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Record W2745412579 · doi:10.1093/ofid/ofx163.1237

An Offer You Can’t Refuse: Clinical Impact of Accepting or Rejecting a Recommendation from an Antibiotic Stewardship Program

2017· article· en· W2745412579 on OpenAlexaffabout
Alex Carignan, Adam Mercier, Julie Perron, Vincent Nault, Isabelle Alarie, Cybèle Bergeron, Mathieu Beaudoin, Louis Valiquette

Bibliographic record

VenueOpen Forum Infectious Diseases · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineAntimicrobial stewardshipPiperacillin/tazobactamPsychological interventionDosingLogistic regressionIntensive care unitEmergency medicinePharmacistAuditCohortRetrospective cohort studyAntibioticsIntensive care medicinePiperacillinInternal medicineFamily medicinePharmacyAntibiotic resistanceNursing

Abstract

fetched live from OpenAlex

The outcomes associated with the acceptance or refusal of a recommendation from an antimicrobial stewardship program (ASP) on an individual level have not been studied yet. Our objective was to compare the clinical characteristics and mortality of patients for whom a recommendation from an ASP, based on prospective audit and feedback and triggered by a computerized decision support system, was accepted or refused. We performed a retrospective cohort study of all hospitalized adult patients who received intravenous or oral antimicrobials in two tertiary care hospitals in Canada between 2014 and 2016, and for whom a recommendation was issued by an ASP. We identified 1,251 recommendations throughout the study period. Among the recommendations made by the pharmacist to prescribers, 1,144 (91.5%) were accepted. The most frequent interventions were immediate scheduling end of treatment (n = 364, 29%), dosing/frequency adjustments (n = 321, 26%), streamlining (n = 251, 20%), and switching from intravenous to oral therapy (n = 247, 20%). The antimicrobials most frequently targeted by recommendations were piperacillin/tazobactam (n = 273, 22%) and fluroquinolones (n = 267, 21). Overall, the length of the antimicrobial targeted by the recommendation was significantly shorter when a recommendation was accepted (0.37 days vs. 2.11 days; P < .001). In the multiple logistic regression analysis, the independent risk factors associated with in-hospital mortality were the Charlson score, issuance of a recommendation for a patient in the intensive care unit, the duration between admission and the recommendation, issuance of a recommendation in 2016 (compared with 2014), and age of the patient. A recommendation issued on a fluoroquinolone or oral penicillin/first generation cephalosporin was associated with lower odds of mortality. After adjustment, refusal of a recommendation by the attending physician was associated with a higher, albeit nonsignificant, risk of mortality (AOR, 1.81; 95% CI, 0.89–3.68; P = .10). The duration of the antimicrobial treatment was significantly shorter when a recommendation triggered by an ASP program was accepted. This decrease in antimicrobial duration was not associated with increased mortality. J. Perron, Lumed Inc., the company that commercializes APSS: Shareholder, Shareholder; V. Nault, Lumed Inc., the company that commercializes APSS: Shareholder, Shareholder; M. Beaudoin, Lumed Inc., the company that commercializes APSS: Shareholder, Shareholder; L. Valiquette, Lumed Inc., the company that commercializes APSS: Shareholder, Shareholder

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)0.005
metaresearch head score (Gemma)0.058
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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.201
GPT teacher head0.586
Teacher spread0.386 · 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
Published2017
Admission routes2
Has abstractyes

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