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Enregistrement W2112621233 · doi:10.1111/ajt.12462

Comprehensive Assessment and Standardization of Solid Phase Multiplex-Bead Arrays for the Detection of Antibodies to HLA-Drilling Down on Key Sources of Variation

2013· letter· en· W2112621233 sur OpenAlexaff
Elaine F. Reed, P. Nagesh Rao, Zuo‐Feng Zhang, Howard M. Gebel, Robert A. Bray, Indira Guleria, John G. Lunz, Thalachallour Mohanakumar, Peter Nickerson, Anat R. Tambur, Adriana Zeevi, Peter S. Heeger, David Gjertson

Notice bibliographique

RevueAmerican Journal of Transplantation · 2013
Typeletter
Langueen
DomaineMedicine
ThématiqueRenal Transplantation Outcomes and Treatments
Établissements canadiensUniversity of ManitobaShared Health
Organismes subventionnairesNational Institute of Allergy and Infectious DiseasesU.S. Public Health Service
Mots-clésMultiplexStandardizationMedicineProtocol (science)TransplantationSerial dilutionCoefficient of variationStatisticsComputer scienceBioinformaticsMathematicsInternal medicinePathologyBiology

Résumé

récupéré en direct d'OpenAlex

To the Editor: We appreciate the opportunity to respond to the letter by Dr. Maillard and Dr. Mariat (1Maillard N Mariat C. Solid-phase bead-based assays limitations are not restricted to interlaboratory variability.Am J Transplant. 2013; 13: 3049Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar). They raise three questions regarding our recent article focusing on inter-laboratory standardization of solid phase multiplex-bead arrays to detect antibodies to HLA within the framework of the Clinical Trials in Organ Transplantation (2Reed EF, Rao P, Zhang Z, et al. Comprehensive assessment and standardization of solid phase multiplex-bead arrays for the detection of antibodies to HLA. Am J Transplant 13: 1859–1870.Google Scholar). Our article provides insights into the key sources of variability in commercially available solid phase HLA antibody testing kits. Importantly, our study demonstrates that standardization of reagents and protocols significantly reduces assay variance. Utilization of a global normalization algorithm further reduced median fluorescence intensity (MFI) variations in the protocol, thereby improving comparison of data across laboratories. Dr. Maillard and Dr. Mariat comment that our study did not address the prozone effect. Although they raise an important point, we do not believe the prozone effect impacted our estimates of inter-laboratory variance, since all participants adopted a standardized protocol, used the same reagents and tested a constant volume of alloantisera. We do agree with Dr. Maillard and Dr. Mariat that adding dithiothreitol, running dilutions or employing other methods to explore potential prozone/interfering factors is worthy of systematic investigation. Dr. Maillard and Dr. Mariat correctly point out that the %CV decreases within higher MFI strata. Although they indicate this finding is presented in the Bland–Altman plots (2, figure 5), it is actually illustrated in figure 3 of our article, which shows the variation among seven centers across distinct MFI strata. Dr. Maillard and Dr. Mariat suggest that the sudden amelioration in %CV within higher MFI strata is due to saturation of the beads with antibodies. However, as we clearly showed, the decline in %CV begins at 1000 MFI, well below a saturation dosage (<10 000 MFI), which indicates saturation is not the primary reason to explain this result. Their third point questions the impact of intra-laboratory variability on results and whether the improvement in %CV was due to a reduction in variance within an individual laboratory or between laboratories. Since it is standard of care that clinical laboratories utilize a standard operating procedure for HLA antibody testing, we expect the major cause of assay variance is lot-to-lot differences in test kits. Although we did not specifically address intra-laboratory variability in our report, each data point shown in the Bland–Altman plot (2, figure 5) can be converted into a pseudo “intra-laboratory” %CV [i.e. |ΔMFI|/(2×avgMFI)] representing the variation when a lab repeats the test of same sample and bead across two lots of single antigen kits. The median intra-laboratory %CV was 19%, and boxplots demonstrate a decline with increasing MFI range within each center and overall (Figure 1). On average, the intra-laboratory %CV was less than our reported inter-laboratory %CV (∼25%). Nonetheless, we acknowledge that other sources of variation in the aspects of the assay can certainly contribute to intra-laboratory variability and that each laboratory needs to address these concerns. We anticipate that both inter- and intra-laboratory variance will decrease with the implementation of standardized testing protocols and the increasing availability of uniform lots of reagents. This research was performed as part of an American Recovery and Reinvestment (ARRA) funded project under Award Number U0163594 (to P. Heeger), from the National Institute of Allergy and Infectious Diseases. The work was carried out by members of the Clinical Trials in Organ Transplantation (CTOT) and Clinical Trials in Organ Transplantation in Children (CTOT-C) consortia. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy and Infectious Diseases or the National Institutes of Health. The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,027
score de la tête « metaresearch » (Gemma)0,097
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,027
Score d'incertitude au seuil0,141

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0270,097
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,002
Communication savante0,0030,002
Science ouverte0,0030,001
Intégrité de la recherche0,0080,009
Charge utile insuffisante (le modèle a refusé de juger)0,0020,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,350
Écart entre enseignants0,325 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations74
Publié2013
Routes d'admission1
Résumé présentoui

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