整合代谢学方法论,用于开发预测模型,用于严重COVID-19的老年患者的死亡风险
Shanpeng Cui1,2, Qiuyuan Han1, Ran Zhang3
1Department of Critical Care Medicine, Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, Heilongjiang Province, China.
BMC infectious diseases
|January 3, 2025
概括
一个新的模型预测了老年患者的COVID-19死亡风险,该模型使用血液中意甲酸水平和关键实验室标记 (LYM,IL-6,PCT,CRP). 这有助于对当前患者群体的早期风险评估.
科学领域:
- * * 医学科学 医学科学
- * 生物化学 * 生物化学
- * 临床医学 临床医学
背景情况:
- *由于人口免疫力发生变化,早期的COVID-19代谢学研究已经过时.
- *本研究使用了疫苗接种/暴露人群的当前代谢学数据.
- * 解决了对重症COVID-19患者更新预后工具的需求.
研究的目的:
- * 开发一个预测模型来预测老年人早期死亡风险,重症COVID-19患者.
- * 整合代谢学和临床数据,以提高预后准确度.
- * 为部分免疫人口提供有关COVID-19病理生理学的最新见解.
主要方法:
- *招募了100名老年COVID-19患者 (2023年1月至2月);46名幸存者,54名未幸存者.
- * 开发了一个预测模型,使用血液代谢学和临床数据在入院后24小时内.
- *在50名老年COVID-19患者的单独队列中对该模型进行了外部验证.
主要成果:
- *确定了幸存者和非幸存者之间的显著代谢和实验室差异.
- * 伊塔科尼酸,LYM,IL-6,PCT和CRP是所有模型中的关键预测因素.
- *KNN模型的AUC值为0.952,灵敏度为0.963和特异性为0.957.
结论:
- * 伊塔科尼酸,LYM,IL-6,PCT和CRP水平是老年COVID-19患者死亡率的重要预测因素.
- *这五个指标可以有效地评估入院时的死亡风险.
- * 开发的模型为早期风险分层提供了有价值的工具.
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