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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".