P342 Trans-continental analysis of over, 2000 Inflammatory Bowel Disease patients implicates geography, disease type, and exposure to immunosuppression as drivers of SARS-CoV-2 seroprevalence: data from the ICARUS-IBD Consortium
Bibliographic record
Abstract
Abstract Background IBD patients on immune-modulatory therapies are considered high-risk for SARS-CoV-2 infection. Direct comparisons of serological responses to SARS-CoV-2 infection in IBD patients across different continents and medications are lacking. We performed SARS-CoV-2 sero-surveillance of IBD patients prior to vaccination at seven large tertiary centres in Asia, Europe, and North America. Methods Clinical data and sera were collected from, 2,213 IBD patients receiving routine care at institutions in Belgium, Canada, Hong Kong, India, Japan, the United Kingdom, and the United States between, 26 May, 2020 and, 24 September, 2021 (Table, 1). Sera were taken prior to vaccination. Clinical data were collected through patient questionnaires and medical records. Antibody reactivity to the SARS-CoV-2 spike protein was assessed using the Roche SARS-CoV-2 anti-spike total antibody and/or Siemens Healthineers COV2T anti-spike total antibody assays, which showed, 99.4% concordance. Univariate analysis was performed to evaluate association between individual variables and sero-status. Results The pre-vaccination seroprevalence of antibodies to SARS-CoV-2 in IBD patient varied widely according to location from, 0% in Hong Kong to, 57.9% in New Delhi, India (p<0.001). Rates in Europe and North America were similar (range, 3.57%-8.94%). Overall, SARS-CoV-2 seroprevalence appears to be equal to or less than local populations (Table, 2). Seroprevalence rates were associated with IBD type (7.8% CD, 12.4% UC, 15% IBD-U, p<0.001), smoking status (p<0.001), and history of COVID diagnosis (p<0.001) or COVID hospitalization (p=0.001), and any IMM (p<0.001). (Table, 3). Whilst there were no significant differences in seroprevalence between patients receiving infliximab (IFX), vedolizumab (VDZ), and ustekinumab (UST), antibody levels were attenuated in patients on IFX monotherapy and combination therapy (both p=0.002) and VDZ (p=0.02), compared with no medications (Figure 1). Conclusion We confirm in diverse poulations that exposure to biologics or immunomodulators, type of disease, and smoking status are associated with seroprevalence and antibody levels. We show for the first time the dominant influence of geographical location on sero-status in these patients. These observations should be considered as we look towards post-vaccination data to help stratify patients for clinical guidelines on SARS-CoV-2 vaccination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".