Evidence map›Paper›PMID 35885473›Full record

ArticleDiagnostics (Basel, Switzerland)2022

The Role of Imaging in the Detection of Non-COVID-19 Pathologies during the Massive Screening of the First Pandemic Wave.

Perrine Canivet, Colin Desir, Marie Thys, Monique Henket, Anne-Noëlle Frix, Benoit Ernst, Sean Walsh, Mariaelena Occhipinti, Wim Vos, Nathalie Maes and 4 more

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 54% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

14 authors at 1 institution in 1 country.

Perrine CanivetDepartment of Radiology, University Hospital of Liège, 4000 Liège, Belgium.
Colin DesirDepartment of Radiology, University Hospital of Liège, 4000 Liège, Belgium.
Marie ThysDepartment of Medico-Economic Information, University Hospital of Liège, 4000 Liège, Belgium.
Monique HenketDepartment of Pneumology, University Hospital of Liège, 4000 Liège, Belgium.
Anne-Noëlle FrixDepartment of Pneumology, University Hospital of Liège, 4000 Liège, Belgium.
Benoit ErnstDepartment of Pneumology, University Hospital of Liège, 4000 Liège, Belgium.ORCID 0000-0002-1096-049X
Sean WalshRadiomics (Oncoradiomics SA), 4000 Liège, Belgium.
Mariaelena OcchipintiRadiomics (Oncoradiomics SA), 4000 Liège, Belgium.
Wim VosRadiomics (Oncoradiomics SA), 4000 Liège, Belgium.ORCID 0000-0003-0827-421X
Nathalie MaesBiostatistics and Medico-Economic Information Department, University Hospital of Liège, 4000 Liège, Belgium.ORCID 0000-0003-2552-2422
Jean Luc CanivetDepartment of Intensive Unit Care, University Hospital of Liège, 4000 Liège, Belgium.
Renaud LouisDepartment of Pneumology, University Hospital of Liège, 4000 Liège, Belgium.
Paul MeunierDepartment of Radiology, University Hospital of Liège, 4000 Liège, Belgium.
Julien GuiotDepartment of Pneumology, University Hospital of Liège, 4000 Liège, Belgium.ORCID 0000-0001-7800-1730
University of Liège · BE

Funding

The European Union's Horizon 2020 research and innovation program 101016131The European Union's Horizon 2020 research and innovation program and EFPIAThe Innovative Medicines Initiative 2 Joint Undertaking (JU) 101005122The University Hospital of Liège
6 · The paper itself

Abstract

During the COVID-19 pandemic induced by the SARS-CoV-2, numerous chest scans were carried out in order to establish the diagnosis, quantify the extension of lesions but also identify the occurrence of potential pulmonary embolisms. In this perspective, the performed chest scans provided a varied database for a retrospective analysis of non-COVID-19 chest pathologies discovered de novo. The fortuitous discovery of de novo non-COVID-19 lesions was generally not detected by the automated systems for COVID-19 pneumonia developed in parallel during the pandemic and was thus identified on chest CT by the radiologist. The objective is to use the study of the occurrence of non-COVID-19-related chest abnormalities (known and unknown) in a large cohort of patients having suffered from confirmed COVID-19 infection and statistically correlate the clinical data and the occurrence of these abnormalities in order to assess the potential of increased early detection of lesions/alterations. This study was performed on a group of 362 COVID-19-positive patients who were prescribed a CT scan in order to diagnose and predict COVID-19-associated lung disease. Statistical analysis using mean, standard deviation (SD) or median and interquartile range (IQR), logistic regression models and linear regression models were used for data analysis. Results were considered significant at the 5% critical level (p < 0.05). These de novo non-COVID-19 thoracic lesions detected on chest CT showed a significant prevalence in cardiovascular pathologies, with calcifying atheromatous anomalies approaching nearly 35.4% in patients over 65 years of age. The detection of non-COVID-19 pathologies was mostly already known, except for suspicious nodule, thyroid goiter and the ascending thoracic aortic aneurysm. The presence of vertebral compression or signs of pulmonary fibrosis has shown a significant impact on inpatient length of stay. The characteristics of the patients in this sample, both from a demographic and a tomodensitometric point of view on non-COVID-19 pathologies, influenced the length of hospital stay as well as the risk of intra-hospital death. This retrospective study showed that the potential importance of the detection of these non-COVID-19 lesions by the radiologist was essential in the management and the intra-hospital course of the patients.

Indexed as

COVID-19HRCTscreening

Identifiers

PMID35885473
PMCPMC9324631
OpenAlexW4283693714

What OpenQuestion holds

Textmetadata
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.