The Predictive Utility of Screening Extended-Spectrum Beta-Lactamase Swabs in Selecting Empiric Antibiotics for Sterile-Site Infections
Bibliographic record
Abstract
Background. Extended-spectrum beta-lactamase (ESBL) screening swabs are performed widely as a means of identifying ESBL carriers and preventing transmission. We tested whether these results could have unintended benefits in predicting the ESBL status of subsequent infections with E. coli and Klebsiella spp. experienced by these patients. Methods. A retrospective cohort study was conducted using an auto-populated antimicrobial stewardship database. Patients were included if 18 years of age or older, admitted to hospital between 2010 and 2015, and screened for ESBL prior to developing an E. coli or Klebsiella spp. sterile-site infection Test characteristics were derived using the screening swab ESBL result as test variable predicting the presence or absence of ESBL in the sterile site Enterobacteriaceae species. Results. A total of 2254 patients had sterile cultures positive for E. coli or Klebsiella spp. during the study period, and a total of 73 of 2254 patients had prior screening for ESBL (3%). Within this sample population a total of 27 of 73 screening swabs were positive for ESBL (37%), and a total of 28 of 73 sterile cultures were positive for ESBL (38%). The overall specificity of ESBL swabs was 89% (75%–96%) and the overall sensitivity was 79% (59%–91%). The positive predictive value was 81% (61%–93%) and the negative predictive value was 87% (73%–95%). The positive likelihood ratio was 7 (3–17) and the negative likelihood ratio was 0.24 (0.12–0.49). In the United States, where the prevalence of ESBL among Enterobacteriaceae sterile isolates is as high as 0.23, a positive ESBL screening swab would yield a post-test probability of 0.68 of third-generation cephalosporin resistance and a negative swab would yield a post-test probability of 0.067 (based on the derived likelihood ratios). Conclusion. Prior ESBL screening swab results are a useful tool for predicting ESBL status of sterile site E. coli and Klebsiella spp. infections. The results of this study suggest that those with a positive ESBL screening swab should receive empiric carbapenem therapy for positive E. coli and Klebsiella spp. sterile cultures. Patients with negative ESBL screening swabs that are not critically ill may be treated with a carbapenem-sparing empiric regimen while awaiting sensitivity results. 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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".