Macrolide-Based Regimens and Mortality in Hospitalized Patients With Community-Acquired Pneumonia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Macrolides are used to treat pneumonia despite increasing antimicrobial resistance. However, the immunomodulatory properties of macrolides may have a favorable effect on pneumonia outcomes. Therefore, we systematically reviewed all studies of macrolide use and mortality among patients hospitalized with community-acquired pneumonia (CAP). METHODS: All randomized control trials (RCTs) and observational studies comparing macrolides to other treatment regimens in adults hospitalized with CAP were identified through electronic databases and gray literature searches. Primary analysis examined any macrolide use and mortality; secondary analysis compared Infectious Diseases Society of America/American Thoracic Society guideline-concordant macrolide/beta-lactam combinations vs respiratory fluoroquinolones. Random effects models were used to generate pooled risk ratios (RRs) and evaluate heterogeneity (I(2)). RESULTS: We included 23 studies and 137,574 patients. Overall, macrolide use was associated with a statistically significant mortality reduction compared with nonmacrolide use (3.7% [1738 of 47,071] vs 6.5% [5861 of 90,503]; RR, 0.78; 95% confidence interval [CI], .64-.95; P = .01; I(2)= 85%). There was no survival advantage and heterogeneity was reduced when analyses were restricted to RCTs (4.6% [22 of 479] vs 4.1% [25 of 613]; RR, 1.13; 95% CI, .65-1.98; P = .66; I(2)= 0%) or to patients treated with guideline-concordant antibiotics (macrolide/beta-lactam, 5.3% [297 of 5574] vs respiratory fluoroquinolones, 5.8% [408 of 7050]; RR, 1.17; 95% CI, .91-1.50; P = .22; I(2)= 43%). CONCLUSIONS: In hospitalized patients with CAP, macrolide-based regimens were associated with a significant 22% reduction in mortality compared with nonmacrolides; however, this benefit did not extend to patients studied in RCTs or patients that received guideline-concordant antibiotics. Our findings suggest guideline concordance is more important than choice of antibiotic when treating CAP.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".