Evidence map›Paper›PMID 37816641›Full record

ArticleNeurology2023

Validation Study of the MDS Criteria for the Diagnosis of Multiple System Atrophy in the Mayo Clinic Brain Bank.

Hiroaki Sekiya, Shunsuke Koga, Aya Murakami, Miki Kawazoe, Minji Kim, Nicholas B Martin, Ryan J Uitti, William P Cheshire, Zbigniew K Wszolek, Dennis W Dickson

Abstract read
In one paragraph

Article in Neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
  2. Multiple system atrophy: cure and care.Journal of neural transmission (Vienna, Austria : 1996) · 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

10 authors.

Hiroaki SekiyaFrom the Department of Neuroscience (H.S., S.K., A.M., M. Kawazoe, N.B.M., D.W.D.), Mayo Clinic, Jacksonville, FL; Division of Neurology (H.S.), Kobe University Graduate School of Medicine; Department of Neurology (A.M.), Kansai Medical University Hirakata, Japan; Departments of Artificial Intelligence and Informatics Research (M. Kim) and Neurology (R.J.U., W.P.C., Z.K.W.), Mayo Clinic, Jacksonville, FL.ORCID 0000-0001-9683-5966
Shunsuke KogaFrom the Department of Neuroscience (H.S., S.K., A.M., M. Kawazoe, N.B.M., D.W.D.), Mayo Clinic, Jacksonville, FL; Division of Neurology (H.S.), Kobe University Graduate School of Medicine; Department of Neurology (A.M.), Kansai Medical University Hirakata, Japan; Departments of Artificial Intelligence and Informatics Research (M. Kim) and Neurology (R.J.U., W.P.C., Z.K.W.), Mayo Clinic, Jacksonville, FL.ORCID 0000-0001-8868-9700
Aya MurakamiFrom the Department of Neuroscience (H.S., S.K., A.M., M. Kawazoe, N.B.M., D.W.D.), Mayo Clinic, Jacksonville, FL; Division of Neurology (H.S.), Kobe University Graduate School of Medicine; Department of Neurology (A.M.), Kansai Medical University Hirakata, Japan; Departments of Artificial Intelligence and Informatics Research (M. Kim) and Neurology (R.J.U., W.P.C., Z.K.W.), Mayo Clinic, Jacksonville, FL.ORCID 0000-0002-5892-2919
Miki KawazoeFrom the Department of Neuroscience (H.S., S.K., A.M., M. Kawazoe, N.B.M., D.W.D.), Mayo Clinic, Jacksonville, FL; Division of Neurology (H.S.), Kobe University Graduate School of Medicine; Department of Neurology (A.M.), Kansai Medical University Hirakata, Japan; Departments of Artificial Intelligence and Informatics Research (M. Kim) and Neurology (R.J.U., W.P.C., Z.K.W.), Mayo Clinic, Jacksonville, FL.ORCID 0000-0002-6569-9452
Minji KimFrom the Department of Neuroscience (H.S., S.K., A.M., M. Kawazoe, N.B.M., D.W.D.), Mayo Clinic, Jacksonville, FL; Division of Neurology (H.S.), Kobe University Graduate School of Medicine; Department of Neurology (A.M.), Kansai Medical University Hirakata, Japan; Departments of Artificial Intelligence and Informatics Research (M. Kim) and Neurology (R.J.U., W.P.C., Z.K.W.), Mayo Clinic, Jacksonville, FL.ORCID 0009-0009-6787-0354
Nicholas B MartinFrom the Department of Neuroscience (H.S., S.K., A.M., M. Kawazoe, N.B.M., D.W.D.), Mayo Clinic, Jacksonville, FL; Division of Neurology (H.S.), Kobe University Graduate School of Medicine; Department of Neurology (A.M.), Kansai Medical University Hirakata, Japan; Departments of Artificial Intelligence and Informatics Research (M. Kim) and Neurology (R.J.U., W.P.C., Z.K.W.), Mayo Clinic, Jacksonville, FL.ORCID 0000-0002-0972-5417
Ryan J UittiFrom the Department of Neuroscience (H.S., S.K., A.M., M. Kawazoe, N.B.M., D.W.D.), Mayo Clinic, Jacksonville, FL; Division of Neurology (H.S.), Kobe University Graduate School of Medicine; Department of Neurology (A.M.), Kansai Medical University Hirakata, Japan; Departments of Artificial Intelligence and Informatics Research (M. Kim) and Neurology (R.J.U., W.P.C., Z.K.W.), Mayo Clinic, Jacksonville, FL.ORCID 0000-0001-8113-4710
William P CheshireFrom the Department of Neuroscience (H.S., S.K., A.M., M. Kawazoe, N.B.M., D.W.D.), Mayo Clinic, Jacksonville, FL; Division of Neurology (H.S.), Kobe University Graduate School of Medicine; Department of Neurology (A.M.), Kansai Medical University Hirakata, Japan; Departments of Artificial Intelligence and Informatics Research (M. Kim) and Neurology (R.J.U., W.P.C., Z.K.W.), Mayo Clinic, Jacksonville, FL.ORCID 0000-0002-6364-5362
Zbigniew K WszolekFrom the Department of Neuroscience (H.S., S.K., A.M., M. Kawazoe, N.B.M., D.W.D.), Mayo Clinic, Jacksonville, FL; Division of Neurology (H.S.), Kobe University Graduate School of Medicine; Department of Neurology (A.M.), Kansai Medical University Hirakata, Japan; Departments of Artificial Intelligence and Informatics Research (M. Kim) and Neurology (R.J.U., W.P.C., Z.K.W.), Mayo Clinic, Jacksonville, FL.ORCID 0000-0001-5487-1053
Dennis W DicksonFrom the Department of Neuroscience (H.S., S.K., A.M., M. Kawazoe, N.B.M., D.W.D.), Mayo Clinic, Jacksonville, FL; Division of Neurology (H.S.), Kobe University Graduate School of Medicine; Department of Neurology (A.M.), Kansai Medical University Hirakata, Japan; Departments of Artificial Intelligence and Informatics Research (M. Kim) and Neurology (R.J.U., W.P.C., Z.K.W.), Mayo Clinic, Jacksonville, FL.ORCID 0000-0001-7189-7917

