Half of Prescribed Antibiotics Are Not Needed: A Pharmacist-Led Antimicrobial Stewardship Intervention and Clinical Outcomes in a Referral Hospital in Ethiopia
Bibliographic record
Abstract
Intense antibiotic consumption in Low- and Middle-Income Countries (LMICs) is fueled by critical gaps in diagnostics and entrenched syndromic management of infectious syndromes. Few data inform the achievability and impact of antimicrobial stewardship interventions, particularly in Sub-Saharan Africa. Our goal was to demonstrate the feasibility of a pharmacist-led laboratory-supported intervention at Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia, and report on antimicrobial use and clinical outcomes associated with the intervention. This was a single-center quasi-experimental study conducted in two phases: (i) an intervention phase (November 2017 to August 2018), during which we implemented weekly audit and immediate feedback on antibiotic prescriptions of patients admitted in 2 pediatric and 2 adult medicine wards, and (ii) a post-intervention phase (September 2018 to January 2019) during which we audited prescriptions but provided no feedback to the treating teams. The intervention was conducted by an AMS team consisting of 4 clinical pharmacists and one ID specialist. Our primary outcome was antimicrobial utilization (days of therapy (DOT) per 1000 patient-days and duration of antibiotic treatment courses); secondary outcomes were length of hospital stay (LOS) and in-hospital all-cause mortality. A multivariable logistic regression model was used to explore factors associated with all-cause in-hospital mortality. We collected data on 1,109 individual patients (707 during intervention, 402 post-intervention). Ceftriaxone, vancomycin, cefepime, and meropenem were the most commonly prescribed antibiotics; 96% of the AMS team's recommendations were accepted. We recommended to discontinue antibiotics in 54% of cases. Once the intervention ceased, total antimicrobial use increased by 51.6% and mean duration of treatment by 4.1 days/patient. Mean LOS and crude mortality increased significantly post-intervention (LOS: 19.8 vs 24.1 days; mortality 6.9% vs 14.7%). These differences remained significant after adjusting for potential confounders. A pharmacist-led AMS intervention focused on duration of antibiotic treatment was feasible with good acceptability in our setting. Cessation of audit-feedback activities was associated with immediate and sustained increase in antibiotic consumption, reflecting a rapid return to baseline (pre-intervention) prescribing practices, and worse clinical outcomes. Audit-feedback activities can effectively reduce antimicrobial consumption and result in better outcomes, but require organizational leadership’s commitment for sustainable benefits.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".