Evidence map›Paper›PMID 36305900›Full record

ArticleEuropean radiology2023

CT-based diagnostic algorithm to identify bowel and/or mesenteric injury in patients with blunt abdominal trauma.

Alexandre Lansier, Camille Bourillon, Charles-André Cuénod, Emilia Ragot, Arnaud Follin, Sophie Hamada, Olivier Clément, Philippe Soyer, Anne-Sophie Jannot

Open access · greenAbstract read
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In one paragraph

Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.7field-weighted citation impact, top 30% 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

8 citing papers in PubMed, 5 citations in OpenAlex.

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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.

Alexandre LansierDepartment of Medical Imaging, Hôpital Européen Georges Pompidou, AP-HP, 20 rue Leblanc, 75015, Paris, France. alexandre.lansier@gmail.com.ORCID http://orcid.org/0000-0002-3499-4467
Camille BourillonDepartment of Medical Imaging, Hôpital de la Croix Saint Simon, 75020, Paris, France.
Charles-André CuénodDepartment of Medical Imaging, Hôpital Européen Georges Pompidou, AP-HP, 20 rue Leblanc, 75015, Paris, France.
Emilia RagotDepartment of Visceral Surgery, Hôpital Européen Georges Pompidou, AP-HP, 75015, Paris, France.
Arnaud FollinDepartment of Intensive Care, Hôpital Européen Georges Pompidou, AP-HP, 75015, Paris, France.
Sophie HamadaDepartment of Intensive Care, Hôpital Européen Georges Pompidou, AP-HP, 75015, Paris, France.
Olivier ClémentDepartment of Medical Imaging, Hôpital Européen Georges Pompidou, AP-HP, 20 rue Leblanc, 75015, Paris, France.
Philippe SoyerFaculté de Médecine, Université Paris Cité, 75006, Paris, France.
Anne-Sophie JannotFaculté de Médecine, Université Paris Cité, 75006, Paris, France.
Assistance Publique – Hôpitaux de Paris · FRUniversité Paris Cité · FRHôpital de la Croix-Rousse · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo develop a CT-based algorithm and evaluate its performance for the diagnosis of blunt bowel and/or mesenteric injury (BBMI) in patients with blunt abdominal trauma.

methodsThis retrospective study included a training cohort of 79 patients (29 with BBMI and 50 patients with blunt abdominal trauma without BBMI) and a validation cohort of 37 patients (13 patients with BBMI and 24 patients with blunt abdominal trauma without BBMI). CT examinations were blindly analyzed by two independent radiologists. For each CT sign, the kappa value, sensitivity, specificity, and accuracy were calculated. A diagnostic algorithm was built using a recursive partitioning model on the training cohort, and its performances were assessed on the validation cohort.

resultsCT signs with kappa value > 0.6 were extraluminal gas, hemoperitoneum, no or moderate bowel wall enhancement, and solid organ injury. CT signs yielding best accuracies in the training cohort were extraluminal gas (98%; 95% CI: 91-100), bowel wall defect (97%; 95% CI: 91-100), irregularity of mesenteric vessels (97%; 95% CI: 90-99), and mesenteric vessel extravasation (97%; 95% CI: 90-99). Using a recursive partitioning model, a decision tree algorithm including extraluminal gas and no/moderate bowel wall enhancement was built, achieving 86% sensitivity (95% CI: 74-99) and 96% specificity (95% CI: 91-100) in the training cohort and 92% sensitivity (95% CI: 78-97) and 88% specificity (95% CI: 74-100) in the validation cohort for the diagnosis of BBMI.

conclusionsAn effective diagnostic algorithm was built to identify BBMI in patients with blunt abdominal trauma using only extraluminal gas and no/moderate bowel wall enhancement on CT examination. KEY POINTS: • A CT diagnostic algorithm that included extraluminal gas and no/moderate bowel wall enhancement was built for the diagnosis of surgical blunt bowel and/or mesenteric injury. • A decision tree combining only two reproducible CT signs has high diagnostic performance for the diagnosis of surgical blunt bowel and/or mesenteric injury.

Indexed as

Abdominal InjuriesWounds, NonpenetratingAlgorithmsHumansIntestinesMesenteryRetrospective StudiesTomography, X-Ray ComputedAbdominal injuriesLower gastrointestinal tractMesenteryTomography, X-ray computedWounds, nonpenetrating

Identifiers

PMID36305900
OpenAlexW4307455441

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.