P383 Increasing treatment time on REMICADE® (infliximab) predicts subsequent long-term retention in stable infliximab inflammatory bowel disease patients in Canada
Bibliographic record
Abstract
Background: A high percentage of patients treated with anti-TNF agents discontinue therapy. The objective of this analysis was to determine the long-term retention patterns of stable Canadian IBD patients treated with REMICADE® (infliximab [IFX]). Methods: Using IMS Brogan's™ database of Canadian private and public insurance claims data, our analysis included IBD patients with: (1) first IFX claim between Jan 2008-May 2015; (2) no IFX claims 12 months prior to the initial claim; (3) ≥1 claim for any drug 12 months after the initial IFX claim; and (4) ≥1 claim for any non-IFX drug 4 months after May 2015. Retention was measured at 12-month intervals and unadjusted odds ratios were determined. Within-group analyses compared 12 month retention by number of years on IFX and considered cohorts of patients according to age group, gender and previous biologic experience. Results: 4,360 patients had ≥2 years of claims history and had been on IFX for ≥1 year. Within-group comparisons showed that the probability of being retained on IFX in subsequent 12 month periods increased with cumulative prior time on IFX. Patients on IFX for 2 to 5 years showed significantly higher retention in the subsequent 12 months compared to patients on IFX for only 1 year (p<0.05). Similar trends were observed across both genders, in patients 19–64 years of age, and for patients who were biologic-naïve. Table 1. Patients retained 12 months later (%) Table 2. Odds ratio of being retained (p<0.05 unless noted otherwise) Conclusions: Real world patients treated with IFX have excellent long-term retention. Previous duration of IFX treatment appears to predict better future retention, becoming statistically significant after 2 years. The results were robust and consistent amongst various subgroups of stable Canadian IBD patients.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".