P64 The impact of anti-TNF and thiopurine therapy on the natural history of Crohn’s disease: a population-based study
Bibliographic record
Abstract
<h3>Introduction</h3> It has been hard to demonstrate if early immunosuppression alters resection rates for Crohn’s disease (CD). We studied a population-based cohort from Cardiff to evaluate the impact of both anti-TNF therapy and thiopurines on the natural history of CD, including surgical resection rates. <h3>Methods</h3> This was a retrospective population-based cohort study of all patients diagnosed with CD whilst resident in Cardiff and nearby towns over 12 years 2005–2016. The primary outcome was the impact of therapy on the time to first resection surgery up to 5 years for patients receiving early sustained use (ESU): drug started within one year of diagnosis and continued at least 3 months, versus never use (NU). A propensity score (PS) was calculated. Inverse probability of treatment weighting (IPTW) based on the PS was used so that confounders (baseline Montreal classification, smoking, steroid use, serum albumin) was similar in both the treated and untreated groups. To address immortal time bias (ITB) if an outcome occurs after diagnosis but before the start of therapy, then this time segment is attributed to the untreated group. Therefore, <i>n</i> represents time segments, not individual patients, in statistical analyses. <h3>Results</h3> 419 CD cases were studied. With IPTW there was a significant reduction in risk of surgical resection with ESU anti-TNF vs NU (p=0.0026,<i> n</i>=460 (57 ESU vs 403 NU segments), hazard ratio (HR) 0.28, 95% confidence interval (CI) 0.12 to 0.62) but not with ESU thiopurine vs NU (p=0.39, <i>n</i>=545, HR 0.82, 95% CI 0.52 to 1.29). 138/419 patients received any anti-TNF therapy prior to resection, while 57 had ESU. Probability of avoiding surgery in ESU vs NU at 1 year (98% v 83%), 2 years (95% vs 80%), and 5 years (91% vs 75%). See figure 1. After 5 years the resection rates converged with wider CIs. <h3>Conclusions</h3> Strengths of our study include the individual patient detail conferred from hospital record data collection, contributing to a PS and IPTW to account for confounders, and removal of ITB. ITB artificially protracts treatment arm survival, therefore its removal portrays a more accurate comparison. With this, we demonstrate a significant reduction in surgical resection rates at 5 years in ESU anti-TNF therapy vs NU. Larger numbers are needed to analyse benefits beyond 5 years.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".