Reducing empirical use of fluoroquinolones for Pseudomonas aeruginosa infections improves outcome
Bibliographic record
Abstract
BACKGROUND: We previously reported ciprofloxacin resistance (CR) and empirical use of fluoroquinolones as predictors of mortality in patients infected with Pseudomonas aeruginosa in a case-control study. Here, we assessed the clinical impact of reducing empirical fluoroquinolone use for P. aeruginosa infections in hospitalized patients by performing a follow-up study in 2005-06 [period 2 (P2)] and comparing this with prior data from 2001-02 [period 1 (P1)]. METHODS: Medical charts of infected patients who received at least 72 h of antibiotic therapy were reviewed. Patients were subgrouped based on the susceptibility of infected strains into the CR or ciprofloxacin-susceptible group. Antibiograms, patient and treatment variables and outcome measures were compared between groups and between study periods. RESULTS: Study patients were elderly (median age, 76 years), had a median of three co-morbidities and a median APACHE II score of 13. Most (75%) had pneumonia or urosepsis. Empirical use of fluoroquinolones was reduced by 30% in P2 versus P1, with a corresponding 39% increase in piperacillin/tazobactam use. The resultant positive impact observed in the CR group during P2 includes shortened delay to receipt of effective therapy (1 versus 3.5 days, P < 0.0001), reduced length of stay (13 versus 16 days, P = 0.03) and 2-fold lower mortality (9% versus 22%, P = 0.05). Susceptibility of P. aeruginosa improved by 10% to all antipseudomonal agents tested. CONCLUSIONS: In settings where high rates of fluoroquinolone resistance exist, use of non-fluoroquinolone-based empirical regimens for P. aeruginosa infections improves patient outcomes and organism susceptibility over time.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".