Evidence map›Paper›PMID 36949873›Full record

ArticleOpen forum infectious diseases2023

Blood Inflammatory Biomarkers Differentiate Inpatient and Outpatient Coronavirus Disease 2019 From Influenza.

Lauren L Luciani, Leigh M Miller, Bo Zhai, Karen Clarke, Kailey Hughes Kramer, Lucas J Schratz, G K Balasubramani, Klancie Dauer, M Patricia Nowalk, Richard K Zimmerman and 2 more

Open access · goldAbstract read
In one paragraph

Article in Open forum infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 32% of its field
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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026
    Review
  3. 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

12 authors at 2 institutions in 1 country.

Lauren L LucianiDepartment of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Leigh M MillerDepartment of Pediatrics, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Bo ZhaiDepartment of Pediatrics, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Karen ClarkeDepartment of Family Medicine and Clinical Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Kailey Hughes KramerDepartment of Internal Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Lucas J SchratzDepartment of Pediatrics, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA.
G K BalasubramaniDepartment of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Klancie DauerDepartment of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
M Patricia NowalkDepartment of Family Medicine and Clinical Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Richard K ZimmermanDepartment of Family Medicine and Clinical Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Jason E ShoemakerDepartment of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
John F AlcornDepartment of Pediatrics, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA.
University of Pittsburgh · USChildren's Hospital of Pittsburgh · US

Funding

University of Pittsburgh Clinical and Translational Science InstituteUL1TR001857 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2016 to 2025
$129.3M
Pittsburgh Training Grant in Pediatric Pulmonary MedicineT32HL129949 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Juan Carlos Celedon · 2016 to 2026
$2.9M
NCATS NIH HHS UL1 TR001857NCIRD CDC HHS U01 IP000969NCIRD CDC HHS U01 IP001035NHLBI NIH HHS T32 HL129949
6 · The paper itself

Abstract

Background: The ongoing circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a diagnostic challenge because symptoms of coronavirus disease 2019 (COVID-19) are difficult to distinguish from other respiratory diseases. Our goal was to use statistical analyses and machine learning to identify biomarkers that distinguish patients with COVID-19 from patients with influenza. Methods: Cytokine levels were analyzed in plasma and serum samples from patients with influenza and COVID-19, which were collected as part of the Centers for Disease Control and Prevention's Hospitalized Adult Influenza Vaccine Effectiveness Network (inpatient network) and the US Flu Vaccine Effectiveness (outpatient network). Results: We determined that interleukin (IL)-10 family cytokines are significantly different between COVID-19 and influenza patients. The results suggest that the IL-10 family cytokines are a potential diagnostic biomarker to distinguish COVID-19 and influenza infection, especially for inpatients. We also demonstrate that cytokine combinations, consisting of up to 3 cytokines, can distinguish SARS-CoV-2 and influenza infection with high accuracy in both inpatient (area under the receiver operating characteristics curve [AUC] = 0.84) and outpatient (AUC = 0.81) groups, revealing another potential screening tool for SARS-CoV-2 infection. Conclusions: This study not only reveals prospective screening tools for COVID-19 infections that are independent of polymerase chain reaction testing or clinical condition, but it also emphasizes potential pathways involved in disease pathogenesis that act as potential targets for future mechanistic studies.

Indexed as

cytokinehumanmachine learningpneumoniaSARS-CoV-2

Identifiers

PMID36949873
PMCPMC10026548
OpenAlexW4321435624

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.