Evidence map›Paper›PMID 28083698›Full record

ArticleEuropean radiology2017

Emergency assessment of patients with acute abdominal pain using low-dose CT with iterative reconstruction: a comparative study.

Pierre-Alexandre Poletti, Minerva Becker, Christoph D Becker, Alice Halfon Poletti, Olivier T Rutschmann, Habib Zaidi, Thomas Perneger, Alexandra Platon

Registry-linked trialAbstract readComparative Study
PubMed Publisher
In one paragraph

Article in European radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05651360 (Diagnostic Performance of Deep Learning Image Reconstruction in Low Dose CT for the Detection of Acute Abdominal Conditions), which is not on this map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
2.8field-weighted citation impact, top 10% 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.

NCT05651360 completednot on this mapstarted 2022, after this paper: background citation

Diagnostic Performance of Deep Learning Image Reconstruction in Low Dose CT for the Detection of Acute Abdominal Conditions

TypeobservationalSponsorOslo University HospitalRan2022 to 2023Enrolled246ConditionsAbdominal Pain, Acute Pain, Acute Cholecystitis, Acute AppendicitisArmslow-dose CT
3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Caecal diverticulitis can be misdiagnosed as acute appendicitis: a systematic review of the literature.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2021
    Pooled it
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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

8 authors at 1 institution in 1 country.

Pierre-Alexandre PolettiDepartment of Radiology, University Hospital of Geneva, 4 rue Gabrielle Perret-Gentil, 1211, Geneva, Switzerland. pierre-alexandre.poletti@hcuge.ch.
Minerva BeckerDepartment of Radiology, University Hospital of Geneva, 4 rue Gabrielle Perret-Gentil, 1211, Geneva, Switzerland.
Christoph D BeckerDepartment of Radiology, University Hospital of Geneva, 4 rue Gabrielle Perret-Gentil, 1211, Geneva, Switzerland.
Alice Halfon PolettiDepartment of Community, Primary Care and Emergency Medicine, University Hospital of Geneva, Geneva, Switzerland.
Olivier T RutschmannDepartment of Community, Primary Care and Emergency Medicine, University Hospital of Geneva, Geneva, Switzerland.
Habib ZaidiDepartment of Radiology, University Hospital of Geneva, 4 rue Gabrielle Perret-Gentil, 1211, Geneva, Switzerland.
Thomas PernegerDivision of Clinical Epidemiology, University Hospital of Geneva, Geneva, Switzerland.
Alexandra PlatonDepartment of Radiology, University Hospital of Geneva, 4 rue Gabrielle Perret-Gentil, 1211, Geneva, Switzerland.
University Hospital of Geneva · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo determine if radiation dose delivered by contrast-enhanced CT (CECT) for acute abdominal pain can be reduced to the dose administered in abdominal radiography (<2.5 mSv) using low-dose CT (LDCT) with iterative reconstruction algorithms.

methodsOne hundred and fifty-one consecutive patients requiring CECT for acute abdominal pain were included, and their body mass index (BMI) was calculated. CECT was immediately followed by LDCT. LDCT series was processed using 1) 40% iterative reconstruction algorithm blended with filtered back projection (LDCT-IR-FBP) and 2) model-based iterative reconstruction algorithm (LDCT-MBIR). LDCT-IR-FBP and LDCT-MBIR images were reviewed independently by two board-certified radiologists (Raters 1 and 2).

resultsAbdominal pathology was revealed on CECT in 120 (79%) patients. In those with BMI <30, accuracies for correct diagnosis by Rater 1 with LDCT-IR-FBP and LDCT-MBIR, when compared to CECT, were 95.4% (104/109) and 99% (108/109), respectively, and 92.7% (101/109) and 100% (109/109) for Rater 2. In patients with BMI ≥30, accuracies with LDCT-IR-FBP and LDCT-MBIR were 88.1% (37/42) and 90.5% (38/42) for Rater 1 and 78.6% (33/42) and 92.9% (39/42) for Rater 2.

conclusionsThe radiation dose delivered by CT to non-obese patients with acute abdominal pain can be safely reduced to levels close to standard radiography using LDCT-MBIR. KEY POINTS: • LDCT-MBIR (<2.5 mSv) can be used to assess acute abdominal pain. • LDCT-MBIR (<2.5 mSv) cannot safely assess acute abdominal pain in obese patients. • LDCT-IR-FBP (<2.5 mSv) cannot safely assess patients with acute abdominal pain.

Indexed as

Abdomen, AcuteAdultAgedAged, 80 and overAlgorithmsBody Mass IndexEmergenciesFemaleHumansMaleMiddle AgedRadiation DosageRadiographic Image Interpretation, Computer-AssistedRadiography, AbdominalRadionuclide ImagingTomography, X-Ray ComputedAcute abdomenCTEmergenciesIterative reconstructionRadiation dose

Identifiers

PMID28083698
OpenAlexW2579171293

What OpenQuestion holds

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Registered trials

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.