Duration of Hospital Admission and the Need for Empirical Antipseudomonal Therapy
Bibliographic record
Abstract
To reduce selective pressure for antimicrobial resistance, empirical use of antipseudomonal antibiotics is often reserved for patients with late-onset hospital-acquired infections. We examined the likelihood of isolating Pseudomonas aeruginosa as a function of time from hospital admission. We conducted a retrospective cohort study of all positive bacterial cultures in a tertiary-care hospital between March 2010 and November 2011. The primary outcome was the proportion of positive cultures yielding P. aeruginosa. Multivariable logistic regression was employed to assess the impact of time from admission on the likelihood of isolating P. aeruginosa, after adjusting for other important risk factors. A total of 7,668 positive cultures were obtained from 4,108 unique patients during the study interval, including 633 (8.3%) yielding P. aeruginosa. The probability of isolating P. aeruginosa increased linearly from 79/2,044 (3.9%) positive cultures obtained on admission to 153/664 (23%) in the 10th week of admission or beyond. The unadjusted odds ratio was 1.002/day (95% confidence interval [CI], 1.0016 to 1.0028; P < 0.0001); the adjusted odds ratio (aOR) was 1.0007/day (95% CI, 1.0001 to 1.0013; P = 0.02). Other important predictors of P. aeruginosa isolation included respiratory specimen type (aOR, 13.8; 95% CI, 9.1 to 21.1), recent hospital admission (aOR,1.8; 95% CI, 1.4 to 2.3), prior P. aeruginosa isolation during current admission (aOR, 4.9; 95% CI, 3.7 to 6.4), and prior antipseudomonal (aOR, 1.9; 95% CI, 1.4 to 2.5) or nonantipseudomonal (aOR, 1.8; 95% CI, 1.4 to 2.4) antibiotic exposure. It was determined that as time from admission increases, there is a linear increase in the likelihood of P. aeruginosa isolation. Any guidelines which distinguish early from late hospital-acquired infection must consider the implications of time point selection on the likelihood of inadequate P. aeruginosa empirical coverage.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".