Nosocomial Gram-negative bacteremia in intensive care: epidemiology, antimicrobial susceptibilities, and outcomes
Bibliographic record
Abstract
OBJECTIVES: To describe the epidemiology, antimicrobial susceptibilities, treatment, and outcomes of intensive care unit (ICU)-acquired Gram-negative bacteremia. METHODS: Patients with ICU-acquired Gram-negative bacteremia from 2004 to 2012 were reviewed retrospectively. Independent predictors of mortality were examined using multivariable Cox regression. RESULTS: Seventy-eight cases of ICU-acquired Gram-negative bacteremia occurred in 74 patients. The infection rate was 0.97/1000 patient-days. Mean patient age was 55 years, 62% were male. The most common admission diagnoses were respiratory failure (34%) and sepsis/septic shock (45%). Mortality was 35% at 30 days. The most common source of bacteremia was pneumonia (33%). Of 83 Gram-negative isolates, Escherichia coli (20%) and Pseudomonas aeruginosa (18%) were most common. For aerobic isolates, susceptibilities to ciprofloxacin (61%) and piperacillin/tazobactam (68%) were low. For pseudomonal isolates, susceptibilities to ciprofloxacin (53%), piperacillin/tazobactam (67%), and imipenem (53%) were equally disappointing. Adequate empiric antimicrobial therapy was prescribed in 85% of bacteremia cases. On multivariable analysis, adequate empiric therapy (adjusted hazard ratio (aHR) 0.38, 95% confidence interval (CI) 0.16-0.89), immune suppression (aHR 3.4, 95% CI 1.4-8.3), and coronary artery disease (aHR 4.5, 95% CI 1.7-11.9) were independently associated with 30-day mortality. CONCLUSIONS: ICU-acquired Gram-negative bacteremia is associated with high mortality. Resistance to ciprofloxacin, piperacillin/tazobactam, and carbapenems was common. Coronary artery disease, immune suppression, and inadequate empiric antimicrobial therapy were independently associated with increased mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".