使用常规临床数据划分COVID-19子组,可以确定不同的医院治疗结果
Bojidar Rangelov1, Alexandra Young2,3, Watjana Lilaonitkul4
1Satsuma Lab, Centre for Medical Image Computing (CMIC), University College London, London, UK. dar.rangelov@gmail.com.
Scientific reports
|June 20, 2023
概括
这项研究使用数据驱动模型确定了三种不同的COVID-19患者亚型,揭示了死亡率和治疗升级的不同风险. 这些发现有助于针对传染病提供个性化的医疗保健.
科学领域:
- 传染病流行病学 传染病流行病学
- 计算生物学 计算生物学
- 临床信息学 临床信息学
背景情况:
- 随着COVID-19的爆发,人们越来越需要先进的预测模型来管理疾病的异质性,并指导临床决策.
- 现有的模型往往缺乏适应性,以适应快速发展的传染病.
- 确定患者子组对于风险分层和资源分配至关重要.
研究的目的:
- 根据临床数据调整和应用无监督的SuStaIn模型来对COVID-19患者进行分类.
- 在住院COVID-19病例中发现不同的患者亚型和疾病严重程度阶段.
- 评估已识别的亚型和病期对住院死亡率和治疗升级的预测价值.
主要方法:
- 使用了SuStaIn (子类型和阶段) 无监督学习模型.
- 将该模型应用于来自国家COVID-19胸部成像数据库 (NCCID) 的1344名住院COVID-19患者.
- 数据包括11项常见的临床措施,患者分为培训和验证队列.
主要成果:
- 发现了三种不同的COVID-19患者亚型:一般血液动力学,脏和免疫学.
- 确定了疾病严重程度的阶段,这些阶段和亚型预测了在医院死亡或治疗升级的不同风险.
- 还确定了一种低风险的"正常出现"亚型,有助于全面的患者分层.
结论:
- 适应的SuStaIn模型有效地识别了具有预后价值的COVID-19亚型和严重程度阶段.
- 这些数据驱动的分类可以改善传染病的决策和治疗优先级.
- 开发的管道可适应未来的传染病爆发,为公共卫生准备提供了有价值的工具.
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