Exploring the Potential Cost-Effectiveness of Patient Decision Aids for Use in Adults with Obstructive Sleep Apnea
Bibliographic record
Abstract
BACKGROUND: There is increasing evidence highlighting the effectiveness of patient decision aids (PtDAs), but evidence supporting their cost-effectiveness is lacking. We consider patients with obstructive sleep apnea (OSA), in whom a PtDA may decrease nonadherence to treatment by empowering patients to receive the option that is most congruent with their own values. OBJECTIVE: To determine the potential costs and benefits of delivering a PtDA to patients with moderate OSA. METHODS: A Markov cohort decision-analytic model was developed for patients with moderate OSA, comparing a PtDA to usual care over 5 years from a societal perspective. Data for patient preference for treatment options was taken from a recent randomized crossover trial, event data (cardiovascular, motor vehicle accidents) came from national databases and published literature. Potential improvements in adherence are unknown, so we considered a realistic range of values. Outcome measures were 5-year costs (in 2010 Canadian dollars), quality-adjusted life years (QALYs), and the incremental cost-effectiveness ratio (ICER). RESULTS: When adherence to treatment was unchanged, the PtDA strategy was dominated by incurring lower QALYs and higher costs. When nonadherence was decreased by 20% in the PtDA arm (corresponding to an increase in adherence from 63% to 70% for continuous positive airway pressure and from 77% to 82% for mandibular advancement splints in year 1), the ICER fell to $62,414/QALY. Costs associated with the treatment devices and delivering the PtDA had the greatest effect on cost-effectiveness. LIMITATIONS: The model relies on surrogate measures and opinions for key parameters. CONCLUSIONS: The cost-effectiveness of PtDAs will depend on contextual factors, but a framework is described for properly considering their long-term cost-effectiveness. A number of important questions around the appropriateness of benefit measurement for PtDA trials are highlighted.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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) | 0.006 | 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".