Funding

Technology and Remote Assessment CoreU19AG063911 · NIA · MAYO CLINIC ROCHESTER · PI Bradley F Boeve · 2019 to 2026
$120.9M
NIA NIH HHS U19 AG063911
6 · The paper itself

Abstract

BACKGROUND AND

objectiveThe second consensus criteria in 2008 have been used in diagnosing multiple system atrophy (MSA). The International Parkinson and Movement Disorder Society (MDS) proposed new diagnostic criteria for MSA in 2022. This study aimed to compare the diagnostic accuracy between these 2 criteria and validate the clinical utility of the newly proposed criteria for MSA.

methodsWe conducted a retrospective autopsy cohort study of consecutive patients with a clinical or pathologic diagnosis of MSA from the Mayo Clinic brain bank between 1998 and 2021. We studied 352 patients (250 pathologically diagnosed MSA and 102 non-MSA); MDS criteria and the second consensus criteria were applied. The sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristic curves were compared between these criteria. Comparison was conducted between clinical subtypes and among clinically challenging cases (those with different clinical diagnoses or those with suspected but undiagnosed MSA before death). We also used machine learning algorithm, eXtreme Gradient Boosting, to identify clinical features contributing diagnostic performance.

resultsThe sensitivity and specificity of clinically established and probable MSA by the MDS criteria were 16% and 99% and 64% and 74%, respectively. The sensitivity and specificity of probable MSA and possible MSA by the second consensus criteria were 72% and 52% and 93% and 21%, respectively. The AUC of MDS clinically probable MSA was the highest (0.69). The diagnostic performance did not differ between clinical subtypes. In clinically challenging cases, MDS clinically established MSA maintained high specificity and MDS clinically probable MSA demonstrated the highest AUC (0.62). MRI findings contributed to high specificity. In addition, combining core clinical features with 2 or more from any of the 13 supporting features and the absence of exclusion criteria also yielded high specificity. Among supporting features, rapid progression was most important for predicting MSA pathology. DISCUSSION: The MDS criteria showed high specificity with clinically established MSA and moderate sensitivity and specificity with clinically probable MSA. The observation that high specificity could be achieved with clinical features alone suggests that MSA diagnosis with high specificity is possible even in areas where MRI is not readily available.

Indexed as

Multiple System AtrophyBrainCohort StudiesHumansRetrospective StudiesSensitivity and Specificity

Identifiers

PMID37816641
PMCPMC10791062

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