A246 POPULATION WIDE STUDY OF THE EPIDEMIOLOGY AND OUTCOMES OF ANTI-TNF DOSE AUGMENTATION IN INFLAMMATORY BOWEL DISEASE
Bibliographic record
Abstract
Abstract Background Inflammatory bowel disease (IBD) patients who experience loss of response to anti-tumor necrosis factor (anti-TNF) therapy are often treated with augmented doses of anti-TNF to recapture response. Despite this, factors associated with dose augmentation and treatment outcomes following dose augmentation remain largely undefined. Aims To examine the epidemiology of anti-TNF dose augmentation and determine the associated treatment outcomes among a province-wide cohort of anti-TNF treated IBD subjects. Methods The University of Manitoba Inflammatory Bowel Disease Epidemiological Database was used to identify patients receiving infliximab or adalimumab maintenance therapy for IBD in the Canadian province of Manitoba. Anti-TNF dose augmentation was defined as a ≥50% increase in anti-TNF dose or a shortening of dosing interval to ≤42 days for infliximab or ≤10 days for adalimumab. Anti-TNF failure was defined as corticosteroid use, IBD-related hospitalization, resective IBD surgery, or change in anti-TNF agent. Competing risks regression using a proportional subhazards model was used to determine the associations between dose augmentation, anti-TNF failure, anti-TNF discontinuation and a number of patient, disease, and treatment factors. Results 871 persons (624 Crohn’s disease (CD), 247 ulcerative colitis (UC)) using anti-TNF maintenance therapy were identified. Cumulative incidence of dose augmentation among continued users was 25.7% at 90 days, 52.3% at 1 year, and 72.8% at 5 years. Anti-TNF failure occurred in 261 of 575 dose augmented subjects, with corticosteroid use the most common failure-defining event. Failure of standard dose anti-TNF in the 90 days preceding dose augmentation was strongly associated with failure of dose augmentation (HR 2.98 (2.27–3.93); p<0.0001). Persons with CD were less likely to receive corticosteroids but more likely to switch anti-TNF agents than persons with UC. Conclusions Rates of adverse IBD outcomes remain high after dose augmentation, particularly when dose augmentation is undertaken shortly after (or in response to) one of these adverse events. Our data suggest that dose augmentation may not be as effective as uncontrolled observational studies have indicated. Funding Agencies None
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".