Evidence map›Paper›PMID 28710662›Full record

Observational studyJournal of clinical monitoring and computing2018

Lung real time three-dimensional imaging in critically ill ventilated patients: a global diagnosis concordance study.

Jean-Marc Le Goff, Claire Roger, Benjamin Louart, Pierre Géraud Claret, Aurélien Daurat, Stéphanie Bulyez, Jean-Yves Lefrant, Xavier Bobbia, Laurent Muller

Abstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in Journal of clinical monitoring and computing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

9 authors at 3 institutions in 1 country.

Jean-Marc Le GoffDivision of Anaesthesia Intensive Care, Pain and Emergency Medicine, University Hospital of Nîmes, Place du Professeur Robert Debré, 30029, Nîmes, France.
Claire RogerDivision of Anaesthesia Intensive Care, Pain and Emergency Medicine, University Hospital of Nîmes, Place du Professeur Robert Debré, 30029, Nîmes, France.
Benjamin LouartDivision of Anaesthesia Intensive Care, Pain and Emergency Medicine, University Hospital of Nîmes, Place du Professeur Robert Debré, 30029, Nîmes, France.
Pierre Géraud ClaretDivision of Anaesthesia Intensive Care, Pain and Emergency Medicine, University Hospital of Nîmes, Place du Professeur Robert Debré, 30029, Nîmes, France.
Aurélien DauratDivision of Anaesthesia Intensive Care, Pain and Emergency Medicine, University Hospital of Nîmes, Place du Professeur Robert Debré, 30029, Nîmes, France.
Stéphanie BulyezDivision of Anaesthesia Intensive Care, Pain and Emergency Medicine, University Hospital of Nîmes, Place du Professeur Robert Debré, 30029, Nîmes, France.
Jean-Yves LefrantDivision of Anaesthesia Intensive Care, Pain and Emergency Medicine, University Hospital of Nîmes, Place du Professeur Robert Debré, 30029, Nîmes, France. jean-yves.lefrant@wanadoo.fr.
Xavier BobbiaDivision of Anaesthesia Intensive Care, Pain and Emergency Medicine, University Hospital of Nîmes, Place du Professeur Robert Debré, 30029, Nîmes, France.
Laurent MullerDivision of Anaesthesia Intensive Care, Pain and Emergency Medicine, University Hospital of Nîmes, Place du Professeur Robert Debré, 30029, Nîmes, France.
Centre Hospitalier Universitaire de Nîmes · FRUniversité de Nîmes · FRCentre Hospitalier Universitaire de Clermont-Ferrand · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung ultrasound (LUS) increases clinical diagnosis performance in intensive care unit (ICU). Real-time three-dimensional (3-D) imaging was compared with two-dimensional (2-D) LUS by assessing the global diagnosis concordance. In this single center, prospective, observational, pilot study, one trained operator performed a 3-D LUS immediately after a 2-D LUS in eight areas of interest on the same areas in 16 ventilated critically ill patients. All cine loops were recorded on a computer without visible link between 2-D and 3-D exams. Two experts blindly reviewed cine loops. Four main diagnoses were proposed: normal lung, consolidation, pleural effusion and interstitial syndrome. Fleiss κ and Cohen's κ values were calculated. In 252 LUS cine loops, the concordance between 2-D and 3-D exams was 83.3% (105/126), 77.6% (99/126) and 80.2% (101/126) for the trained operator and the experts respectively. The Cohen's κ coefficient value was 0.69 [95% Confidence Interval (CI) 0.58-0.80] for expert 1 meaning a substantial agreement. The inter-rater reliability was very good (Fleiss' κ value = 0.94 [95% CI 0.87-1.0]) for 3-D exams. The Cohen's κ was excellent for pleural effusion (κ= 0.93 [95% CI 0.76-1.0]), substantial for normal lung diagnosis (κ = 0.68 [95% CI 0.51-0.86]) and interstitial syndrome (κ = 0.62 [95% CI 0.45-0.80]) and fair for consolidation diagnoses (κ = 0.47 [95% CI 0.30-0.64]). In ICU ventilated patients, there was a substantial concordance between 2-D and 3-D LUS with a good inter-rater reliability. However, the diagnosis concordance for lung consolidation is poor.

Indexed as

AdultAgedAlgorithmsComputer SystemsCritical CareCritical IllnessDiagnosis, Computer-AssistedFemaleHumansImaging, Three-DimensionalIntensive Care UnitsLungMaleMiddle AgedObserver VariationPilot Projects3-Dimensional ultrasonographyConcordanceICULung ultrasoundLUSPilot study

Identifiers

PMID28710662
OpenAlexW2734381367

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.