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Record W2335958828 · doi:10.1093/ofid/ofv131.66

Evaluating the Effectiveness of an Antimicrobial Stewardship Intervention on Reducing the Incidence Rate of Healthcare-Associated Clostridium difficile Infection

2015· article· en· W2335958828 on OpenAlexaffabout
Giulio Didiodato, Leslie McArthur

Bibliographic record

VenueOpen Forum Infectious Diseases · 2015
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsRoyal Victoria Regional Health Centre
Fundersnot available
KeywordsMedicineClostridium difficileAntimicrobial stewardshipIncidence (geometry)Emergency medicineInternal medicineAntibioticsAntibiotic resistance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.068
GPT teacher head0.404
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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