在COVID-19抵达期间开发基于代谢基的呼吸道感染预后
John I Robinson1, Laura R Marks1, Andrew L Hinton2
1Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, USA.
这项研究使用机器学习识别了尿液代谢学特征,以预测严重的COVID-19结果. 这种预后生物标志物有助于在流行病期间早期分配资源.
科学领域:
- 生物化学 生物化学
- 医学诊断 医学诊断 医学诊断
- 计算生物学 计算生物学
背景情况:
- 在新的呼吸道病毒大流行期间,优化医疗资源配置至关重要.
- 早期预后对于在严重疾病发作之前有效地使用抗病毒疗法至关重要.
- 由于资源有限和感染控制措施有限,在流行病早期开发预测工具具有挑战性.
研究的目的:
- 在COVID-19患者中发现和验证严重呼吸衰竭或死亡的预后生物标志物签名.
- 评估新陈代谢分析在流行病期间快速预后评估的实用性.
- 在未来的流行病中为治疗和资源分配决策提供信息.
主要方法:
- 在最初的COVID-19浪潮期间,从163名患者收集了尿液样本.
- 液态染色体质谱法 (LC-MS) 用于代谢分析.
- 机器学习识别了与严重结果相关的三种元型尿液签名,在单独的队列中得到验证.
主要成果:
- 一个尿液代谢签名在发现队列中达到89.4%的ROC AUC,可以在90天内预测严重的结果.
- 签名显示,在一个盲目验证队列中,ROC AUC为81.2%.
- 一个使用两个基线元类型的模型在验证队列中显示了类似的预测性能.
结论:
- 基于快速代谢的预后生物标志物发现和验证是可行的,即使是在流行病的早期阶段.
- 鉴定到的尿液签名显示,它有望为关键资源分配决策提供信息.
- 这种方法在未来的公共卫生紧急情况中对患者分层有价值.
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