Antimicrobial Stewardship Among Hospitalized Patients with Influenza Respiratory Tract Infections
Bibliographic record
Abstract
Overlap between Influenza and bacterial respiratory illnesses contributes to the inappropriate use of antibiotics. One major study from the United States suggests a significant number of patients are being treated with antibiotics inappropriately. This paper however, did not perform an intervention to evaluate whether an Antimicrobial Stewardship Program (ASP) is effective in decreasing inappropriate antibiotic use. Appropriate use of antibiotics in Influenza patients has not been formally assessed in Canadian healthcare systems. Given ASP’s have been shown to be effective in previous studies, an opportunity has arisen for implementation in the setting of antibiotics in patients with Influenza, which has not previously been studied We retrospectively identified all adults admitted to hospital who tested positive for Influenza from January 2016 to January 2017. We assessed the appropriateness of antibiotic use during the patient’s admission, evaluating whether antibiotics have been used according to standard of care for community acquired pneumonia. Antibiotic use and length of duration pre and post Stewardship implementation will be analyzed. After data has been collected, the results of this retrospective cohort study will inform the implementation of an ASP Eighty-one patients recorded positive Influenza tests. Twenty-six were collected from ICU patients and were excluded. Mean time to diagnosis from swab collection and final diagnosis was 2.8 days. Of the 55 non-ICU patients, 13 (24%) patients were continued on antibiotics after the diagnosis of Influenza was confirmed, with an average of 4.7 days of antibiotic use. It was deemed that 9 of these patients were continued appropriately on antibiotics with 4 patients having CXR infiltrate, 4 patients immunocompromised and 1 blood culture positive with strep pneumonia. Four (8%) patients were treated inappropriately with antibiotics for >24 hours after positive Influenza test, with a mean duration of 2.5 days after positive result There is an opportunity for improvements in the appropriate use of antibiotics. Implementation of an ASP whereby positive Influenza results are delivered directly to the Stewardship team, could be an effective strategy to improve judicious antibiotic therapy 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".