在COVID ARDS中死亡率的驱动因素取决于患者亚型
Helen Cheyne1, Amir Gandomi2, Shahrzad Hosseini Vajargah1
1BioSymetrics, Inc., Huntington, NY, USA.
Computers in biology and medicine
|September 25, 2023
概括
COVID-19急性呼吸困难综合征 (ARDS) 是异质的. 机器学习识别了独特的COVID-19 ARDS患者群体 (现象群),具有独特的死亡风险因素,对于有针对性的治疗至关重要.
科学领域:
- 关键护理医学 关键护理医学
- 肺部病理学 肺部病理学
- 在医疗保健中的数据科学.
背景情况:
- 急性呼吸窘迫综合征 (ARDS) 是COVID-19患者死亡的主要原因之一.
- 上一篇 ARDS 现象集群的努力还没有完全捕捉到 COVID-19 ARDS 特定的异质性.
- 了解COVID-19 ARDS异质性对于开发有效的治疗策略至关重要.
研究的目的:
- 在COVID-19 ARDS患者队列中识别临床相关的团.
- 确定与医院死亡率相关的特定现象群风险因素.
- 为了调查COVID-19 ARDS现象集中的死亡风险因素如何不同.
主要方法:
- 这是一项回顾性队列研究,利用2864名住院COVID-19 ARDS患者的数据集.
- 代的监督和无监督机器学习方法的应用,用于现象集群识别.
- 开发和比较预测模型的医院死亡率在每个现象集群和整体队列.
主要成果:
- 在COVID-19 ARDS队列中识别不同的现象集群.
- 证明死亡风险因素在已识别的现象集群中存在显著差异.
- 观察到一些风险因素可以增加一个现象群中的死亡率,而在另一个现象群中降低死亡率.
结论:
- COVID-19 ARDS 呈现出显著的异质性,需要对现象集群进行特定分析.
- 整体死亡风险因素可能不适用于个别的COVID-19 ARDS现象集群.
- 鉴定群特异性风险因素对于推进COVID-19 ARDS的治疗策略至关重要.
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