Evidence map›Paper›PMID 36117964›Full record

ArticleFrontiers in medicine2022

Improvement of an interobserver agreement of ARDS diagnosis by adding additional imaging and a confidence scale.

Laura A Hagens, Fleur L I M Van der Ven, Nanon F L Heijnen, Marry R Smit, Hester A Gietema, Suzanne C Gerretsen, Marcus J Schultz, Dennis C J J Bergmans, Ronny M Schnabel, Lieuwe D J Bos

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.6field-weighted citation impact, top 6% 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

7 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Observational
  3. Observational
  4. Article
  5. Observational
  6. Improving Acute Respiratory Distress Syndrome Diagnosis: Is Lung Ultrasound the Answer?American journal of respiratory and critical care medicine · 2023
    Article
  7. 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

10 authors at 5 institutions in 4 countries.

Laura A HagensDepartment of Intensive Care, Amsterdam University Medical Center, Location Amsterdam Medical Center, University of Amsterdam, Amsterdam, Netherlands.
Fleur L I M Van der VenDepartment of Intensive Care, Amsterdam University Medical Center, Location Amsterdam Medical Center, University of Amsterdam, Amsterdam, Netherlands.
Nanon F L HeijnenDepartment of Intensive Care, Maastricht University Medical Centre+, Maastricht, Netherlands.
Marry R SmitDepartment of Intensive Care, Amsterdam University Medical Center, Location Amsterdam Medical Center, University of Amsterdam, Amsterdam, Netherlands.
Hester A GietemaDepartment of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, Netherlands.
Suzanne C GerretsenDepartment of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, Netherlands.
Marcus J SchultzDepartment of Intensive Care, Amsterdam University Medical Center, Location Amsterdam Medical Center, University of Amsterdam, Amsterdam, Netherlands.
Dennis C J J BergmansDepartment of Intensive Care, Maastricht University Medical Centre+, Maastricht, Netherlands.
Ronny M SchnabelDepartment of Intensive Care, Maastricht University Medical Centre+, Maastricht, Netherlands.
Lieuwe D J BosDepartment of Intensive Care, Amsterdam University Medical Center, Location Amsterdam Medical Center, University of Amsterdam, Amsterdam, Netherlands.
Maastricht University Medical Centre · NLAmsterdam University Medical Centers · NLUniversity of Amsterdam · NLMaastricht University · NLMahidol University · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute respiratory distress syndrome (ARDS) often is not recognized in clinical practice, largely due to variation in the interpretation of chest x-ray (CXR) leading to poor interobserver reliability. We hypothesized that the agreement in the interpretation of chest imaging for the diagnosis of ARDS in invasively ventilated intensive care unit patients between experts improves when using an 8-grade confidence scale compared to using a dichotomous assessment and that the agreement increases after adding chest computed tomography (CT) or lung ultrasound (LUS) to CXR. Three experts scored ARDS according to the Berlin definition based on case records from an observational cohort study using a dichotomous assessment and an 8-grade confidence scale. The intraclass correlation (ICC), imaging modality, and the scoring method were calculated per day and compared using bootstrapping. A consensus judgement on the presence of ARDS was based on the combined confidence grades of the experts, followed by a consensus meeting for conflicting scores. In total, 401 patients were included in the analysis. The best ICC was found using an 8-grade confidence scale for LUS (ICC: 0.49; 95%-CI: 0.29-0.63) and CT evaluation (ICC: 0.49; 95%-CI: 0.34-0.61). The ICC of CXR increased by 0.022 and of CT by 0.065 when 8-grade scoring was used instead of the dichotomous assessment. Adding information from LUS or chest CT increased the ICC by 0.25 when using the 8-grade confidence assessment. An agreement on the diagnosis of ARDS can increase substantially by adapting the scoring system from a dichotomous assessment to an 8-grade confidence scale and by adding additional imaging modalities such as LUS or chest CT. This suggests that a simple assessment of the diagnosis of ARDS with a chart review by one assessor is insufficient to define ARDS in future studies. Clinical trial registration: Trialregister.nl (identifier NL8226).

Indexed as

ARDSchest X-rayconfidenceCTdiagnosisimaging

Identifiers

PMID36117964
PMCPMC9473335
OpenAlexW4293715466

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LicenceCC BY
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Registered trials

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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.