Evidence map›Paper›PMID 38703476›Full record

ArticleClinical imaging2024

Screening for exclusion of high-risk bleeding features of esophageal varices in cirrhosis through CT and MRI.

Ali Borhani, Harry Luu, Alireza Mohseni, Ziyi Xu, Mohammadreza Shaghaghi, Celestina Tolosa, Mohammad Mirza Aghazadeh Attari, Seyedeh Panid Madani, Haneyeh Shahbazian, Pegah Khoshpouri and 4 more

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In one paragraph

Article in Clinical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

14 authors.

Ali BorhaniDepartment of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Harry LuuDepartment of Medicine, Gastroenterology and Hepatology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Alireza MohseniDepartment of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Ziyi XuBloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Mohammadreza ShaghaghiDepartment of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Celestina TolosaDepartment of Medicine, Gastroenterology and Hepatology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Mohammad Mirza Aghazadeh AttariDepartment of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Seyedeh Panid MadaniDepartment of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Haneyeh ShahbazianDepartment of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Pegah KhoshpouriDepartment of Radiology, University of Washington, Seattle, WA, USA.
Shadi AfyouniDepartment of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Ghazal ZandiehDepartment of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Ihab R KamelDepartment of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA. Electronic address: ikamel@jhmi.edu.
Amy K KimDepartment of Medicine, Gastroenterology and Hepatology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.

Funding

Improved early prognostic algorithm for hepatocellulcar carcinomaK08CA237624 · NCI · JOHNS HOPKINS UNIVERSITY · PI KIM, AMY K · 2020 to 2024
$1.3M
NCI NIH HHS K08 CA237624
6 · The paper itself

Abstract

BACKGROUND &

aimEsophageal varices (EV) screening guidelines have evolved with improved risk stratification to avoid unnecessary esophagogastroduodenoscopy (EGD) in individuals with low bleeding risks. However, uncertainties persist in the recommendations for certain patient groups, particularly those with hepatocellular carcinoma (HCC) and/or receiving non-selective beta-blockers (NSBB) without prior endoscopy. This study assessed the efficacy of imaging in ruling out EVs and their high-risk features associated with bleeding in patients with cirrhosis and with HCC. We also evaluated the impact of NSBB on the detection of these characteristics.

methodsA total of 119 patients undergoing EGD with CT and/or MRI within 90 days of the procedure were included. 87 patients had HCC. A new imaging grading system was developed utilizing the size of EVs and the extent of their protrusion into the esophagus lumen. The negative predictive value (NPV) of EVimaging(-) versus EVimaging (+) (grades 1-3) in ruling out the presence of EV and/or high-risk features by EGD was calculated. The predictive performance of imaging was determined by logistic regression.

resultsThe NPV of imaging for detecting EV and high-risk features was 81 % and 92 %, respectively. Among HCC patients, the NPV for EV and high-risk features was 80 % and 64 %, respectively. Being on NSBB didn't statistically impact the imaging detection of EV. Imaging was a better predictor of high-risk EGD findings than Child-Turcotte-Pugh scores.

conclusionsOur results suggest that imaging can effectively rule out the presence of EV and high-risk features during EGD, even in patients with HCC and/or receiving NSBB.

Indexed as

Esophageal and Gastric VaricesGastrointestinal HemorrhageLiver CirrhosisMagnetic Resonance ImagingTomography, X-Ray ComputedAdultAgedCarcinoma, HepatocellularEndoscopy, Digestive SystemFemaleHumansLiver NeoplasmsMaleMiddle AgedPredictive Value of TestsRetrospective StudiesCirrhosisEsophageal varicesHepatocellular carcinomaPortal hypertensionRadiographic biomarker

Identifiers

PMID38703476
PMCPMC12716801

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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.