918. Influence of a hospital-based antimicrobial stewardship program on clinical and economic outcomes. The experience from a referral center in Mexico.
Bibliographic record
Abstract
Abstract Background Antibiotic stewardship programs (ASP) are relatively new in Mexico. It was until May 2018 that it was established as a public health policy in the country. Few hospitals have acknowledged the mandate and started an ASP despite the efforts. Besides, little has been done to evaluate the efficacy of the program in terms of antibiotic expenditure and the incidence of associated infections. Therefore, the main objective was to evaluate antibiotic expenditure before and after the ASP in a third-level hospital in Mexico. As a secondary analysis, the relationship with Clostridioides difficile infections was explored. Methods This is a retrospective and descriptive study from January 2017 to February 2020 (pre-COVID-19 pandemic). First, the mean antibiotic expenditure (adjusting for DDD/patient days-costs in USD) was evaluated before and after (June 2018 as the first month of implementation) the ASP. Statistical difference of the means was evaluated. It was also performed an exploratory analysis between the prescription of Clindamycin and Levofloxacin with the number of cases and deaths related to C. difficile infection. Results The average antibiotic expenditure before ASP was US$13,468 ± 3,267, and US$8,193 ± 2,574 (p< 0.001) after. Graph 1 presents the changes in trend. Specifically, caspofungin, ciprofloxacin, clindamycin, fluconazole, levofloxacin, linezolid, piperacillin/tazobactam, and tigecycline showed statistically significant reduction after June 2018. Graph 2 displays the descriptive relationship between the prescription of levofloxacin and clindamycin, the cases of C. difficile infection, and the elimination of all associated mortality. Graph 1. Antibiotic expenditure. Before and after AMS Graph 2. Relationship between monthly consumption of clindamycin and levofloxacin and the number of cases/deaths secondary to Clostridioides difficile infections, before and after ASP. Conclusion The ASP has significantly reduced the antibiotic expenditure in the hospital. Besides reducing the prescription of associated antibiotics, a possible reduction in C. difficile infections and associated deaths were observed. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".