Economic Evaluation of Including Biomarker Testing in the Biologic Therapy Withdrawal Decision-Making Process in Non-Systemic Juvenile Idiopathic Arthritis: The International UCAN CAN-DU and CURE Study
Bibliographic record
Abstract
Objectives To assess the cost-effectiveness of including biomarker testing in the decision-making process of withdrawing biologic therapy for patients with non-systemic JIA compared to usual care. Methods A health economic model was developed to assess 3 different scenarios reflecting decision-making in response to biomarker information and what percentage of patients start biologic therapy withdrawal early (within 2 years after reaching inactive disease) including 20%, 46% and 75%, compared to usual care (74%). A 1-month cycle length and ten-year time horizon were used. Transition probabilities, costs and effects were based on data from the UCAN CAN-DU cohorts (in The Netherlands and Canada), and the Wilhelmina Children’s Hospital (Utrecht, the Netherlands), plus clinical expert opinion and the literature. Costs include drugs, biomarker testings, pediatric rheumatology visits and other hospital related costs, such as radiology investigations, laboratory testing and hospitalization. Effects were measured in quality-adjusted life years (QALYs). A probabilistic analysis was performed to reflect uncertainty Results In the analysis we compared usual care to each individual scenario. The percentage of flare-ups within the 1st year of stopping biologics are 62% for usual care, compared to 43%, 50% and 57% respectively. In usual care, the average time in active disease per patient is 25 months (21%), where the scenarios show 23 months (19%), 24 months (20%) and 25 months (21%). The average time off biologics in usual care is 26 months (22%), where the scenarios show 26 months (22%), 29 months (24%), 30 months (25%). The absolute costs are €79,051 for usual care, compared to €78,315, €77,354, and €76,745 respectively, resulting in incremental costs of €−737, €−1,697, and €−2,306. The absolute QALYs are 7.470 for usual care, compared to 7.535, 7.521, and 7.490 respectively, resulting in incremental QALYs of 0.065, 0.051 and 0.020. The incremental cost-effectiveness ratio for each scenario is €−11,254/QALY, €−33,301/QALY and €−117,145/QALY. The Net Health Benefit, for a willingness-to-pay (WTP) threshold of €50,000/QALY, is 0.080, 0.085 and 0.066. For this WTP, the probabilistic analysis shows that the probability of biomarker testing being cost-effective was 100% for all scenarios. Conclusion The inclusion of biomarker testing in the decision-making process of withdrawing biologic therapy in JIA is likely to be cost-effective. The benefits of biomarker-guided therapy withdrawal are preference sensitive and will depend on the balance between how patients/families and physicians tradeoff between time off biologics (and consequently cost savings) and the (avoidable) risk of flare-up due to early withdrawal.
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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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".