Long-Term Azithromycin Therapy to Reduce Acute Exacerbations in Patients With Severe Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Rationale According to clinical trials, azithromycin taken daily for 1 year, decreased exacerbations of chronic obstructive pulmonary disease (COPD). Objectives Effectiveness evaluation of long-term azithromycin to reduce exacerbations in severe COPD patient on optimal therapy in real-life practice. Methods We conducted a retrospective observational study of severe COPD patients who were prescribed azithromycin (PA)(250 mg, at least 3 times weekly for at least 6 months). Comparison group included severe COPD patients not prescribed azithromycin (NPA). Data were extracted from clinical chart review. Main results Study included 126 PA and 69 NPA patients. They had severe airflow obstruction, mostly emphysema and one-third bronchiectasis. A predominant feature in the PA group was respiratory tract colonization with Pseudomonas aeruginosa. The mean number of exacerbations per patient per year in the PA group was 3.2 ± 2.1 before initiating azithromycin, and 2.3 ± 1.6 during following year on therapy (p < 0.001). Patients in the NPA group had 1.7 ± 1.3 and 2.5 ± 1.7 exacerbations during first and second follow-up year respectively (p < 0.001). Exacerbation changes from pre to post differed between groups (p < 0.001). Decrease in emergency visits and hospital admissions was significant in PA group. Exacerbation reductions and patient proportions having ≥2 exacerbations extended to the second year of treatment. Conclusion These data showed that long-term azithromycin reduces exacerbation numbers in severe COPD patients, and benefits persist beyond one year. Desirable effects are more likely to outweigh the risks and adverse events in patients colonized with Pseudomonas aeruginosa.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".