Evidence map›Paper›PMID 36996138›Full record

ArticlePloS one2023

Validation of the severe COVID-19 prognostic value of serum IL-6, IFN-λ3, CCL17, and calprotectin considering the timing of clinical need for prediction.

Kei Yamamoto, Yusuke Ohsiro, Tetsuya Suzuki, Michiyo Suzuki, Sayaka Miura, Maki Nagashima, Noriko Iwamoto, Junko S Takeuchi, Moto Kimura, Wataru Sugiura and 3 more

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Observational
  2. Article
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.

Kei YamamotoDisease Control and Prevention Center, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan.ORCID 0000-0001-6218-523X
Yusuke OhsiroClinical Laboratory, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan.
Tetsuya SuzukiDisease Control and Prevention Center, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan.ORCID 0000-0003-3165-9062
Michiyo SuzukiDisease Control and Prevention Center, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan.
Sayaka MiuraDisease Control and Prevention Center, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan.
Maki NagashimaDisease Control and Prevention Center, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan.
Noriko IwamotoDisease Control and Prevention Center, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan.
Junko S TakeuchiCenter for Clinical Sciences, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan.
Moto KimuraCenter for Clinical Sciences, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan.ORCID 0000-0003-2202-5527
Wataru SugiuraCenter for Clinical Sciences, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan.
Satoru NebuyaSanyo Chemical Industries, Ltd., Higashiyama-ku, Kyoto, Japan.
Masato KurokawaSanyo Chemical Industries, Ltd., Higashiyama-ku, Kyoto, Japan.
Norio OhmagariDisease Control and Prevention Center, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although biomarkers to predict coronavirus disease 2019 (COVID-19) severity have been studied since the early pandemic, no clear guidelines on using them in clinical practice are available. Here, we examined the ability of four biomarkers to predict disease severity using conserved sera from COVID-19 patients who received inpatient care between January 1, 2020 and September 21, 2021 at the National Center for Global Health and Medicine, collected at the appropriate time for prediction. We predicted illness severity in two situations: 1) prediction of future oxygen administration for patients without oxygen support within 8 days of onset (Study 1) and 2) prediction of future mechanical ventilation support (excluding non-invasive positive pressure ventilation) or death of patients within 4 days of the start of oxygen administration (Study 2). Interleukin-6, IFN-λ3, thymus and activation-regulated chemokine, and calprotectin were measured retrospectively. Other laboratory and clinical information were collected from medical records. AUCs were calculated from ROC curves and compared for the predictive ability of the four biomarkers. Study 1 included 18 patients, five of whom had developed oxygen needs. Study 2 included 45 patients, 13 of whom required ventilator management or died. In Study 1, IFN-λ3 showed a good predictive ability with an AUC of 0.92 (95% CI 0.76-1.00). In Study 2, the AUC of each biomarker was 0.70-0.74. The number of biomarkers above the cutoff showed the possibility of good prediction with an AUC of 0.86 (95% CI 0.75-0.97). When two or more biomarkers were positive, sensitivity and specificity were 0.92 and 0.63, respectively. In terms of biomarker testing at times when prognostication may be clinically useful, IFN-λ3 was predictive of oxygenation demand and a combination of the four biomarkers was predictive of mechanical ventilator requirement.

Indexed as

COVID-19BiomarkersChemokine CCL17HumansInterferon LambdaInterleukin-6InterleukinsLeukocyte L1 Antigen ComplexOxygenPrognosisRetrospective StudiesSARS-CoV-2BiomarkersCCL17 protein, humanChemokine CCL17Interferon Lambdainterferon-lambda, humanInterleukin-6InterleukinsLeukocyte L1 Antigen ComplexOxygen

Identifiers

PMID36996138
PMCPMC10062661

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.