在结合的患者+护理系统中的实证表型化:生成低维类别,用于对机械通风患者进行假设驱动的研究
J N Stroh1, Peter D Sottile2, Yanran Wang1,2,3,4,3,4,5
1University of Colorado Anschutz Medical Campus.
medRxiv : the preprint server for health sciences
|January 3, 2024
概括
这项研究引入了一个计算管道来表型患者-呼吸机相互作用数据,揭示了不同的肺-呼吸机系统 (LVS) 行为,以改善机械通风 (MV) 管理. 该方法细分复杂的数据,有助于理解随时间推移的MV后果.
科学领域:
- 生物医学工程 生物医学工程
- 关键护理医学 关键护理医学
- 数据科学数据科学数据科学
背景情况:
- 在机械呼吸 (MV) 过程中分析患者数据对于改善护理和了解长期后果至关重要.
- 复杂性源于肺-呼吸系统 (LVS) 相互作用,患者的变化以及缺乏行为分类.
- 当前的流程与患者 - 呼吸机动态的异质性作斗争.
研究的目的:
- 开发一个计算管道用于细分和表型联合波形和护理设置数据.
- 创建一种方法来理解患者与呼吸机相互作用的数据生成过程.
- 为了能够更有效地分析机械通风数据.
主要方法:
- 开发了一个模块化计算管道,以分割联合波形和护理设置时间序列.
- 无监督的集群技术被用于数据表型化.
- 该方法允许在波形表示和集群算法中进行各种选择.
主要成果:
- 管道确定了典型的表型,捕获了35名ARDS患者的97%数据,包括COVID-19患者.
- 现象类型围绕呼吸器设置 (模式,PEEP,潮体积) 和波形行为进行组织.
- 很少有表型变化与通风器设置调整相关,突出显著的LVS动态.
结论:
- 开发的管道有效地将复杂的患者-呼吸机数据细分为有意义的表型.
- 现象类型可以对机械通风数据进行紧的纵向分析.
- 可概括的管道可以针对特定的研究应用和LVS行为分析进行优化.
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