Evidence map›Paper›PMID 36345428›Full record

ArticleOpen forum infectious diseases2022

Discriminatory Ability of Gas Chromatography-Ion Mobility Spectrometry to Identify Patients Hospitalized With COVID-19 and Predict Prognosis.

Joshua Nazareth, Daniel Pan, Jee Whang Kim, Jack Leach, James G Brosnan, Adam Ahmed, Emma Brodrick, Paul Bird, Alfian Wicaksono, Emma Daulton and 6 more

Open access · goldAbstract read
In one paragraph

Article in Open forum infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 37% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Gas Chromatography-Ion Mobility Spectrometry Reveals Acetoin as a Biomarker for Carbapenemase-Producing Klebsiella pneumoniae.Medical science monitor : international medical journal of experimental and clinical research · 2024
    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

16 authors at 3 institutions in 1 country.

Joshua NazarethDepartment of Respiratory Sciences, University of Leicester, Leicester, United Kingdom.ORCID https://orcid.org/0000-0002-0603-9091
Daniel PanDepartment of Respiratory Sciences, University of Leicester, Leicester, United Kingdom.
Jee Whang KimDepartment of Respiratory Sciences, University of Leicester, Leicester, United Kingdom.
Jack LeachUniversity of Leicester Medical School, Leicester, United Kingdom.
James G BrosnanUniversity of Leicester Medical School, Leicester, United Kingdom.
Adam AhmedUniversity of Leicester Medical School, Leicester, United Kingdom.
Emma BrodrickIMSPEX Diagnostics Ltd, Abercynon, United Kingdom.
Paul BirdDepartment of Clinical Microbiology, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom.
Alfian WicaksonoSchool of Engineering, University of Warwick, Coventry, United Kingdom.
Emma DaultonSchool of Engineering, University of Warwick, Coventry, United Kingdom.
Julian W TangDepartment of Respiratory Sciences, University of Leicester, Leicester, United Kingdom.
Caroline WilliamsDepartment of Respiratory Sciences, University of Leicester, Leicester, United Kingdom.
Pranabashis HaldarDepartment of Respiratory Sciences, University of Leicester, Leicester, United Kingdom.
James A CovingtonSchool of Engineering, University of Warwick, Coventry, United Kingdom.
Manish PareekDepartment of Respiratory Sciences, University of Leicester, Leicester, United Kingdom.
Amandip SahotaDepartment of Infectious Diseases and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom.
University of Leicester · GBUniversity of Warwick · GBUniversity Hospitals of Leicester NHS Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Rapid diagnostic and prognostic tests for coronavirus disease (COVID-19) are urgently required. We aimed to evaluate the diagnostic and prognostic ability of breath analysis using gas chromatography-ion mobility spectrometry (GC-IMS) in hospitalized patients with COVID-19. Methods: Between February and May 2021, we took 1 breath sample for analysis using GC-IMS from participants who were admitted to the hospital for COVID-19, participants who were admitted to the hospital for other respiratory infections, and symptom-free controls, at the University Hospitals of Leicester NHS Trust, United Kingdom. Demographic, clinical, and radiological data, including requirement for continuous positive airway pressure (CPAP) ventilation as a marker for severe disease in the COVID-19 group, were collected. Results: A total of 113 participants were recruited into the study. Seventy-two (64%) were diagnosed with COVID-19, 20 (18%) were diagnosed with another respiratory infection, and 21 (19%) were healthy controls. Differentiation between participants with COVID-19 and those with other respiratory tract infections with GC-IMS was highly accurate (sensitivity/specificity, 0.80/0.88; area under the receiver operating characteristics curve [AUROC], 0.85; 95% CI, 0.74-0.96). GC-IMS was also moderately accurate at identifying those who subsequently required CPAP (sensitivity/specificity, 0.62/0.80; AUROC, 0.70; 95% CI, 0.53-0.87). Conclusions: GC-IMS shows promise as both a diagnostic tool and a predictor of prognosis in hospitalized patients with COVID-19 and should be assessed further in larger studies.

Indexed as

COVID-19diagnosisrespiratory virusvolatile organic compounds

Identifiers

PMID36345428
PMCPMC9619573
OpenAlexW4298621401

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.