B-062 A Self-Imposed Gray Area? Analysis of Anti-Tissue Transglutaminase and Anti-Endomysial Antibody Discordance in a Celiac Disease Screening Serology Testing Algorithm
Notice bibliographique
Résumé
Abstract Background Celiac disease (CD) is an autoimmune enteropathy affecting around 1% of the population. While occasionally asymptomatic, the disease can present in a variety of ways and can be triggered by dietary gluten at any age. CD serology, including anti-tissue transglutaminase (anti-tTG) IgA and/or IgG, anti-endomysial antibody IgA and/or IgG, and anti-deamidated gliadin peptide IgA and/or IgG, is a key tool for CD screening, diagnosis, and monitoring. Of these tests, the clinical utility of anti-EMA testing has been questioned due to its high cost and limited sensitivity: 5–10% of CD patients do not test positive for anti-EMA and a negative anti-EMA does not rule out CD. The objective of this study is to: 1) evaluate the concordance between anti-tTG and anti-EMA serology test results, and 2) assess the clinical validity of our current CD reflex testing algorithm in which every positive anti-tTG test result goes on for anti-EMA testing. Methods Using our laboratory’s information system, we conducted a retroactive study on patients who underwent CD serology testing at our institution. Query was performed for all patient data [pediatric (<18 years) and adult (>18 years)] collected between April 2020 and August 2022 for anti-tTG IgA performed on the BioPlex 2200 (BioRad Laboratories Inc., Hercules, CA) and anti-EMA IgA (Euroimmun, Germany) (N = 124 308). Vendor-supplied clinical sensitivity and specificity are as follows: anti-tTG IgA 94.3% sensitivity, 98.8% specificity; anti-EMA IgA 95.3% sensitivity and 98.0% specificity. Results From our data, we calculated a mean positive agreement between our anti-tTG IgA and anti-EMA test results of 53.2% (95% confidence interval (CI) 47.5–58.8%) for all anti-tTG IgA positive cases (N = 6691, 5.4% of total anti-tTG IgA test results). The agreement between anti-tTG IgA and anti-EMA test results is improved if multiples of the upper limit of normal (ULN) for anti-tTG IgA is applied. The mean positive agreement between anti-tTG IgA and anti-EMA test results when the anti-tTG result is >10x ULN is 98.4% (95% CI 97.7–99.2%). However, if an anti-tTG IgA cutoff of <10x ULN is employed a mean positive agreement of 32.3% (95% CI 25.8–38.7%) is observed. At an anti-tTG IgA cutoff of <3x ULN, the mean positive agreement is just 11.8% (95% CI 8.2–15.4%). These findings are consistent in both pediatric (N = 29 381) and adult (N = 94 961) populations and are problematic as 45.4% of our positive anti-tTG IgA test results are accompanied by a negative anti-EMA. Conclusion The agreement between anti-tTG IgA and anti-EMA is dependent on the value of anti-tTG IgA with greater agreement observed with increasing anti-tTG IgA values. As all specimens at our institution with positive anti-tTG IgA test results are automatically reflexed for anti-EMA testing, has led to physician confusion as the majority of patients are falling into a gray area with conflicting CD serology test results. Our algorithm is also out of line with most CD screening guidelines that suggest an initial positive anti-tTG test result is followed up with biopsy. Finally, discontinuing anti-EMA reflex testing could result in cost savings of approximately $120 000 CAD per year.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».