一个Delphi过程,以确定可能与预测分析工具相关的相关结果,以检测重症监护室内血液动力学恶化
Andre L Holder1, Ashish K Khanna2, Michael J Scott3
1Critical Care Medicine, Emory University School of Medicine, Atlanta, USA.
Cureus
|January 8, 2024
概括
这项研究使用了Delphi方法来确定重症监护室 (ICU) 中人工智能工具的关键结果. 血液动力学稳定指数 (HSI) 的最相关和可实现的结果是减少血液动力学不稳定度的时间和改善识别时间.
科学领域:
- 关键护理医学 关键护理医学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 人工智能工具越来越多地用于ICU的早期患者事件检测.
- 血液动力学稳定指数 (HSI) 显示出有希望的 (AUC 0.82) 预测血液动力学干预需求.
- 需要将未来的人工智能工具研究与临床相关和可实现的结果保持一致.
研究的目的:
- 确定人工智能工具,特别是HSI在ICU环境中最相关和可实现的结果.
- 引导未来的研究开发和验证人工智能预测分析用于重症监护.
主要方法:
- 一项为期三轮的Delphi研究,涉及美国10名重症监护医生和3名护士.
- 专家小组成员使用5分级评分潜在结果的相关性 (≥65%的评分为4或5的共识).
- 用Kemeny-Young方法根据相关性和可实现性对结果进行排名.
主要成果:
- 在24个可能的结果中,有19个实现了关于相关性的共识.
- "减少血液动力学不稳定的时间"和"减少血液动力学不稳定的识别时间"排名最高.
- 一个矩阵可视化了结果根据相关性和可实现性的分布.
结论:
- 德尔菲研究有效地确定了ICUAI工具的相关和可实现的结果.
- 这些发现为旨在通过人工智能改善重症监护结果的研究人员提供了指导.
- 未来的研究应该集中在针对临床显著结果的AI工具上,例如减少血液动力学不稳定性持续时间和更快的识别.
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