Evidence map›Paper›PMID 34015025›Full record

ArticlePloS one2021

Value of quantitative analysis in lung computed tomography in patients severely ill with COVID-19.

Marta Rorat, Tomasz Jurek, Krzysztof Simon, Maciej Guziński

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

13 citing papers in PubMed, 20 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Marta RoratDepartment of Forensic Medicine, Wroclaw Medical University, Wroclaw, Poland.ORCID 0000-0002-5318-4945
Tomasz JurekDepartment of Forensic Medicine, Wroclaw Medical University, Wroclaw, Poland.ORCID 0000-0003-0110-0276
Krzysztof SimonDepartment of Infectious Diseases and Hepatology, Wroclaw Medical University, Wroclaw, Poland.
Maciej GuzińskiDepartment of General Radiology, Interventional Radiology and Neuroradiology, Wroclaw Medical University, Wroclaw, Poland.ORCID 0000-0002-9781-2114
Wroclaw Medical University · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionQuantitative computed tomography (QCT) is used to objectively assess the degree of parenchymal impairment in COVID-19 pneumonia. MATERIALS AND

methodsRetrospective study on 61 COVID-19 patients (severe and non-severe; 33 men, age 63+/-15 years) who underwent a CT scan due to tachypnea, dyspnoea or desaturation. QCT was performed using VCAR software. Patients' clinical data was collected, including laboratory results and oxygenation support. The optimal cut-off point for CT parameters for predicting death and respiratory support was performed by maximizing the Youden Index in a receiver operating characteristic (ROC) curve analysis.

resultsThe analysis revealed significantly greater progression of changes: ground-glass opacities (GGO) (31,42% v 13,89%, p<0.001), consolidation (11,85% v 3,32%, p<0.001) in patients with severe disease compared to non-severe disease. Five lobes were involved in all patients with severe disease. In non-severe patients, a positive correlation was found between severity of GGO, consolidation and emphysema and sex, tachypnea, chest x-ray (CXR) score on admission and laboratory parameters: CRP, D-dimer, ALT, lymphocyte count and lymphocyte/neutrophil ratio. In the group of severe patients, a correlation was found between sex, creatinine level and death. ROC analysis on death prediction was used to establish the cut-off point for GGO at 24.3% (AUC 0.8878, 95% CI 0.7889-0.9866; sensitivity 91.7%, specificity 75.5%), 5.6% for consolidation (AUC 0.7466, 95% CI 0.6009-0.8923; sensitivity 83.3%, specificity 59.2%), and 37.8% for total (GGO+consolidation) (AUC 0.8622, 95% CI 0.7525-0.972; sensitivity 75%, specificity 83.7%). The cut-off point for predicting respiratory support was established for GGO at 18.7% (AUC 0.7611, 95% CI 0.6268-0.8954; sensitivity 87.5%, specificity 64.4%), consolidation at 3.88% (AUC 0.7438, 95% CI 0.6146-0.8729; sensitivity 100%, specificity 46.7%), and total at 23.5% (AUC 0.7931, 95% CI 0.673-0.9131; sensitivity 93.8%, specificity 57.8%).

conclusionQCT is a good diagnostic tool which facilitates decision-making regarding intensification of oxygen support and transfer to an intensive care unit in patients severely ill with COVID-19 pneumonia. QCT can make an independent and simple screening tool to assess the risk of death, regardless of clinical symptoms. Usefulness of QCT to predict the risk of death is higher than to assess the indications for respiratory support.

Indexed as

AgedCOVID-19Disease ProgressionFemaleHumansLungMaleMiddle AgedPrognosisRetrospective StudiesSeverity of Illness IndexTomography, X-Ray Computed

Identifiers

PMID34015025
PMCPMC8136668
OpenAlexW3162149061

What OpenQuestion holds

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Registered trials

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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.