Cost-Effectiveness Analysis of Hydrus Microstent for Patients with Mild to Moderate Primary Open-Angle Glaucoma in Canada
Bibliographic record
Abstract
OBJECTIVE: To assess the cost-effectiveness of Hydrus Microstent combined with cataract surgery (CS) vs. CS alone for treating patients with mild to moderate primary open-angle glaucoma (POAG). DESIGN: Cost-utility analysis using efficacy and safety results of a pivotal randomized clinical trial. SUBJECTS: Modeled cohort of patients with mild to moderate POAG and visually significant cataract. METHODS: A semi-Markov model was developed to model effects and costs over a 15-year time horizon from the Canadian public health care payer perspective for patients with mild or moderate POAG receiving Hydrus Microstent during CS vs. CS alone. The model utilizes the Hydrus Microstent for Lowering IOP in Glaucoma Patients Undergoing Cataract Surgery (HORIZON) trial patient cohort. Progression was guided using the annualized rate of progression derived from a post hoc analysis of 5-year visual field loss data from the HORIZON trial. The amount of visual field lost was mapped on a sequential addition of medications used as a proxy for irreversible progression. Costs were derived from various publicly available sources and publications. Utility values were sourced from a published analysis that conducted a mapping exercise based on Health Utilities Index mark 3 using Canadian tariffs. We conducted deterministic and probabilistic sensitivity analyses to examine the uncertainty around alternative model input values. Scenario analyses explored structural uncertainty. MAIN OUTCOME MEASURES: Total costs per patient, quality-adjusted life years (QALYs), and incremental cost-utility ratio. RESULTS: Compared with CS alone, Hydrus + CS was a dominant strategy (greater benefits and lower costs). Although life years were equivalent between the 2 treatments (11.41 years), the Hydrus + CS arm was associated with higher benefits (9.351 vs. 9.040 in QALYs). This translated into an additional 0.311 QALYs for Hydrus + CS. Total costs were lower with Hydrus + CS (Can$ 26 770 vs. Can$ 27 145) resulting in a saving of Can$ 375. Results of scenario analyses showed robustness of the model. The cost-effectiveness acceptability curve shows a probability of 85.3% of Hydrus + CS being cost-effective compared with CS alone at a willingness-to-pay threshold of 50 000/QALY. CONCLUSIONS: Hydrus Microstent combined with CS is a cost-effective long-term treatment for patients with POAG. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".