210Audit of Prescribing Patterns for Restricted Antibiotics in an Inpatient Setting (APPRAIS): Appraisal Reveals Hospital Overuse and Underdosing of Carbapenems
Bibliographic record
Abstract
Background. This project evaluated the guideline concordance of inpatient use of restricted antimicrobials (daptomycin, imipenem, meropenem, ertapenem, linezolid and tigecycline) at the University of Alberta Hospital (UAH), examined the usability of the current formulary guidelines, and identified areas where action could be taken using stewardship to promote patient safety and appropriate antimicrobial use. Methods. All inpatients at the UAH started on a restricted antibiotic from January 9 - February 6, 2014 were prospectively enrolled and followed. Exclusion criteria included patients under the age of 18, and any patient treated with restricted antimicrobials as outpatients in the emergency department or medical outpatient unit. Patient charts and electronic health records were reviewed to collect relevant patient, microbiological and clinical data. Indication for use and guideline concordance was determined by the researcher, Drug Stewardship Pharmacist and Physician. Data was de-identified, entered into a secure database, and analyzed using Excel pivot tables and descriptive statistics. Results. Eighty-five patients were prescribed a total of 92 restricted antibiotics during the study period. The attending service ordered 77% of the restricted antibiotics; only 23% (n = 21/92) of orders came from the Transplant Infectious Disease or Infectious Disease service. Orders were classified as "use outside of guidelines" 28% of the time (n = 26/92). Twenty of these "use outside of guidelines" orders had appropriate non-restricted formulary options, which would have been considered equally safe and effective in the clinical situation. Fourteen percent (n = 13/92) of the restricted antibiotics were incorrectly dosed by the attending services and 9 of the 13 incorrect orders (69%) under-dosed the patient. Conclusion. This study demonstrated carbapenem overuse, a concern in the current era of evolving antimicrobial resistance, and a surprisingly high dosing error rate, which is a safety concern. An educational intervention has already been implemented to address the identified dosing issue. Going forward we hope to gain institutional support for an active, ongoing stewardship program, which is a current need in this facility. 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".