The Cost-effectiveness of Biannual Serum Urate (SU) Monitoring after Reaching Target in Gout: A Health Economic Analysis Comparing SU Monitoring
Bibliographic record
Abstract
OBJECTIVE: The 2012 American College of Rheumatology gout management guidelines recommend monitoring serum urate (SU) every 6 months after target SU has been achieved. Our objective was to determine through modeling whether this testing would be cost-effective, considering financial cost, quality of life, and estimated change in adherence. METHODS: A cost-utility analysis was completed with a 3-arm model: (1) no regular urate monitoring; (2) annual urate monitoring; and (3) biannual urate monitoring. Inputs to the model for health-related quality of life, flare rate, and treatment location were drawn from the medical literature and modeled over a lifetime horizon. RESULTS: No monitoring was the least costly (Australian$6974) but least effective [13.51 quality-adjusted life-yrs (QALY)], while annual urate monitoring [A$7117; 13.53 QALY; incremental cost-effectiveness ratio (ICER) A$13,678/QALY gained] and biannual monitoring [A$7298; 13.54 QALY; ICER A$15,420 per QALY gained] were both cost-effective alternatives in base case analysis. Sensitivity analysis on both an individual component level and a probabilistic sensitivity analysis (PSA) demonstrated that the result was robust to changes in input variables. An improvement in adherence of ≥ 3.5% with biannual monitoring was all that was required to demonstrate cost-effectiveness. In PSA, the probability of biannual monitoring was 78%, no monitoring was 20%, and annual monitoring was 2%. CONCLUSION: The results suggest that biannual SU monitoring after attaining target SU is the most cost-effective, compared with no testing and annual testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".