Antibiotic Review Kit for Hospitals (ARK-Hospital): a stepped wedge cluster randomised controlled trial
Bibliographic record
Abstract
Abstract Background Strategies to reduce antibiotic overuse in hospitals depend on clinicians taking decisions to stop unnecessary antibiotics. There is a lack of evidence on how support clinicians do this effectively. We evaluated a multifaceted behaviour change intervention (ARK) which aims to reduce antibiotic consumption in hospitals by increasing decisions to stop antibiotics at clinical review. Methods We performed a stepped-wedge, hospital-level, cluster-randomised controlled trial using computer-generated sequence randomisation of 39 acute hospitals to 7 calendar-time blocks (12/February/2018–01/July/2019). Co-primary outcomes were monthly antibiotic defined-daily-doses (DDD) per acute/medical admission (organisation-level, superiority) and all-cause 30-day mortality (patient-level, non-inferiority, margin 5%). Clusters were eligible if they admitted non-elective medical patients, could identify an intervention “champion” and provide pre-intervention data from February/2016. Sites were followed up for a minimum of 14 months. Intervention effects were assessed using interrupted time series analyses in each cluster. Overall effects were derived through random-effects meta-analysis, using meta-regression to assess heterogeneity in effects across prespecified factors. Trial registration was ISRCTN12674243 . Findings Adjusted estimates showed a year-on-year reduction in antibiotic consumption (−4.8%, 95%CI: -9.1%,-0.2%, p=0.042) following the ARK intervention. Among 7,160,421 acute/medical admissions, we observed a -2.7% (95%CI: -5.7%,+0.3%, p=0.079) immediate and +3.0% (95%CI: - 0.1%,+6.2%, p=0.060) sustained change in adjusted 30-day mortality. This mortality trend was not related to the magnitude of antibiotic reduction achieved (Spearman’s ρ=0.011, p=0.949). Whilst 90-day mortality odds appeared to increase over time (+3.9%, 95%CI:+0.5%,+7.4%, p=0.023), this was not observed among admissions before COVID-19 onset (+3.2%, 95%CI:-1.5%,+8.2%, p=0.182). Length of hospital stay was unaffected. Interpretation The weak, inconsistent effects of the intervention on mortality are likely to be explained by the COVID-19 pandemic onset during the post-implementation phase. We conclude that the ARK-intervention resulted in sustained, safe reductions in hospital antibiotic use. Funding NIHR Programme Grants for Applied Research, RP-PG-0514-20015. Research in context Evidence before this study Acutely ill patients often need to receive antibiotics before full diagnostic information is available. Consequently, reducing overuse of antibiotics in hospitals requires clinicians to review and where appropriate, stop unnecessary antibiotic prescriptions. Evidence-based tools to support clinicians stop unnecessary antibiotics do not exist. We searched PubMed, with no language or date restrictions, on 31/January/2022 for clinical studies focused on improving antibiotic use for hospitalised adults using the terms “anti-bacterial agents therapeutic use” AND “antibiotic stewardship”. Among the 427 studies found, the great majority were uncontrolled evaluations of different approaches to education, decision support and feedback. These included one before-after study, which found no impact of unsupported clinician-led prescription review. Three small, hospital-level cluster-randomised trials were identified. One evaluated different approaches to feedback, one compared different hospital specialties and one found intense feedback to be effective. All were small and none considered clinical outcomes or sustainability. There is a need for research to deliver proven interventions ready for implementation into practice. Added value of this study We evaluated a multifaceted “Antibiotic Review Kit” (ARK) intervention to support prescribers to appropriately stop antibiotics at clinical review. ARK comprises a prescription decision-aid supported by a brief online training tool, guidance on implementation (including regular data collection and feedback) and a patient information leaflet. We found that the intervention was associated with a sustained reduction in hospital-level antibiotic use overall and of oral and narrow-spectrum antibiotics specifically. Weak trends were observed for 30-day mortality in opposite directions for immediate and sustained impact. Although there was a sustained increase in 90-day mortality after the intervention, this was only seen when analyses included patients admitted after the start of the COVID-19 pandemic. Taken together we conclude that these mortality effects are unrelated to the intervention. Implications of all available evidence The ARK intervention is safe and effective in reducing antibiotic use among adult medical hospital admissions. The tools used are now freely available for adoption into practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".