Taleb Sara, Yassine Hadi M, Benslimane Fatiha M, Smatti Maria K, Schuchardt Sven, Albagha Omar, Al-Thani Asmaa A, Ait Hssain Ali, Diboun Ilhame, Elrayess Mohamed A
Division of Genomics and Translational Biomedicine, College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar.
Biomedical Research Center (BRC), Qatar University, Doha, Qatar.
Front Med (Lausanne). 2021 Aug 12;8:733657. doi: 10.3389/fmed.2021.733657. eCollection 2021.
Detection of early metabolic changes in critically-ill coronavirus disease 2019 (COVID-19) patients under invasive mechanical ventilation (IMV) at the intensive care unit (ICU) could predict recovery patterns and help in disease management. Targeted metabolomics of serum samples from 39 COVID-19 patients under IMV in ICU was performed within 48 h of intubation and a week later. A generalized linear model (GLM) was used to identify, at both time points, metabolites and clinical traits that predict the length of stay (LOS) at ICU (short ≤ 14 days/long >14 days) as well as the duration under IMV. All models were initially trained on a set of randomly selected individuals and validated on the remaining individuals in the cohort. Further validation in recently published metabolomics data of COVID-19 severity was performed. A model based on hypoxanthine and betaine measured at first time point was best at predicting whether a patient is likely to experience a short or long stay at ICU [area under curve (AUC) = 0.92]. A further model based on kynurenine, 3-methylhistidine, ornithine, p-cresol sulfate, and C24.0 sphingomyelin, measured 1 week later, accurately predicted the duration of IMV (Pearson correlation = 0.94). Both predictive models outperformed Acute Physiology and Chronic Health Evaluation II (APACHE II) scores and differentiated COVID-19 severity in published data. This study has identified specific metabolites that can predict in advance LOS and IMV, which could help in the management of COVID-19 cases at ICU.
在重症监护病房（ICU）对接受有创机械通气（IMV）的2019冠状病毒病（COVID-19）危重症患者早期代谢变化的检测，可预测恢复模式并有助于疾病管理。对39例在ICU接受IMV的COVID-19患者的血清样本，在插管后48小时内及一周后进行了靶向代谢组学分析。使用广义线性模型（GLM）在两个时间点识别可预测在ICU的住院时间（LOS，短≤14天/长>14天）以及IMV持续时间的代谢物和临床特征。所有模型最初在一组随机选择的个体上进行训练，并在队列中的其余个体上进行验证。在最近发表的关于COVID-19严重程度的代谢组学数据中进行了进一步验证。基于在第一个时间点测量的次黄嘌呤和甜菜碱的模型，在预测患者在ICU的住院时间是短还是长方面表现最佳[曲线下面积（AUC）=0.92]。另一个基于一周后测量的犬尿氨酸、3-甲基组氨酸、鸟氨酸、对甲酚硫酸盐和C24.0鞘磷脂的模型，准确预测了IMV的持续时间（Pearson相关性=0.94）。这两个预测模型均优于急性生理与慢性健康状况评估II（APACHE II）评分，并在已发表的数据中区分了COVID-19的严重程度。本研究确定了可提前预测住院时间和IMV的特定代谢物，这有助于ICU中COVID-19病例的管理。
