Macrolide Use in the Treatment of Critically Ill Patients with Pneumonia: Incidence, Correlates, Timing and Outcomes
Bibliographic record
Abstract
BACKGROUND: Macrolide antibiotics are commonly used to treat pneumonia despite increasing antimicrobial resistance. Evidence suggests that macrolides may also decrease mortality in severe sepsis via immunomodulatory properties. OBJECTIVE: To evaluate the incidence, correlates, timing and mortality associated with macrolide-based treatment. METHODS: A population-based cohort of critically ill adults with pneumonia at five intensive care units in Edmonton, Alberta, was prospectively followed over two years. Data collected included disease severity (Acute Physiology and Chronic Health Evaluation [APACHE] II score), pneumonia severity (Pneumonia Severity Index score), comorbidities, antibiotic treatments at presentation and time to effective antibiotic. The independent association between macrolide-based treatment and 30-day all-cause mortality was examined using multivariable Cox regression. A secondary exploratory analysis examined time to effective antimicrobial therapy. RESULTS: The cohort included 328 patients with a mean Pneumonia Severity Index score of 116 and a mean APACHE II score of 17; 84% required invasive mechanical ventilation. Ninety-one (28%) patients received macrolide-based treatments, with no significant correlates of treatment except nursing home residence (15% versus 30% for nonresidents [P=0.02]). Overall mortality was 54 of 328 (16%) at 30 days: 14 of 91 (15%) among patients treated with macrolides versus 40 of 237 (17%) for nonmacrolides (adjusted HR 0.93 [95% CI 0.50 to 1.74]; P=0.8). Patients who received effective antibiotics within 4 h of presentation were less likely to die than those whose treatment was delayed (14% versus 17%; adjusted HR 0.50 [95% CI 0.27 to 0.94]; P=0.03). CONCLUSIONS: Macrolide-based treatment was not associated with lower 30-day mortality among critically ill patients with pneumonia, although receipt of effective antibiotic within 4 h was strongly predictive of survival. Based on these results, timely effective treatment may be more important than choice of antibiotics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".