Evidence map›Paper›PMID 33090353›Full record

ArticleInternal and emergency medicine2021

The role of lung ultrasound as a frontline diagnostic tool in the era of COVID-19 outbreak.

Cristina Sorlini, Marco Femia, Giovanni Nattino, Pietro Bellone, Elisa Gesu, Paolo Francione, Michele Paternò, Pasquale Grillo, Aurora Ruffino, Guido Bertolini and 3 more

Open access · bronzeAbstract read
In one paragraph

Article in Internal and emergency medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 4 pooled it
5.1field-weighted citation impact, top 4% 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

30 citing papers in PubMed, 4 syntheses or guidelines pooled it, 54 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Thoracic imaging tests for the diagnosis of COVID-19.The Cochrane database of systematic reviews · 2022
    Pooled it
  4. Pooled it
  5. Observational
  6. Article
  7. Observational
  8. Article
  9. Review
  10. Article
  11. Review
  12. A Review of the Machine Learning Algorithms for Covid-19 Case Analysis.IEEE transactions on artificial intelligence · 2023
    Article
  13. Review
  14. Article
  15. Review
  16. From the Triage to the Intermediate Area: A Simple and Fast Model for COVID-19 in the Emergency Department.International journal of environmental research and public health · 2022
    Article
  17. Observational
  18. Article
  19. Review
  20. Review
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

13 authors at 3 institutions in 1 country.

Cristina SorliniAccident and Emergency Services, ASST Santi Paolo e Carlo, Via Pio II 3, 20153, Milan, Italy.ORCID http://orcid.org/0000-0002-7535-102X
Marco FemiaDepartment of Advanced Diagnostic-Therapeutic Technologies, ASST Santi Paolo e Carlo, Via Pio II 3, 20153, Milan, Italy.ORCID http://orcid.org/0000-0003-0698-0620
Giovanni NattinoLaboratory of Clinical Epidemiology, Department of Public Health, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via G.B. Camozzi 3, 24020, Ranica, BG, Italy. giovanni.nattino@marionegri.it.ORCID http://orcid.org/0000-0002-3034-6251
Pietro BellonePostgraduation School in Emergency Medicine, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy.
Elisa GesuPostgraduation School in Emergency Medicine, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy.
Paolo FrancionePostgraduation School in Internal Medicine, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy.
Michele PaternòPostgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy.
Pasquale GrilloPostgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy.
Aurora RuffinoPostgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy.
Guido BertoliniLaboratory of Clinical Epidemiology, Department of Public Health, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via G.B. Camozzi 3, 24020, Ranica, BG, Italy.ORCID http://orcid.org/0000-0002-3101-4259
Maurizio CariatiDepartment of Advanced Diagnostic-Therapeutic Technologies, ASST Santi Paolo e Carlo, Via Pio II 3, 20153, Milan, Italy.
Francesca CortellaroAccident and Emergency Services, ASST Santi Paolo e Carlo, Via Pio II 3, 20153, Milan, Italy.
Fenice Network (Italian Group for Clinical Research in Emergency Medicine)
University of Milan · ITAzienda Socio Sanitaria Territoriale Santi Paolo e CarloMario Negri Institute for Pharmacological Research · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The diffusion of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) worldwide prompted the World Health Organization to declare the status of pandemic. The molecular diagnosis of SARS-CoV-2 infection is based on the detection of viral RNA on different biological specimens. Unfortunately, the test may require several hours to be performed. In the present study, we evaluated the diagnostic accuracy of lung point-of-care ultrasound (POCUS) for SARS-CoV-2 pneumonia in a cohort of symptomatic patients admitted to one emergency department (ED) in a high-prevalence setting. This retrospective study enrolled all patients who visited one ED with suspected respiratory infection in March 2020. All the patients were tested (usually twice if the first was negative) for SARS-CoV-2 on ED admission. The reference standard was considered positive if at least one specimen was positive. If all the specimens tested negative, the reference was considered negative. Diagnostic accuracy was evaluated using sensitivity, specificity, and positive and negative predictive value. Of the 444 symptomatic patients who were admitted to the ED in the study period, the result of the lung POCUS test was available for 384 (86.5%). The sensitivity of the test was 92.0% (95% CI 88.2-94.9%), and the specificity was 64.9% (95% CI 54.6-74.4%). We observed a prevalence of SARS-CoV-2 infection of 74.7%. In this setting, the positive and negative predicted values were 88.6% (95% CI 84.4-92.0) and 73.3% (95% CI 62.6-82.2%), respectively. Lung POCUS is a sensitive first-line screening tool for ED patients presenting with symptoms suggestive of SARS-CoV-2 infection.

Indexed as

AgedCOVID-19COVID-19 TestingDiagnosis, DifferentialEmergency Service, HospitalFemaleHumansItalyMaleMiddle AgedPandemicsPneumonia, ViralPoint-of-Care SystemsSARS-CoV-2Sensitivity and SpecificityUltrasonographyCOVID-19Diagnostic testPoint-of-care ultrasoundSARS-CoV-2SensitivitySpecificity

Identifiers

PMID33090353
PMCPMC7579896
OpenAlexW3093611748

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.