Evidence map›Paper›PMID 31409598›Full record

ArticleClinical chemistry2019

Islet Autoantibody Standardization Program 2018 Workshop: Interlaboratory Comparison of Glutamic Acid Decarboxylase Autoantibody Assay Performance.

Vito Lampasona, David L Pittman, Alistair J Williams, Peter Achenbach, Michael Schlosser, Beena Akolkar, William E Winter, Participating Laboratories

Open access · bronzeAbstract readMulticenter Study
In one paragraph

Article in Clinical chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed
7.2field-weighted citation impact, top 3% 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, 99 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

8 authors at 20 institutions in 16 countries.

Vito LampasonaSan Raffaele Diabetes Research Institute, IRCCS San Raffaele Hospital, Milan, Italy; lampasona.vito@hsr.it.
David L PittmanDepartment of Pathology, University of Florida, Gainesville, FL.
Alistair J WilliamsDiabetes and Metabolism, Translational Health Sciences, University of Bristol, Bristol, UK.
Peter AchenbachInstitute of Diabetes Research, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.
Michael SchlosserDepartment of Surgery, University Medical Center Greifswald, Greifswald, Germany.
Beena AkolkarDivision of Diabetes, Endocrinology, and Metabolic, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.
William E WinterDepartment of Pathology, University of Florida, Gainesville, FL.
Participating Laboratories
University of Florida · USHelmholtz Zentrum München · DEUniversity of Florida Health · USVita-Salute San Raffaele University · ITHôpital Necker-Enfants Malades · FRLinköping University · SENational Institute of Diabetes and Digestive and Kidney Diseases · USOslo University Hospital · NOPacific Northwest Diabetes Research Institute · USReinier de Graaf Hospital · NLRoyal Devon & Exeter NHS Foundation Trust · GBThe Royal Melbourne Hospital · AUUniversity of Bristol · GBUniversity of Helsinki · FIUniversity of Washington · USWave 80 Biosciences (United States) · USBioCruces Health research Institute · ESCenter for Beta Cell Therapy in Diabetes · BEHaukeland University Hospital · NOLund University · SE

Funding

Project 3P01AI042288 · NIAID · UNIVERSITY OF FLORIDA · PI Todd Michael Brusko · 1997 to 2026
$32.9M
Highly Sensitive and Specific MSD Multiplexed Autoimmune Disease PanelU24AI118660 · NIAID · MESO SCALE DIAGNOSTICS, LLC · PI MATHEW, ANU · 2015 to 2019
$1.9M
NIAID NIH HHS P01 AI042288NIAID NIH HHS U24 AI118660
6 · The paper itself

Abstract

backgroundThe Islet Autoantibody Standardization Program (IASP) aims to improve the performance of immunoassays measuring type 1 diabetes (T1D)-associated autoantibodies and the concordance of results among laboratories. IASP organizes international interlaboratory assay comparison studies in which blinded serum samples are distributed to participating laboratories, followed by centralized collection and analysis of results, providing participants with an unbiased comparative assessment. In this report, we describe the results of glutamic acid decarboxylase autoantibody (GADA) assays presented in the IASP 2018 workshop.

methodsIn May 2018, IASP distributed to participants uniquely coded sera from 43 new-onset T1D patients, 7 multiple autoantibody-positive nondiabetic individuals, and 90 blood donors. Results were analyzed for the following metrics: sensitivity, specificity, accuracy, area under the ROC curve (ROC-AUC), partial ROC-AUC at 95% specificity (pAUC95), and concordance of qualitative and quantitative results.

resultsThirty-seven laboratories submitted results from a total of 48 different GADA assays adopting 9 different formats. The median ROC-AUC and pAUC95 of all assays were 0.87 [interquartile range (IQR), 0.83-0.89] and 0.036 (IQR, 0.032-0.039), respectively. Large differences in pAUC95 (range, 0.001-0.0411) were observed across assays. Of formats widely adopted, bridge ELISAs showed the best median pAUC95 (0.039; range, 0.036-0.041).

conclusionsSeveral novel assay formats submitted to this study showed heterogeneous performance. In 2018, the majority of the best performing GADA immunoassays consisted of novel or established nonradioactive tests that proved on a par or superior to the radiobinding assay, the previous gold standard assay format for GADA measurement.

Indexed as

AdolescentAdultAutoantibodiesChildDiabetes Mellitus, Type 1EducationFemaleGlutamate DecarboxylaseHumansImmunoassayMaleMiddle AgedReference StandardsROC CurveYoung AdultAutoantibodiesGlutamate Decarboxylase

Identifiers

PMID31409598
PMCPMC8936135
OpenAlexW2967824496

What OpenQuestion holds

Textmetadata
LicenceTDM
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.