Evidence map›Paper›PMID 32766567›Full record

ArticleCritical care explorations2020

Point-of-Care Lung Ultrasound for Detecting Severe Presentations of Coronavirus Disease 2019 in the Emergency Department: A Retrospective Analysis.

Mark Favot, Adrienne Malik, Jonathan Rowland, Brian Haber, Robert Ehrman, Nicholas Harrison

Open access · goldAbstract read
In one paragraph

Article in Critical care explorations, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
0.9field-weighted citation impact, top 23% 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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Observational
  5. Article
  6. 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

6 authors at 1 institution in 2 countries.

Mark FavotAll authors: Department of Emergency Medicine, Wayne State University School of Medicine, Detroit, MI.
Adrienne MalikAll authors: Department of Emergency Medicine, Wayne State University School of Medicine, Detroit, MI.
Jonathan RowlandAll authors: Department of Emergency Medicine, Wayne State University School of Medicine, Detroit, MI.
Brian HaberAll authors: Department of Emergency Medicine, Wayne State University School of Medicine, Detroit, MI.
Robert EhrmanAll authors: Department of Emergency Medicine, Wayne State University School of Medicine, Detroit, MI.
Nicholas HarrisonAll authors: Department of Emergency Medicine, Wayne State University School of Medicine, Detroit, MI.
Apple (Israel) · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAnalyze the diagnostic test characteristics of point-of-care lung ultrasound for patients suspected to have novel coronavirus disease 2019.

designRetrospective cohort.

settingTwo emergency departments in Detroit, Michigan, United States, during a local coronavirus disease 2019 outbreak (March 2020 to April 2020). PATIENTS: Emergency department patients receiving lung ultrasound for clinical suspicion of coronavirus disease 2019 during the study period.

interventionsNone, observational analysis only. MEASUREMENTS AND MAIN

resultsBy a reference standard of serial reverse transcriptase-polymerase chain reactions, 42 patients were coronavirus disease 2019 positive, 16 negative, and eight untested (test results lost, died prior to testing, and/or did not meet hospital guidelines for rationing of reverse transcriptase-polymerase chain reaction tests). Thirty-three percent, 44%, 38%, and 17% had mortality, ICU admission, intubation, and venous or arterial thromboembolism, respectively. Receiver operating characteristics, area under the curve, sensitivity, and specificity with 95% CIs were calculated for five lung ultrasound patterns coded by a blinded reviewer and chest radiograph. Chest radiograph had area under the curve = 0.66 (95% CI, 0.54-0.79), 74% sensitivity (95% CI, 48-93%), and 53% specificity (95% CI, 32-75%). Two lung ultrasound patterns had a statistically significant area under the curve: symmetric bilateral pulmonary edema (area under the curve, 0.57; 95% CI, 0.50-0.64), and a nondependent bilateral pulmonary edema pattern (edema in superior lung ≥ inferior lung and no pleural effusion; area under the curve, 0.73; 95% CI, 0.68-0.90). Chest radiograph plus the nondependent bilateral pulmonary edema pattern showed a statistically improved area under the curve (0.80; 95% CI, 0.68-0.90) compared to either alone, but at the ideal cutoff had sensitivity and specificity equivalent to nondependent bilateral pulmonary edema only (69% and 77%, respectively). The strongest combination of clinical, chest radiograph, and lung ultrasound factors for diagnosis was nondependent bilateral pulmonary edema pattern with temperature and oxygen saturation (area under the curve, 0.86; 95% CI, 0.76-0.94; sensitivity = 77% [58-93%]; specificity = 76% [53-94%] at the ideal cutoff), which was superior to chest radiograph alone.

conclusionsLung ultrasound diagnosed severe presentations of coronavirus disease 2019 with similar sensitivity to chest radiograph, CT, and reverse transcriptase-polymerase chain reaction (on first testing) and improved specificity compared to chest radiograph. Diagnostically useful lung ultrasound patterns differed from those hypothesized by previous, nonanalytical, reports (case series and expert opinion), and should be evaluated in a rigorous prospective study.

Indexed as

COVID-19diagnosisemergency departmentpoint of caresensitivity and specificityultrasound

Identifiers

PMID32766567
PMCPMC7402420
OpenAlexW3047522720

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.