Re-evaluation of Hospital Screening and Transmission Control Programs for Extended-Spectrum β-Lactamase-Producing Enterobacteriaceae in Toronto
Bibliographic record
Abstract
Background. Antibiotic resistance in Enterobacteriaceae (E) is recognized as a public health crisis, and hospital outbreaks due to transmission of extended-spectrum β-lactamase-producing (ESBL)-E are frequently reported. Nonetheless, infection control strategies in a hospital setting remain unclear. We compared the incidence of hospital-onset (HO) and community-onset (CO) ESBL-E bacteremia in Toronto hospitals with and without ESBL-E screening/control protocols to assess the potential benefit of these programs. Methods. We conducted a multi-center retrospective cohort study comparing the incidence of ESBL-E E. coli and Klebsiella spp. bacteremia from 2010 to 2014 in 6 screening and 6 non-screening Toronto-area hospitals. The primary outcome was the incidence of HO-ESBL-E bacteremia. A first positive blood culture obtained greater than or less than 72 hours post-admission was defined as HO- or CO-ESBL-E bacteremia, respectively. Results. A total of 1191 unique patients were identified, including 1031 cases of E. coli and 160 cases of Klebsiella spp. ESBL-E bacteremia. The overall incidence of ESBL-E bacteremia increased from 0.135 to 0.257 per 1000 in-patient days (IPD) over the 5-year study period. The incidence of HO-ESBL-E bacteremia was consistently higher in non-screening than screening hospitals, rising from 0.057 (versus 0.032) in 2010 to 0.071 (versus 0.051) in 2014 per 1000 IPD (figure 1A). The HO/CO ratio was also greater in non-screening versus screening hospitals (0.976 versus 0.441). The incidence of CO-ESBL-E bacteremia was consistently higher in screening than non-screening hospitals, rising from 0.097 (versus 0.083) in 2010 to 0.252 (versus 0.139) in 2014 per 1000 IPD (figure 1B). Among ESBL-E E. coli and Klebsiella spp., antibiotic susceptibility did not change over the study period and was as follows: ciprofloxacin 17.7% and 26.7%, gentamicin 63.7% and 52.1%, and ertapenem 99.1% and 95.8%, respectively. Conclusion. The incidence of ESBL-E bacteremia steadily increased from 2010 to 2014. ESBL-E screening/control programs were associated with a persistently lower incidence of HO bacteremia, suggesting that ESBL-E screening may prevent nosocomial transmission. Cost-benefit analyses of such programs should be conducted. Disclosures. All authors: No reported disclosures.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".