Evidence map›Paper›PMID 37620113›Full record

SynthesisRMD open2023

Imaging in diagnosis, monitoring and outcome prediction of large vessel vasculitis: a systematic literature review and meta-analysis informing the 2023 update of the EULAR recommendations.

Philipp Bosch, Milena Bond, Christian Dejaco, Cristina Ponte, Sarah Louise Mackie, Louise Falzon, Wolfgang A Schmidt, Sofia Ramiro

Open access · goldAbstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in RMD open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
23.0field-weighted citation impact, top 1% 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

48 citing papers in PubMed, 1 synthesis or guideline pooled it, 89 citations in OpenAlex.

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  5. Visual analysis of [European journal of nuclear medicine and molecular imaging · 2026
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  13. American journal of nuclear medicine and molecular imaging · 2026
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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 7 institutions in 7 countries.

Philipp Bosch *Department of Rheumatology and Immunology, Medical University of Graz, Graz, Austria.ORCID 0000-0002-6783-6422
Milena Bond *Department of Rheumatology, Hospital of Bruneck (ASAA-SABES), Teaching Hospital of the Paracelsius Medical University, Brunico, Italy.ORCID 0000-0002-5400-2955
Christian DejacoDepartment of Rheumatology and Immunology, Medical University of Graz, Graz, Austria.ORCID 0000-0002-0173-0668
Cristina PonteRheumatology Research Unit, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.ORCID 0000-0002-3989-1192
Sarah Louise MackieLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK.ORCID 0000-0003-2483-5873
Louise FalzonHealth Economics and Decision Science, The University of Sheffield, Sheffield, UK.ORCID 0000-0002-9449-6056
Wolfgang A SchmidtDepartment of Rheumatology, Immanuel Krankenhaus Berlin, Medical Centre for Rheumatology Berlin-Buch, Berlin, Germany.ORCID 0000-0001-7831-8738
Sofia RamiroDepartment of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands sofiaramiro@gmail.com.ORCID 0000-0002-8899-9087
Medical University of Graz · ATDecision Sciences (United States) · USImmanuel Krankenhaus · DEInstitute of Parasitology · CZLeiden University Medical Center · NLUniversity of Leeds · GBUniversity of Lisbon · PT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo update the evidence on imaging for diagnosis, monitoring and outcome prediction in large vessel vasculitis (LVV) to inform the 2023 update of the European Alliance of Associations for Rheumatology recommendations on imaging in LVV.

methodsSystematic literature review (SLR) (2017-2022) including prospective cohort and cross-sectional studies (>20 participants) on diagnostic, monitoring, outcome prediction and technical aspects of LVV imaging. Diagnostic accuracy data were meta-analysed in combination with data from an earlier (2017) SLR.

resultsThe update retrieved 38 studies, giving a total of 81 studies when combined with the 2017 SLR. For giant cell arteritis (GCA), and taking clinical diagnosis as a reference standard, low risk of bias (RoB) studies yielded pooled sensitivities and specificities (95% CI) of 88% (82% to 92%) and 96% (95% CI 86% to 99%) for ultrasound (n=8 studies), 81% (95% CI 71% to 89%) and 98% (95% CI 89% to 100%) for MRI (n=3) and 76% (95% CI 67% to 83%) and 95% (95% CI 71% to 99%) for fluorodeoxyglucose positron emission tomography (FDG-PET, n=4), respectively. Compared with studies assessing cranial arteries only, low RoB studies with ultrasound assessing both cranial and extracranial arteries revealed a higher sensitivity (93% (95% CI 88% to 96%) vs 80% (95% CI 71% to 87%)) with comparable specificity (94% (95% CI 83% to 98%) vs 97% (95% CI 71% to 100%)). No new studies on diagnostic imaging for Takayasu arteritis (TAK) were found. Some monitoring studies in GCA or TAK reported associations of imaging with clinical signs of inflammation. No evidence was found to determine whether imaging severity might predict worse clinical outcomes.

conclusionUltrasound, MRI and FDG-PET revealed a good performance for the diagnosis of GCA. Cranial and extracranial vascular ultrasound had a higher pooled sensitivity with similar specificity compared with limited cranial ultrasound.

Indexed as

Fluorodeoxyglucose F18Giant Cell ArteritisCross-Sectional StudiesHumansPositron-Emission TomographyProspective StudiesFluorodeoxyglucose F18giant cell arteritisMagnetic Resonance Imagingsystemic vasculitisultrasonography

Identifiers

PMID37620113
PMCPMC10450079
OpenAlexW4386143309

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.