Risk Factors Associated With Resistance to Ciprofloxacin in Clinical Bacterial Isolates From Intensive Care Unit Patients
Bibliographic record
Abstract
OBJECTIVE: To determine risk factors and outcomes associated with ciprofloxacin resistance in clinical bacterial isolates from intensive care unit (ICU) patients. DESIGN: Prospective cohort study. SETTING: Twenty-bed medical-surgical ICU in a Canadian tertiary care teaching hospital. PATIENTS: All patients admitted to the ICU with a stay of at least 72 hours between January 1 and December 31, 2003. METHODS: Prospective surveillance to determine patient comorbidities, use of medical devices, nosocomial infections, use of antimicrobials, and outcomes. Characteristics of patients with a ciprofloxacin-resistant gram-negative bacterial organism were compared with characteristics of patients without these pathogens. RESULTS: Ciprofloxacin-resistant organisms were recovered from 20 (6%) of 338 ICU patients, representing 38 (21%) of 178 nonduplicate isolates of gram-negative bacilli. Forty-nine percent of Pseudomonas aeruginosa isolates and 29% of Escherichia coli isolates were resistant to ciprofloxacin. In a multivariate analysis, independent risk factors associated with the recovery of a ciprofloxacin-resistant organism included duration of prior treatment with ciprofloxacin (relative risk [RR], 1.15 per day [95% confidence interval {CI}, 1.08-1.23]; P<.001), duration of prior treatment with levofloxacin (RR, 1.39 per day [95% CI, 1.01-1.91]; P=.04), and length of hospital stay prior to ICU admission (RR, 1.02 per day [95% CI, 1.01-1.03]; P=.005). Neither ICU mortality (15% of patients with a ciprofloxacin-resistant isolate vs 23% of patients with a ciprofloxacin-susceptible isolate; P=.58) nor in-hospital mortality (30% vs 34%; P=.81) were statistically significantly associated with ciprofloxacin resistance. CONCLUSIONS: ICU patients are at risk of developing infections due to ciprofloxacin-resistant organisms. Variables associated with ciprofloxacin resistance include prior use of fluoroquinolones and duration of hospitalization prior to ICU admission. Recognition of these risk factors may influence antibiotic treatment decisions.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".