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Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients
- Source :
- Frontiers in Medicine, Vol 8 (2021)
- Publication Year :
- 2021
- Publisher :
- Frontiers Media S.A., 2021.
-
Abstract
- Introduction: 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.Methods: 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.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 [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.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.
- Subjects :
- COVID-19
metabolomics
biomarkers
ICU outcome
ICU management
Medicine (General)
R5-920
Subjects
Details
- Language :
- English
- ISSN :
- 2296858X
- Volume :
- 8
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Medicine
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.2fcbab3867e0401880c151997f6208c8
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fmed.2021.733657