Evidence map›Paper›PMID 33217674›Full record

SynthesisClinical imaging2021

Diagnostic accuracy of CT and radiographic findings for novel coronavirus 2019 pneumonia: Systematic review and meta-analysis.

Mandeep Garg, Pankaj Gupta, Muniraju Maralakunte, Praveen Kumar-M, Anindita Sinha, Mandeep Kang, Ritesh Agarwal, Manavjit Singh Sandhu

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Clinical imaging, 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
–field-weighted citation impact
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.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Covid-19 imaging: A narrative review.Annals of medicine and surgery (2012) · 2021
    Review
  10. Review
  11. The Conundrum of 'Long-COVID-19': A Narrative Review.International journal of general medicine · 2021
    Review
  12. Article
  13. 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

8 authors.

Mandeep GargDepartment of Radiodiagnosis and Pulmonary Medicine, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India; Department of Radiodiagnosis.
Pankaj GuptaDepartment of Radiodiagnosis and Pulmonary Medicine, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India; Department of Radiodiagnosis. Electronic address: Pankajgupta959@gmail.com.
Muniraju MaralakunteDepartment of Radiodiagnosis and Pulmonary Medicine, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India; Department of Radiodiagnosis.
Praveen Kumar-MDepartment of Radiodiagnosis and Pulmonary Medicine, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India; Department of Pharmacology.
Anindita SinhaDepartment of Radiodiagnosis and Pulmonary Medicine, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India; Department of Radiodiagnosis.
Mandeep KangDepartment of Radiodiagnosis and Pulmonary Medicine, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India; Department of Radiodiagnosis.
Ritesh AgarwalDepartment of Radiodiagnosis and Pulmonary Medicine, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India; Department of Pulmonary Medicine.
Manavjit Singh SandhuDepartment of Radiodiagnosis and Pulmonary Medicine, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India; Department of Radiodiagnosis.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate pooled prevalence, sensitivity, and specificity of chest computed tomography (CT) and radiographic findings for novel coronavirus-2019 (COVID-19) pneumonia.

methodsWe performed systematic literature search in PubMed and Embase to identify articles reporting baseline imaging findings of COVID-19 pneumonia. The quality of the articles was assessed using NIH quality assessment tool for case series studies. The pooled prevalence, sensitivity, specificity, and diagnostic odds ratio of imaging findings were calculated.

resultsFifty-six studies (6007 patients, age, 2.1-70 years, 2887 females, 5762 CT, 396 radiographs,) were included. The mean interval between onset of symptoms and CT acquisition was 1-8 days. On CT, the pooled prevalence of ground glass opacities (GGO), GGO plus consolidation, and consolidation only was 66.9% (95% CI 60.8-72.4%), 44.9% (38.7-51.3%), and 32.1 (23.6-41.9%) respectively. Pooled sensitivity and specificity of GGO on CT was 73% (71%-80%) and 61% (41%-78%), respectively. For GGO plus consolidation and consolidation only, the pooled sensitivities/ specificities were 58% (48%-68%)/ 58% (41%-73%) and 49% (20%-78%)/ 56% (30%-78%), respectively. The pooled prevalence of GGO and consolidation on chest radiograph was 38.7% (22.2%-58.3%) and 46.9% (29.7%-64.9%), respectively. The diagnostic accuracy of radiographic findings could not be assessed due to small number of studies.

conclusionGGO on CT has the highest diagnostic performance for COVID-19 pneumonia, followed by GGO plus consolidation and consolidation only. However, the moderate to low sensitivity and specificity suggest that CT should not be used as the primary tool for diagnosis. Chest radiographic abnormalities are seen in half of the patients.

Indexed as

CoronavirusCoronavirus InfectionsCOVID-19Tomography, X-Ray ComputedAdolescentAdultAgedChildChild, PreschoolCOVID-19 TestingFemaleHumansLungMiddle AgedSARS-CoV-2Young AdultCovid-19ImagingSystematic review

Identifiers

PMID33217674
PMCPMC7657023

What OpenQuestion holds

Textmetadata
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.