An Antimicrobial Prescription Optimization System Improves the Adequacy of Antibacterial Dosage in Obese Inpatients
Bibliographic record
Abstract
Background. Obesity affects the pharmacokinetics of antibacterials (Abx). As a consequence, Abx are frequently underdosed in obese patients which may increase the risk of unfavorable outcomes. Antimicrobial stewardship programs can improve the use of Abx in obese inpatients. The aim of this study was to analyze the impact of an antimicrobial stewardship intervention on Abx optimal dosing in obese inpatients. Mean Percentage of Days Of Inappropriate Treatment (Dose and Frequency) According to BMI and Abx Classes, Before and After The Implementation of APSS *Significant difference in mean percentage (χ2; p < 0.05). †Including β-lactamase inhibitors. Methods. This study included all hospitalized adults receiving selected Abx in a 677-bed hospital in Quebec, Canada between August 2008 and August 2013. Data were retrospectively collected from Antimicrobial Prescription Surveillance System (APSS), a computerized decision support system. We evaluated the number of inappropriate days of Abx treatment per 1000 hospitalized-patients days and the proportion of inappropriate days of Abx treatment per total days of Abx treatment. Pre-intervention rates (2008–2010) were compared to post-intervention rates (2010–2013) in non-obese (BMI < 30), obese (30 ≤ BMI < 39.9), and morbidly obese (MO) (BMI ≥ 40) patients. Results. A total of 40 605 hospitalizations with Abx were included in the study: 78% (31 of 614) concerned non-obese patients, 16% (6481) obese patients, and 6% (2510) MO patients. Conclusion. Regardless of the weight of patients, APSS had a positive impact on dosing optimization on several classes of Abx. In MO patients, the effect was the most important on penicillins with or without β-lactamase inhibitor and fluoroquinolones. Improving Abx prescriptions in MO patients is important since suboptimal dosing could be associated with unfavorable outcomes. Disclosures. V. Nault, Lumed inc: Shareholder, Salary; L. Valiquette, Lumed Inc.: Shareholder, Licensing agreement or royalty
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".