Evidence map›Paper›PMID 39487874›Full record

ArticleIntensive care medicine experimental2024

Quantification of pulmonary edema using automated lung segmentation on computed tomography in mechanically ventilated patients with acute respiratory distress syndrome.

Alice Marguerite Conrad, Julia Zimmermann, David Mohr, Matthias F Froelich, Alexander Hertel, Nils Rathmann, Christoph Boesing, Manfred Thiel, Stefan O Schoenberg, Joerg Krebs and 3 more

Abstract read
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In one paragraph

Article in Intensive care medicine experimental, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Observational
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.

Alice Marguerite Conrad *Department of Anesthesiology and Critical Care Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany.
Julia Zimmermann *Department of Anesthesiology and Critical Care Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany.
David MohrDepartment of Anesthesiology and Critical Care Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany.
Matthias F FroelichDepartment of Clinical Radiology and Nuclear Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany.
Alexander HertelDepartment of Clinical Radiology and Nuclear Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany.
Nils RathmannDepartment of Clinical Radiology and Nuclear Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany.
Christoph BoesingDepartment of Anesthesiology and Critical Care Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany.
Manfred ThielDepartment of Anesthesiology and Critical Care Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany.
Stefan O SchoenbergDepartment of Clinical Radiology and Nuclear Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany.
Joerg KrebsDepartment of Anesthesiology and Critical Care Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany.
Thomas LueckeDepartment of Anesthesiology and Critical Care Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany.
Patricia R M RoccoLaboratory of Pulmonary Investigation, Centro de Ciências da Saúde, Carlos Chagas Filho Institute of Biophysics, Federal University of Rio de Janeiro, Avenida Carlos Chagas Filho, 373, Bloco G-014, Ilha Do Fundão, Rio de Janeiro, Brazil.
Matthias OttoDepartment of Anesthesiology and Critical Care Medicine, Faculty of Medicine, University Hospital Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68165, Mannheim, Germany. matthias.otto@umm.de.ORCID http://orcid.org/0000-0002-4635-8863

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundQuantification of pulmonary edema in patients with acute respiratory distress syndrome (ARDS) by chest computed tomography (CT) scan has not been validated in routine diagnostics due to its complexity and time-consuming nature. Therefore, the single-indicator transpulmonary thermodilution (TPTD) technique to measure extravascular lung water (EVLW) has been used in the clinical setting. Advances in artificial intelligence (AI) have now enabled CT images of inhomogeneous lungs to be segmented automatically by an intensive care physician with no prior radiology training within a relatively short time. Nevertheless, there is a paucity of data validating the quantification of pulmonary edema using automated lung segmentation on CT compared with TPTD.

methodsA retrospective study (January 2016 to December 2021) analyzed patients with ARDS, admitted to the intensive care unit of the Department of Anesthesiology and Critical Care Medicine, University Hospital Mannheim, who underwent a chest CT scan and hemodynamic monitoring using TPTD at the same time. Pulmonary edema was estimated using manually and automated lung segmentation on CT and then compared to the pulmonary edema calculated from EVLW determined using TPTD.

results145 comparative measurements of pulmonary edema with TPTD and CT were included in the study. Estimating pulmonary edema using either automated lung segmentation on CT or TPTD showed a low bias overall (- 104 ml) but wide levels of agreement (upper: 936 ml, lower: - 1144 ml). In 13% of the analyzed CT scans, the agreement between the segmentation of the AI algorithm and a dedicated investigator was poor. Manual segmentation and automated segmentation adjusted for contrast agent did not improve the agreement levels.

conclusionsAutomated lung segmentation on CT can be considered an unbiased but imprecise measurement of pulmonary edema in mechanically ventilated patients with ARDS.

Indexed as

ARDSAutomated lung segmentationComputed tomographyExtravascular lung waterPulmonary edemaTranspulmonary thermodilution

Identifiers

PMID39487874
PMCPMC11531458

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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.