Evidence map›Paper›PMID 35536176›Full record

ArticleRheumatology (Oxford, England)2022

Ultrasound and MR muscle imaging in new onset idiopathic inflammatory myopathies at diagnosis and after treatment: a comparative pilot study.

Anne W Walter, Johan Lim, Joost Raaphorst, Frank F Smithuis, J Michiel den Harder, Filip Eftimov, Wouter Potters, Christiaan G J Saris, Marianne de Visser, Ivo N van Schaik and 3 more

Open access · hybridAbstract read
In one paragraph

Article in Rheumatology (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
3.4field-weighted citation impact, top 7% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 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

13 authors at 3 institutions in 1 country.

Anne W WalterDepartment of Neurology, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam Neuroscience.ORCID 0000-0001-9106-0508
Johan LimDepartment of Neurology, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam Neuroscience.ORCID 0000-0002-7565-1954
Joost RaaphorstDepartment of Neurology, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam Neuroscience.
Frank F SmithuisDepartment of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam Movement Sciences, Amsterdam.
J Michiel den HarderDepartment of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam Movement Sciences, Amsterdam.
Filip EftimovDepartment of Neurology, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam Neuroscience.
Wouter PottersDepartment of Neurology, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam Neuroscience.
Christiaan G J SarisDepartment of Neurology, Radboud UMC, Donders Institute for Brain Cognition and Behaviour, Radboud University, Nijmegen.
Marianne de VisserDepartment of Neurology, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam Neuroscience.
Ivo N van SchaikDepartment of Neurology, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam Neuroscience.
Rob J de HaanDepartment of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.
Anneke J van der KooiDepartment of Neurology, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam Neuroscience.
Camiel VerhammeDepartment of Neurology, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam Neuroscience.
Amsterdam Neuroscience · NLUniversity of Amsterdam · NLRadboud University Nijmegen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo prospectively compare ultrasound (US) and whole-body MRI for detection of muscle abnormalities compatible with idiopathic inflammatory myopathies (IIM).

methodsNewly diagnosed IIM patients underwent US (14 muscles) and MRI (36 muscles) at diagnosis and after nine weeks monotherapy with intravenous immunoglobulin. Muscles were compatible with IIM when quantitative US echo-intensity (EI) z scores was ≥1.5, semi-quantitative US Heckmatt score was ≥2, qualitative US was abnormal, or when MRI showed oedema on T2-weighted images. At patient level, findings were classified as abnormal when quantitative US EI z scores was >1.5 (n = 3 muscles), >2.5 (n = 2 muscles) or >3.5 (n = 1 muscle), or if ≥3 muscles showed abnormalities as described above for the other diagnostic methods.

resultsAt diagnosis, in 18 patients US of 252 muscles revealed abnormalities in 36 muscles (14%) with quantitative, in 153 (61%) with semi-quantitative and in 168 (67%) with qualitative analysis. MRI showed oedema in 476 out of 623 muscles (76%). Five patients (28%) reached abnormal classification with quantitative US, 16 (89%) with semi-quantitative and qualitative US, and all patients (100%) with MRI. Nine-week follow-up of 12 patients showed no change over time with quantitative US or MRI, and a decrease in abnormalities with semi-quantitative US (P <0.01), and qualitative US (P <0.01).

conclusionAt diagnosis, MRI was more sensitive than US to detect muscle abnormalities compatible with IIM. Semi-quantitative US and qualitative US detected abnormalities in the majority of the patients while evaluating fewer muscles than MRI and showed change over time after nine weeks of treatment.

Indexed as

Muscle, SkeletalMyositisEdemaHumansMagnetic Resonance ImagingPilot Projectsidiopathic inflammatory myopathymagnetic resonance imagingmuscle imagingmyositisultrasonography

Identifiers

PMID35536176
PMCPMC9788821
OpenAlexW4280489312

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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.