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Record W2222600673 · doi:10.1093/ofid/ofu051.167

1795Above and Beyond Individual Exposure: Ward-level Antibiotic Prescribing Is the Principal Predictor of Increased Clostridium difficile Infection (CDI) Risk

2014· article· en· W2222600673 on OpenAlexaffabout
Kevin A. Brown, Nick Daneman, David N. Fisman

Bibliographic record

VenueOpen Forum Infectious Diseases · 2014
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsClostridium difficileMedicineAntibioticsVancomycinClostridiumIntensive care medicineInternal medicineMicrobiologyBacteriaStaphylococcus aureus

Abstract

fetched live from OpenAlex

Background. Recent research on Clostridium difficile transmission suggests that exposure to spores from symptomatic patients may not completely explain hospital-acquired infections. Antibiotic exposure has been shown to be a primary risk factor for not only C. difficile infection (CDI), but also for asymptomatic colonization with C. difficile. In this study, we sought to discern the relative impacts of individual-level and ward-level antibiotic exposure on patient risk. Methods. A cohort study design was used to assess the association of antibiotic exposure with the incidence of CDI among patients admitted to Sunnybrook hospital in Toronto, Canada. The source cohort consisted of all patients over 18 years old, without a previous CDI diagnosis, and hospitalized in an acute care ward at Sunnybrook hospital in a period spanning June 1, 2010 to May 31, 2012 (n = 47,241). Results. Across wards, patients received antibiotics for 22% to 58% of days. We found that ward-level prescribing was strongly associated with increased CDI risk. Based on weighted linear regression, each 10% increase in ward-level ABx prescribing was associated with a 3.9 per 1,000 patient-days (95% CI: based on weighted linear regression: 2.1 to 5.6, p < 0.001) increase in CDI incidence, and explained 56% of ward level variation in CDI rates (figure). In multilevel Poisson analyses controlling for time since admission, age, individual-level antibiotic receipt, previous hospitalizations, infection pressure, and ICU admission, ward-level antibiotic prescribing was the strongest predictor of CDI risk. Each 10% increase in ward-level ABx prescribing was associated with a doubling of risk (RR = 1.83, 95% CI: 1.42 to 2.35); adjusted risk in the highest prescribing wards was thus 6 times higher (RR = 6.13, 95% CI: 2.86 to 12.98) than risk in the lowest prescribing wards. In comparison, individual-level exposure was associated with only a doubling of risk (RR = 2.06, 95% CI: 1.37 to 3.10). Conclusion. Although many studies have considered the impact of individual-level risk factors on CDI, we found that ward-level antibiotic prescribing better explains CDI risk, and this suggests that asymptomatic colonized patients may be driving infection spread. This research has major implications for infection control, hospital hygiene, and antimicrobial stewardship. 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)0.001
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.024
GPT teacher head0.279
Teacher spread0.255 · 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
Published2014
Admission routes2
Has abstractyes

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