Evaluating the Effectiveness of an Antimicrobial Stewardship Intervention on Reducing the Incidence Rate of Healthcare-Associated Clostridium difficile Infection
Bibliographic record
Abstract
Background. The incidence rate of healthcare-associated Clostridium difficile infection (HA-CDI) is estimated at 1 in 100 patients. Antibiotic exposure is the most consistently reported risk factor. Strategies to reduce HA-CDI have focused on reducing antibiotic utilization. Prospective audit and feedback (PAF) is a commonly used antimicrobial stewardship intervention (ASi). The impact of this ASi on HA-CDI is equivocal. This study examines the impact of a PAF ASi on the incidence of HA-CDI. Methods. Single-site, 339 bed community-hospital in Ontario, Canada. Primary outcome is HA-CDI incidence rate. Daily PAF ASi is the exposure variable. PAF ASi is implemented across wards in a non-randomized stepped wedge design starting in July 2013. Criteria for ASi; any intravenous antibiotic use for ≥48 hours, or any fluoroquinolone or cephalosporin use for ≥48 hours. HA-CDI cases and model covariates aggregated by ward and month. Pooled statistical analyses done using generalized linear models with log link function. Potential clustering by ward and serial correlation of HA-CDI accounted for in final model. Other covariates tested for inclusion in final model were derived from previously published risk factors. Final pooled model was compared with random coefficient model (RCM). Goodness-of-fit (GOF) assessed using deviance statistics. Results. N = 430 observations with ASi implemented in 64 periods. The final model included; ASi, days of antibiotic therapy (DOT), previous month's CDI cases (LCDI), current month's community-associated-CDI cases (CA-CDI), length of stay (LOS) and days of hospitalization due to age over 65 years (Age), and interaction terms between ASi-DOT, LOS-Age and ASi-LCDI. ASi incidence rate ratio (IRR) is 0.42 (95% CI, 0.19–0.92). ASi-DOT interaction IRR was not significant. LCDI IRR was 1.20 (95% CI, 1.07–1.35) and CA-CDI IRR was 1.26 (95% CI, 1.12–1.42). Pooled model was equivalent to RCM and provided excellent GOF. Conclusion. Daily PAF ASi resulted in a significant reduction in HA-CDI; however, this effect was not mediated by an overall reduction in antibiotic utilization. In addition, the importance of CDI environmental pressure was demonstrated through the significant impact of both CA-CDI and LCDI on subsequent HA-CDI incidence 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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".