P1038 Does combination immunomodulator therapy impact real-world biologic effectiveness in Inflammatory Bowel Diseases?A propensity score analysis in patients treated with anti-TNF and non-anti-TNF biologics in the UK IBD BioResource
Bibliographic record
Abstract
Abstract Background Randomised controlled trials have demonstrated that the combination of infliximab and azathioprine is superior to either drug as monotherapy both in patients with Crohn’s Disease (CD) and ulcerative colitis (UC). Data on the combination of other biological agents -including other anti-TNFs, vedolizumab and ustekinumab- and immunomodulators (IMMs) is lacking or shows conflicting results. Given the risks of combination therapy, physicians are often hesitant on whether to combine IMMs with biologic therapies. Methods We measured the effectivenessof biologic therapies [infliximab (IFX), adalimumab (ADA), golimumab (GLM), vedolizumab (VDZ) and ustekinumab (UST)] in patients with UC and CD with and without IMM use enrolled in the UK Inflammatory Bowel Disease (IBD) BioResource over long durations of followup.Demographic, disease, treatment and outcome data were retrieved. Inverse probability of treatment weighting (IPTW) was used to balance groups of those receiving vs those not receiving IMM using a propensity score-weighting approach accounting for baseline patient or disease related clinical characteristics. Effectiveness of treatment was based on persistence free of treatment discontinuation or treatment failure. Failure was defined as occurring if any of the following occurred: (a) clinician-recorded treatment failure (b) start of treatment with a different advanced therapy (c) occurrence of resectional or defunctioning bowel surgery. We compared Kaplan Meier curves for treatment failure-free survival after IPTW and used a log rank test for differences between the groups. Results In UC, we included 2479 patients receiving IFX (1173 on IMM), 1117 ADA (401 on IMM), 188 GLM (77 on IMM) and 1256 VDZ (392 on IMM). In CD, we included 5956 patients receiving IFX (3062 on IMM), 4524 ADA (1898 on IMM), 1067 VDZ (340 on IMM) and 627 UST (198 on IMM).As expected, combination IMM therapy was associated with increased drug effectiveness for IFX in both UC and CD. Combination IMM therapy was also associated with increased effectiveness of ADA in CD. For all other drugs, there was no significant benefit for combination IMM. Hazard ratios(HRs) for the effect of IMM of each drug effectiveness in UC and CD are summarised (Figure 1). Conclusion We confirm prior results indicated that IFX effectiveness is improved by concomitant use of an IMM in both UC and CD.In contrast to RCT data but in accordance to other robust real-world studies, we find evidence that for ADA in CD concomitant IMM can increase drug effectiveness. We find no evidence of benefit for concomitant IMM for other biologics in UC or CD.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".