Evidence map›Paper›PMID 32904064›Full record

ArticleCritical care explorations2020

Novel Outcome Biomarkers Identified With Targeted Proteomic Analyses of Plasma From Critically Ill Coronavirus Disease 2019 Patients.

Douglas D Fraser, Gediminas Cepinskas, Eric K Patterson, Marat Slessarev, Claudio Martin, Mark Daley, Maitray A Patel, Michael R Miller, David B O'Gorman, Sean E Gill and 3 more

Abstract read
In one paragraph

Article in Critical care explorations, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed.

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  16. High-throughput proteomics: a methodological mini-review.Laboratory investigation; a journal of technical methods and pathology · 2022
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Douglas D FraserLawson Health Research Institute, London, ON, Canada.
Gediminas CepinskasLawson Health Research Institute, London, ON, Canada.
Eric K PattersonLawson Health Research Institute, London, ON, Canada.
Marat SlessarevLawson Health Research Institute, London, ON, Canada.
Claudio MartinLawson Health Research Institute, London, ON, Canada.
Mark DaleyLawson Health Research Institute, London, ON, Canada.
Maitray A PatelComputer Science, Western University, London, ON, Canada.
Michael R MillerLawson Health Research Institute, London, ON, Canada.
David B O'GormanLawson Health Research Institute, London, ON, Canada.
Sean E GillLawson Health Research Institute, London, ON, Canada.
Guillaume ParePathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada.
Ioannis PrassasLaboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.
Eleftherios DiamandisLaboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesCoronavirus disease 2019 patients admitted to the ICU have high mortality. The host response to coronavirus disease 2019 has only been partially elucidated, and prognostic biomarkers have not been identified. We performed targeted proteomics on critically ill coronavirus disease 2019 patients to better understand their pathophysiologic mediators and to identify potential outcome markers.

designBlood was collected at predetermined ICU days for proximity extension assays to determine the plasma concentrations of 1,161 proteins.

settingTertiary care ICU and academic laboratory. SUBJECTS: All patients admitted to the ICU suspected of being infected with severe acute respiratory syndrome coronavirus 2, using standardized hospital screening methodologies, had blood samples collected until either testing was confirmed negative on ICU day 3 (coronavirus disease 2019 negative) or until ICU day 10 if the patient positive (coronavirus disease 2019 positive).

interventionsNone. MEASUREMENTS AND MAIN

resultsAge- and sex-matched healthy control subjects and ICU patients who were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well-balanced with the exception that coronavirus disease 2019 positive patients suffered bilateral pneumonia more frequently than coronavirus disease 2019 negative patients. Mortality rate for coronavirus disease 2019 positive ICU patients was 40%. Feature selection identified the top performing proteins for identifying coronavirus disease 2019 positive ICU patients from both healthy control subjects and coronavirus disease 2019 negative ICU patients (classification accuracies 100%). 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. Mortality was predicted for coronavirus disease 2019 positive patients based on plasma proteome profiling on both ICU day 1 (accuracy 92%) and ICU day 3 (accuracy 83%). Promising prognostic proteins were then narrowed down to six, each of which provided excellent classification performance for mortality when measured on ICU day 1 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; area-under-the-curve =1.0;

conclusionsTargeted proteomics with feature classification easily distinguished both healthy control subjects and coronavirus disease 2019 tested negative ICU patients from coronavirus disease 2019 tested positive ICU patients. Multiple proteins were identified that accurately predicted coronavirus disease 2019 tested positive patient mortality.

Indexed as

biomarkerscoronavirus disease 2019host responseinflammationintensive care unit

Identifiers

PMID32904064
PMCPMC7449255

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.