康记录是电子健康记录.机器学习是机器学习.

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