Efficacy and safety of antimicrobial stewardship prospective audit and feedback in patients hospitalized with COVID-19: A protocol for a pragmatic clinical trial
Bibliographic record
Abstract
BACKGROUND: The use of broad-spectrum antibiotics is widespread in patients with COVID-19 despite a low prevalence of bacterial co-infection, raising concerns for the accelerated development of antimicrobial resistance. Antimicrobial stewardship (AMS) is vital but there are limited randomized clinical trial data supporting AMS interventions such as prospective audit and feedback (PAF). High quality data to demonstrate safety and efficacy of AMS PAF in hospitalized COVID-19 patients are needed. METHODS AND DESIGN: This is a prospective, multi-center, non-inferiority, pragmatic randomized clinical trial evaluating AMS PAF intervention plus standard of care (SOC) versus SOC alone. We include patients with microbiologically confirmed SARS-CoV-2 infection requiring hospital admission for severe COVID-19 pneumonia. Eligible ward beds and critical care unit beds will be randomized prior to study commencement at each participating site by computer-generated allocation sequence stratified by intensive care unit versus conventional ward in a 1:1 fashion. PAF intervention consists of real time review of antibacterial prescriptions and immediate written and verbal feedback to attending teams, performed by site-based AMS teams comprised of an AMS pharmacist and physician. The primary outcome is clinical status at post-admission day 15 measured using a 7-point ordinal scale. Patients will be followed for secondary outcomes out to 30 days. A total of 530 patients are needed to show a statistically significant non-inferiority, with 80% power and 2.5% one-sided alpha assuming standard deviation of 2 and the non-inferiority margin of 0.5. DISCUSSION: This study protocol presents a pragmatic clinical trial design with small unit cluster randomization for AMS intervention in hospitalized COVID-19 that will provide high-level evidence and may be adopted in other clinical situations. TRIAL REGISTRATION: This study is being performed at the University of Alberta and is registered at ClinicalTrials.gov (NCT04896866) on May 17, 2021.
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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 | 0.087 | 0.079 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".