Epidemiology and treatment of carbapenemase-producing <i>Enterobacteriaceae</i> bacteremia in a community hospital setting
Bibliographic record
Abstract
Background: Carbapenemase-producing Enterobacteriaceae (CPE) infections are a rapidly evolving global threat. With bacteremia mortality rates exceeding 40%, limited consensus exists regarding optimal antimicrobial treatment. Objectives: To characterize our institution’s CPE bacteremia population and describe the antimicrobial regimens used for treatment. Methods: A dual-centre retrospective chart review was conducted of adult CPE bacteremia patients admitted between January 1, 2010 and April 30, 2017. Baseline demographics included out-of-country hospitalization, causative organism, and susceptibilities. Treatment details included antimicrobial agent, dosing regimen, and use of monotherapy or combination therapy. Clinical outcomes included 30-day all-cause mortality and 30-day re-admission rates. Results: Thirteen cases of CPE bacteremia were reviewed. Nine patients had previously been hospitalized in the Indian subcontinent. Twelve isolates produced the New Delhi metallo-beta-lactamase-1 (NDM-1). All isolates were sensitive (n = 8) or intermediate (n = 5) to tigecycline. An equal number of cases were treated with monotherapy (n = 6) and combination therapy (n = 6). The most commonly prescribed antimicrobials were colistin (n = 7) and tigecycline (n = 8). The overall 30-day mortality and re-admission rates were 54% (7/13) and 50% (3/6), respectively, although the effect of potential confounders such as causative CPE, monomicrobial or polymicrobial infection, or delay in therapy were not considered. Conclusions: This study highlights the largest Canadian CPE bacteremia cohort to date. CPE bacteremia most commonly occurred in patients with prior hospitalization in the Indian subcontinent. Based on our antimicrobial susceptibility testing results, tigecycline may have a role as part of empiric therapy at our institution.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".