Evidence map›Paper›PMID 34395930›Full record

ArticleHeliyon2021

Standardized reporting systems of chest computed tomography in a population with low coronavirus disease 2019 prevalence: A retrospective comparative study.

Ryo Kurokawa, Shohei Inui, Wataru Gonoi, Yudai Nakai, Masanori Ishida, Yusuke Watanabe, Takatoshi Kubo, Yosuke Amano, Koh Okamoto, Hidenori Kage and 6 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.2field-weighted citation impact, top 55% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. 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 2 institutions in 1 country.

Ryo KurokawaDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Shohei InuiDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Wataru GonoiDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Yudai NakaiDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Masanori IshidaDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Yusuke WatanabeDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Takatoshi KuboDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Yosuke AmanoDepartment of Respiratory Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Koh OkamotoDepartment of Infectious Diseases, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Hidenori KageDepartment of Respiratory Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Sohei HaradaDepartment of Infection Control and Prevention, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Goh TanakaDepartment of Respiratory Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Takuya KawaharaClinical Research Promotion Center, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Takahide NagaseDepartment of Respiratory Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Kyoji MoriyaDepartment of Infectious Diseases, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Osamu AbeDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
The University of Tokyo · JPUniversity of Tokyo Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo compare the diagnostic performance and interobserver agreement of three reporting systems for computed tomography findings in coronavirus disease 2019 (COVID-19), namely the COVID-19 Reporting and Data System (CO-RADS), COVID-19 Imaging Reporting and Data System (COVID-RADS), and Radiological Society of North America (RSNA) expert consensus statement, in a low COVID-19 prevalence area.

methodThis institutional review board approval single-institutional retrospective study included 154 hospitalized patients between April 1 and May 21, 2020; 26 (16.9 %; 63.2 ± 14.1 years, 21 men) and 128 (65.7 ± 16.4 years, 87 men) patients were diagnosed with and without COVID-19 according to reverse transcription-polymerase chain reaction results, respectively. Written informed consent was waived due to the retrospective nature of the study. Six radiologists independently classified chest computed tomography images according to each reporting system. The area under receiver operating characteristic curves, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and interobserver agreements were calculated and compared across the systems using paired t-test and kappa analysis.

resultsMean area under receiver operating characteristic curves were as follows: CO-RADS, 0.89 (95 % confidence interval [CI], 0.87-0.90); COVID-RADS, 0.78 (0.75-0.80); and RSNA expert consensus statement, 0.88 (0.86-0.90). Average kappa values across observers were 0.52 (95 % CI: 0.45-0.60), 0.51 (0.41-0.61), and 0.57 (0.49-0.64) for CO-RADS, COVID-RADS, and RSNA expert consensus statement, respectively. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were the highest at 0.71, 0.53, 0.72, 0.96, and 0.56 in the CO-RADS; 0.56, 0.31, 0.54, 0.95, and 0.35 in the COVID-RADS; 0.83, 0.49, 0.61, 0.96, and 0.55 in the RSNA expert consensus statement, respectively.

conclusionsThe CO-RADS exhibited the highest specificity, positive predictive value, which are especially important in a low-prevalence population, while maintaining high accuracy and negative predictive value, demonstrating the best performance in a low-prevalence population.

Indexed as

Chest computed tomographyCOVID-19Diagnostic performanceInterobserver agreement

Identifiers

PMID34395930
PMCPMC8353969
OpenAlexW3191295940

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.