AUTHOR=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. 

TITLE=Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients

JOURNAL=Frontiers in Medicine

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.733657

DOI=10.3389/fmed.2021.733657

ISSN=2296-858X

ABSTRACT=Introduction: Detection of early metabolic changes in critically-ill COVID-19 patients under invasive mechanical ventilation (IMV) at the intensive care unit (ICU) could predict recovery patterns and help in disease management. 
Methods: Targeted metabolomics of serum samples from 39 COVID-19 patients under IMV in ICU was performed within 48 hours 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.
Results: 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 (AUC=0.92). A further model based on kynurenine, 3-methylhistidine, Ornithine, p-Cresol sulfate and C24.0 sphingomyelin, measured one-week later, accurately predicted the duration of IMV (Pearson correlation=0.94). Both predictive models outperformed APACHE II scores and differentiated COVID-19 severity in published data. 
Conclusion: 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.