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AuthorTaleb, Sara
AuthorYassine, Hadi M
AuthorBenslimane, Fatiha M
AuthorSmatti, Maria K
AuthorSchuchardt, Sven
AuthorAlbagha, Omar
AuthorAl-Thani, Asmaa A
AuthorAit Hssain, Ali
AuthorDiboun, Ilhame
AuthorElrayess, Mohamed A
Available date2021-09-19T07:50:31Z
Publication Date2021
Publication NameFrontiers in Medicine
Identifierhttp://dx.doi.org/10.3389/fmed.2021.733657
CitationTaleb S, Yassine HM, Benslimane FM, Smatti MK, Schuchardt S, Albagha O, Al-Thani AA, Ait Hssain A, Diboun I and Elrayess MA (2021) Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients. Front. Med. 8:733657. doi: 10.3389/fmed.2021.733657
URIhttp://hdl.handle.net/10576/23245
AbstractDetection 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.
SponsorThis project was funded by Qatar University's internal grant number QUHI-BRC-20/21-1. This publication was made possible by GSRA grant, ID# GSRA5-1-0602-18124, from the Qatar National Research Fund (a member of Qatar Foundation).
Languageen
PublisherFrontiers Media
SubjectCOVID-19
ICU management
ICU outcome
biomarkers
metabolomics
TitlePredictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients.
TypeArticle
Volume Number8
ESSN2296-858X
dc.accessType Open Access


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