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Record W4414740831 · doi:10.1093/clinchem/hvaf086.506

B-108 Performance evaluation of ANCA and anti-GBM antibody test on a BioPlex 2200 system

2025· article· en· W4414740831 on OpenAlexaff
Roman A. Laskowski, Aimee Roewekamp, Simone Corriveau, Fang Wu, Song Lu

Bibliographic record

VenueClinical Chemistry · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsUniversity of SaskatchewanSaskatchewan Health Authority
Fundersnot available
KeywordsAntibodyMultiplexImmunoassayVasculitisImmunofluorescenceAnalyteAutoimmune disease

Abstract

fetched live from OpenAlex

Abstract Background Anti-neutrophil cytoplasmic antibodies (ANCA) including anti-MPO and anti-PR3 are important serum markers for the diagnosis of ANCA-associated vasculitides. Goodpasture’s syndrome is a rare autoimmune disease affecting lung and kidney, and it is associated with anti-glomerular basement membrane (GBM) antibody. These 3 antibodies play a crucial role in the diagnosis of autoimmune vasculitis and can be screened by solid phase immunoassay followed by confirmatory immunofluorescence assay (IFA). The BioPlex 2200 multiplex bead system is a solid phase immunoassay; the Vasculitis kit allows simultaneously detection of ANCA and anti-GBM antibody in a single run. In this study, we evaluated the analytical performance of BioPlex 2200 vasculitis panel as a primary screening method for autoimmune vasculitis. Methods As an FDA-approved assay, the following manufacturer-claimed performance parameters were verified per relevant CLSI guidelines: precision, linearity, dilution, method comparison, and reference interval (cutoff). Complex precision was conducted using two levels of QC across the cutoff (1.0 antibody index, AI) in a 2x2x5 design. A calibrator set was used to verify assay linearity. Four dilution factors (2, 4, 10, 20) were verified by serial manual dilution of the pooled patient samples. A quantitative method comparison was performed with an external lab using the same assay. In addition, we also compared the qualitative result interpretation between BioPlex assay and ELISA (Euroimmun) or IFA. 30 serum samples from healthy donors were used to verify the cutoff value. According to the literature and CAP survey criteria, the total allowable error of all 3 analytes was set at ±30%. Results were analyzed by EP Evaluator (Data Innovations). Results The total CV% of ANCA was < 5%, and the between-day CV% of anti-GBM was slightly higher but < 10%. All 3 assays demonstrated linearity (slope: 0.966 to 1.02, intercept: -0.13 to 0.02, observed error: 7.90% to 8.80%) and accuracy across manufacturer-claimed AMR (0.2-8.0 AI). However, the linearity was not retained after dilution due to a significantly increased recovery (> 110%). The BioPlex method run in the local lab and reference lab were comparable quantitatively (slope: 0.989 to 1.010, intercept: -0.39 to 0.53, R: 0.984 to 0.999). In the qualitative result interpretation, ANCA was highly consistent between BioPlex assay and ELISA (Kappa: 0.789 to 0.943). Although BioPlex assay detected few anti-GBM positive samples than ELISA, it correlated well with the gold standard IFA (sensitivity: 100%, specificity: 80%). Finally, all 30 healthy donor samples were negative for the 3 autoantibodies (< 0.2 AI), verifying the manufacturer-claimed cutoff. Conclusion The BioPlex 2200 vasculitis panel meets the manufacturer-claimed precision and AMR. The accuracy has been verified by method comparison with a reference lab using the same methodology. As a semi-quantitative test, dilution compromises linearity and leads to increased recovery. The qualitative interpretation of BioPlex assay result is consistent with ELISA for ANCA tests, whereas the anti-GBM by BioPlex assay correlates well with IFA except for some false positive cases. In conjunction with reflexing IFA tests on the positive samples, BioPlex 2200 vasculitis panel can be utilized as a primary screening method for autoimmune vascuiltis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.376
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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