Evidence map›Paper›PMID 34302561›Full record

ArticleEmergency radiology2021

Diagnostic accuracy and inter-observer agreement with the CO-RADS lexicon for CT chest reporting in COVID-19.

Anirudh Venugopalan Nair, Matthew McInnes, Bamil Jacob, Devendra Kumar, Deepak Kuttikatt Soman, Hanee Subair Valiyakath Subair, Parag Suresh Mahajan, Mohammed Ameer Hamza Shah, Mohammed Ali Saleh Sabawi, Mahmoud Al-Heidous

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Article in Emergency radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 15% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 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

10 authors at 3 institutions in 3 countries.

Anirudh Venugopalan NairDept of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar. dranirudhnair@gmail.com.ORCID http://orcid.org/0000-0001-7662-7668
Matthew McInnesThe Ottawa Hospital, University of Ottawa, Ontario, Canada.
Bamil JacobDept of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar.
Devendra KumarDept of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar.
Deepak Kuttikatt SomanDept of Community Medicine, Government Medical College, Palakkad, India.
Hanee Subair Valiyakath SubairDept of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar.
Parag Suresh MahajanDept of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar.
Mohammed Ameer Hamza ShahDept of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar.
Mohammed Ali Saleh SabawiDept of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar.
Mahmoud Al-HeidousDept of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar.
Hamad Medical Corporation · QAGovernment Medical College · INUniversity of Ottawa · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo measure the diagnostic accuracy and inter-observer agreement with the use of COVID-19 Reporting and Data System (CO-RADS) for detection of COVID-19 on CT chest imaging.

methodsThis retrospective study included 164 consecutive patients with clinical suspicion of COVID-19 in whom a CT chest examination was performed at a single institution between April 2020 and July 2020. Of them, 101 patients was RT-PCR positive for COVID-19. Six readers with varying radiological experience (two each of chest radiologists, general radiologists, and radiologists in training) independently assigned a CO-RADS assessment category for each CT chest study. The Fleiss' K was used to quantify inter-observer agreement. The inter-observer agreement was also assessed based on the duration of onset of symptoms to CT scan. ROC curve analysis was used to determine the diagnostic accuracy of CO-RADS. The area under curve was calculated to determine the reader accuracy for detection of COVID-19 lung involvement with RT-PCR as reference standards. The data sets were plotted in ROC space, and Youden's J statistic was calculated to determine the threshold cut-off CO-RADS category for COVID-19 positivity.

resultsThere was overall moderate inter-observer agreement between all readers (Fleiss' K 0.54 [95% CI 0.54, 0.54]), with substantial agreement among chest radiologists (Fleiss' K 0.68 [95% CI 0.67, 0.68]), general radiologists (Fleiss' K 0.61 [95% CI 0.61, 0.61]), and moderate agreement among radiologists-in-training (Fleiss' K 0.56 [95% CI 0.56, 0.56]). There was overall moderate inter-observer agreement in early disease (stages 1 and 2), with cumulative Fleiss' K 0.45 [95% CI 0.45, 0.45]). The overall AUC for CO-RADS lexicon scheme to accurately diagnose COVID-19 yielded 0.92 (95% CI 0.91, 0.94) with strong concordance within and between groups, of chests radiologists with AUC of 0.91 (95% CI 0.88, 0.94), general radiologists with AUC 0.96 (95% CI 0.94, 0.98), and radiologists in training with AUC of 0.90 (95% CI 0.87, 0.94). For detecting COVID-19, ROC curve analysis yielded CO-RADS > 3 as the cut-off threshold with sensitivity 90% (95% CI 0.88, 0.93), and specificity of 87% (95% CI 0.83, 0.91).

conclusionReaders across different levels of experience could accurately identify COVID-19 positive patients using the CO-RADS lexicon with moderate inter-observer agreement and high diagnostic accuracy.

Indexed as

COVID-19HumansObserver VariationRetrospective StudiesSARS-CoV-2Tomography, X-Ray ComputedCO-RADSCOVID-19SARS-CoV-2

Identifiers

PMID34302561
PMCPMC8308071
OpenAlexW3186112344

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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.