A Cost-Effectiveness Analysis of Azithromycin for the Prevention of Acute Exacerbations of Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Abstract Rationale Daily oral azithromycin therapy can reduce the risk of acute exacerbations of chronic obstructive pulmonary disease (COPD). However, given its adverse events and additional costs, it is not known whether adding long-term azithromycin as an adjunct therapy to inhaled pharmacotherapy is cost effective. Objectives The objective of this study was to evaluate the cost-effectiveness of add-on azithromycin therapy in COPD as recommended by contemporary COPD management guidelines. Methods We extended a previously validated Canadian COPD policy model to include azithromycin-related inputs and outcomes. The cost-effectiveness of azithromycin was evaluated over a 20-year time horizon in patients who continue to exacerbate despite receiving maximal inhaled therapies. The benefit of azithromycin was modeled as a reduction in exacerbation rates. Adverse events included cardiovascular death, hearing loss, gastrointestinal symptoms, and antimicrobial resistance. The incremental cost-effectiveness ratio (ICER) was calculated with costs in 2020 Canadian dollars ($) and quality-adjusted life-years (QALYs) discounted at 1.5% per year. The analysis was stratified among patient subgroups based on exacerbation histories. Results In patients with a positive exacerbation history (one or more events in the previous 12 mo), azithromycin was associated with $49,732 costs, 7.65 QALYs, and 10.95 exacerbations per patient over 20 years. The corresponding values were $48,436, 7.62, and 11.86 for the reference group, resulting in an ICER of $43,200 per QALY gained. In patients defined as frequent exacerbators (two or more moderate or one or more severe events in the past 12 mo), the ICER was reduced to $8,862 per QALY gained. In patients with no history of exacerbation, azithromycin had lower QALYs and higher costs than the reference group. Conclusions Add-on azithromycin is cost effective in patients with a recent history of exacerbations at commonly accepted willingness-to-pay thresholds of $50,000–$100,000/QALY. Guidelines should consider recommending add-on azithromycin for patients who had at least one moderate or severe exacerbation in the past year, albeit more information about treatment efficacy would strengthen this recommendation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".