A103 EARLY INITIATION OF ANTI-TNF THERAPY IS COST-SAVING COMPARED TO LATE INITIATION FOR PATIENTS WITH CROHN’S DISEASE
Bibliographic record
Abstract
Anti-TNF therapies are effective for the induction and maintenance of remission in patients with Crohn’s disease (CD), and are generally prescribed when patients fail to respond to conventional, less-costly medical therapies including steroids and immunomodulators. Our recent retrospective study showed that early initiation (within two years of diagnosis) of anti-TNF therapies reduced rates of surgery and loss of response requiring dose escalation. However, the cost effectiveness of this strategy is unknown, given the expensive nature of these medications. The aim of this study was to determine if early initiation of anti-TNF therapy is more cost-effective compared to delayed initiation for the management of CD. A Markov model was constructed to simulate the progression of patients with CD after the initiation of either infliximab or adalimumab. Using this model, we compared the lifetime cost-effectiveness of early (≤2 years after diagnosis) versus late (>2 years after diagnosis) initiation of anti-TNF therapy using published loss of response rates. Transition probabilities were determined through a literature search and costs were obtained from the Alberta Disease Registry. Utility scores were obtained from published literature using the Standard Gamble Approach. Deterministic and probabilistic sensitivity analysis was used to characterize uncertainty related to input parameters. Over a patient’s lifetime, early initiation of infliximab yielded an additional 1.02 quality-adjusted life years (QALYs) and saved $18,054 compared to late initiation of infliximab. Early initiation of adalimumab yielded an additional 0.74 QALYs and saved $18,526 compared to late initiation of adalimumab. At a willingness-to-pay threshold of $50,000 per QALY, early initiation of both infliximab and adalimumab had a 68% chance of being cost-effective, while late initiation had a 32% chance of being cost-effective. Based on our current model, early initiation of either infliximab or adalimumab is cost-saving and dominates late initiation for patients with CD. These results may serve to support early treatment with anti-TNF therapy from both a cost and patient outcome perspective. Figure 1. Model structure diagram. CIHR
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".