Clinical outcome of empiric antimicrobial therapy of bacteremia due to extended-spectrum beta-lactamase producing Escherichia coli and Klebsiella pneumoniae
Bibliographic record
Abstract
BACKGROUND: Prompt administration of adequate empiric antimicrobial therapy is a major determinant influencing the outcome of serious infections. The objective of this study was to describe empiric antimicrobial therapy employed and assess its effect on the outcome of patients bacteremic with extended-spectrum beta-lactamase (ESBL) producing Escherichia coli and Klebsiella pneumoniae. FINDINGS: A retrospective surveillance study of all patients with bacteremias caused by ESBL-producing E. coli and K. pneumoniae (EK-ESBL) from 2000-2007 in the Calgary Health Region was conducted. Data were available for 79 episodes of bacteremia among 76 patients. Forty-four (56%) were male, the median age was 70.0 yrs [interquartile range (IQR) 60.6-70.1 yrs], and 72 (91%) episodes were E. coli. Seventy-four episodes (94%) were treated with empiric therapy within the first 48 hours. A non-statistically significant increased mortality occurred in those treated empirically with a beta-lactam/beta-lactamase inhibitor combination (6/16; 38% vs. 10/53; 18%; p = 0.063) while empiric carbapenem therapy was associated with lower mortality (0/10 died vs. 16/53 (30%), p = 0.089). Only 42 (53%) episodes received adequate therapy within the first 48 hours. The median time to first adequate antibiotic therapy was 41.0 hours [IQR 5.8-59.5] (n = 75). The case-fatality rate was not different among those that received adequate compared to inadequate therapy by 48 hours as compared to inadequate empiric therapy (9/42; 21% vs. 7/37; 19%; p = 1.0). CONCLUSION: Inadequate empiric therapy is common among patients with EK-ESBL bacteremia in our region but was not associated with adverse mortality outcome.
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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 | 0.005 |
| 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 | 0.001 | 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".