190Adequacy of Prior Antibiotic Use in Patients with Clostridium difficile Infection: A Resptrospective Analysis
Bibliographic record
Abstract
Background. Clostridium difficile infections (CDI) are the main cause of healthcare associated diarrhea. Inappropriate use of antibiotics (AB) is a significant risk factor for CDI. The aim of this study is to determine the proportion of CDI patients who received inappropriate AB prior to developing CDI. Methods. This is a retrospective cohort study to assess the proportion of newly diagnosed CDI patients whose AB regimen could have been optimized prior to CDI episode at a tertiary medical center. The study population, identified through a Microbiology lab database, included patients with a first episode of CDI between February and May 2013. Each patient's AB regimen, indications and culture results were reviewed using a standardized form. AB therapy was defined as inappropriate when ≥1 of the following were met: (1) absence of a clear and valid indication to initiate therapy; (2) deviation of initial empiric therapy from local recommendations; (3) inappropriate de-escalation or duration of therapy. Inappropriateness of AB was determined by a panel of experts in CDI and antibiotic stewardship. Ethics approval was obtained before starting the chart review. Results. 50 patients with CDI were identified (52% male, mean age, 76yrs). 47% had received AB in the previous 5 weeks and 53% received therapy on the day of CDI diagnosis. ABs prescribed were 54% penicillin, 44% quinolones, 20% cephaloporines and 18% carbapenems for the following conditions: penumonia 24%, urinary tract infection 11%, skin and soft tissue infection 12%, 32% for other indications. 10% received antibiotics for prophylaxis. 49% (n = 24) of patients with CDI had received an inappropriate AB in the previous 5 weeks. The main sources of inappropriate use were: failure to de-escalate (n = 9; 38%); inappropriate duration (n = 13; 54%); absence of clear diagnosis on day 3 of therapy (n = 4; 20%); and failure to switch to a narrower spectrum AB despite diagnostic test results and clinical improvement (n = 9; 38%). Conclusion. A significant proportion of CDI cases received an inappropriate course of AB in the previous 5 weeks illustrating the need to improve AB use in our hospital. Antimicrobial stewardship has been initiated and a prospective audit is being planned to investigate its effect on CDI rates. 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".