P552 Withdrawal of immunomodulator medications (IM) in children with inflammatory bowel disease on combination therapy of IM and biologics
Bibliographic record
Abstract
Abstract Background Anti-tumor necrosis factor (anti-TNF) antagonists such as infliximab (IFX) are widely used for the treatment of inflammatory bowel disease (IBD). Early studies suggested that combination therapy with IFX and an immunomodulator drug (IM) such as azathioprine (AZA) or methotrexate (MTX) may help in optimising biologic pharmacokinetics, minimising immunogenicity, and improving outcomes. On the other hand, IM especially AZA, may increase infection and cancer risks with no clear evidence on long-term benefits of combination therapy. As such, stopping IM and continuation of an anti-TNF agent as a monotherapy in patients in remission seem to be a sensible strategy. However, there is no evidence to prove the efficacy of this strategy. The aim of this work was to examine frequency and factors associated with the first relapse after IM withdrawal in a cohort of children with IBD on combination therapy. Methods In a retrospective multicenter pediatric study, we determined the percentage of patients and investigated potential factors associated with the first relapse in a cohort of children and young adults with IBD on combination therapy of anti-TNF and IM after stopping IM. Cox regression analysis was used to assess factors associated with IBD relapse following IM withdrawal. Results A total of 79 patients (42, males, 62 Crohn’s disease) with 74 (93.7%) on IFX were included. In addition to the anti-TNF agent, 33 (41.8%) were on AZA and the rest were on MTX. The median duration of combination therapy was 2.0 (IQR 1.2–2.8) years. All participants were in clinical remission at the time of IM withdrawal. The median duration of follow-up after IM withdrawal was 11.0 (IQR 5.0–16.2) months. Only 8 (10.1%) patients relapsed over that period of follow-up. Age, sex, disease phenotype at diagnosis, family history of IBD, type of IM, and biochemical markers and clinical disease activity indices prior to IM stoppage did not predict a future relapse. Among those with CD on IFX who maintained remission, the median last IFX trough level before IM withdrawal was 6.25 Ug/ml (IQR: 4.04–8.70) vs. 3.8 Ug/ml (IQR: 2.40–11.6) in those who relapsed (p = 0.4). Conclusion Over short-term follow-up, the majority of children on combination therapy of IM and an anti-TNF agent remain in clinical remission after IM withdrawal.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".