Biomarkers-SAVE-ICU

Biomarkers-SAVE-ICU is a prospective cohort sub-study of the SAVE-ICU RCT that aims to compare Inflammatory, thrombosis, proteomic, metabolomic, transcriptional, and immunoglobulin profiles of critically ill patients receiving inhaled volatile anesthetic-based sedation regimen versus standard/usual care intravenous sedation regimen within SAVE-ICU RCT. This study builds on the success of Dr Fraser’s team that used state‐of‐the‐art scientific methods, including machine learning, to profile COVID‐19 related pathophysiology and to identify potential therapeutic targets and outcome biomarkers. We will now apply the same strategy to compare the effects of inhaled vs intravenous sedatives on the various biomarkers.

Key findings from Dr Fraser’s research in COVID-19 patients that informed the present study are summarized below:

1. Elevation in six biomarkers (tumor necrosis factor, granzyme B, heat shock protein 70, interleukin-18, interferon-gamma-inducible protein 10 and elastase) distinguished COVID-19 from non-COVID-19 critically ill patients. In contrast to other studies of sepsis and ARDS patients, where levels of tumor necrosis factor (TNF) increase transiently at disease onset, our critically ill COVID-19 patients showed persistent elevation of TNF during the first 7 days from ICU admission, suggesting that anti-TNF therapies as potential therapeutic targets.
2. Thrombosis profiling showed evidence of endothelial activation and glycocalyx degradation in critically ill COVID-19 patients.¹⁵ In contrast, we did not find any difference in the 3 thrombotic factors (ADAMTS13, protein C, and vWF) between COVID-19 and non-COVID-19 critically ill patients, suggesting that endothelial injury rather than coagulation pathway derangement is likely the primary driver of increased thrombosis reported in COVID-19 patients.
3. Analysis of biomarkers of coagulation, endothelial function and fibrinolysis showed that while elevated D-dimers were the strongest at distinguishing between COVID-19 patients and non-COVID-19 critically ill patients and healthy controls, they were not associated with mortality. Instead, clot lysis time, antigen levels of soluble thrombomodulin, plasminogen activator inhibitor-1 and plasminogen were associated with mortality.
4. Targeted proteomic analysis showed the presence of a unique COVID-19 proteome with six proteins predicting ICU mortality with 100% accuracy. The proteins were CMRF-35-like molecule, interleukin receptor-12 subunit B1, cluster of differentiation 83 [CD83], family with sequence similarity 3, insulin-like growth factor 1 receptor and opticin. The coronavirus disease 2019 proteome was dominated by interleukins and chemokines, as well as several membrane receptors linked to lymphocyte-associated microparticles and/or cell debris.
5. Metabolomics profiling showed that among 162 metabolites three metabolites (kynurenine, arginine, and creatinine) could be used as diagnostic and prognostic markers in COVID-19 disease. Arginine/kynurenine ratio accurately identified COVID-19 status, whereas creatinine/arginine ratio accurately predicted COVID-19 associated death.
6. Transcriptional profiling showed that critically ill COVID-19 patients on day 1 of admission to the ICU have a unique leukocyte transcriptional profile that distinguishes them from non-COVID-19 critically ill patients. These unique features included: (i) a robust overrepresentation of interferon-related gene expression; (ii) a marked decrease in the transcriptional level of genes contributing to general protein synthesis and bioenergy metabolism; and (iii) the dysregulated expression of genes associated with coagulation, platelet function, complement activation, and tumour necrosis factor/interleukin 6 signalling.
7. Anticoronavirus immunoglobulin G profiling showed that critically ill COVID-19 patients had anti-SARSCoV-2 immunoglobulin G, whereas serologic responses to non- SARSCoV-2 antigens were weak or absent.

Douglas Fraser

Angela Jerath

Beverly Orser

Brian Cuthbertson

Claudio Martin

Marat Slessarev

Coming soon