Five years of nosocomial Gram-negative bacteremia in a general intensive care unit: epidemiology, antimicrobial susceptibility patterns, and outcomes
Bibliographic record
Abstract
OBJECTIVES: Nosocomial Gram-negative bacteremia in the critically ill is associated with significant morbidity and mortality. This study provides epidemiological and antimicrobial susceptibility data for nosocomial Gram-negative bacteremia in a general intensive care unit (ICU) over a five-year period. METHODS: Positive blood cultures from January 1, 1999 to December 31, 2003 were reviewed for microbial etiology and susceptibilities. Patient charts were reviewed to determine the source of infection and outcome. RESULTS: Forty-five nosocomial Gram-negative bacteremias occurred in 44 patients. Infection rates of 6.9/1000 admissions and 11.3/10,000 patient days remained stable. Admitting diagnoses included respiratory failure, solid organ transplant, post-surgery, and multi-trauma. Seven bacterial species were identified; Pseudomonas aeruginosa and Enterobacter spp were most common. Sources of bacteremia included pneumonia (48.9%), and central venous catheterization (22.2%). Antimicrobial susceptibilities were highest for imipenem, gentamicin, tobramycin, ceftazidime, and piperacillin-tazobactam. Ciprofloxacin susceptibility was inferior to imipenem, gentamicin, and tobramycin (p < 0.05). Mortality rates were 53.3% in the ICU, and 60% for overall hospitalization. Average length of ICU stay was 50.5 days compared to 6.13 days for all-comers. CONCLUSIONS: Nosocomial Gram-negative bacteremia is associated with marked morbidity and mortality in critically ill patients. Significant resistance to ciprofloxacin was demonstrated. Empiric treatment regimens should be based on unit-specific data.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".