Sustained impact of a computer-assisted antimicrobial stewardship intervention on antimicrobial use and length of stay
Bibliographic record
Abstract
Objectives: : Prospective audit and feedback interventions are the core components of an antimicrobial stewardship programme. Herein, we describe the sustained impact of an antimicrobial stewardship programme, based on a novel clinical decision-support system (Antimicrobial Prescription Surveillance System; APSS), on antimicrobial use and costs, hospital length of stay (LOS) in days and the proportion of inappropriate antimicrobial prescriptions. Methods: A quasi-experimental, retrospective study was conducted using interrupted time series between 2008 and 2013. Data on all hospitalized adults receiving antimicrobials were extracted from the data warehouse of a 677 bed academic centre. The intervention started in August 2010. Prospective audit and feedback interventions, led by a pharmacist, were triggered by APSS based on deviations from published and local guidelines. Changes in outcomes before and after the intervention were compared using segmented regression analysis. Results: APSS reviewed 40 605 hospitalizations for 35 778 patients who received antimicrobials. The intervention was associated with a decrease in the average LOS (level change -0.92, P < 0.01; trend -0.08, P < 0.01; intercept 11.4 days), antimicrobial consumption in DDDs/1000 inpatient days (level change -32.4, P < 0.01; trend -1.12, P < 0.02; intercept 243 DDDs per 1000 days of hospitalization), antimicrobial spending in Canadian dollars (level change -19 649, P = 0.01; trend -1881, P < 0.01; intercept $74 683) and proportion of non-concordance with local guidelines for prescribing antimicrobials (level change -2.3, P = 0.04; intercept 41%). Conclusions: The implementation of the APSS-initiated strategy was associated with a positive impact on antimicrobial use and spending, LOS and inappropriate prescriptions. The high rate of accepted interventions may have contributed to these results.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".