Assessment of antibiotic prescribing quality through repeated point prevalence surveys in a Malaysian teaching hospital
Bibliographic record
Abstract
Introduction: Optimising antibiotic prescribing in hospitals through antimicrobial stewardship (AMS) initiatives is essential in addressing the global threat of antimicrobial resistance. Methods: Point prevalence surveys were performed in April 2019 and November 2022 utilizing the Hospital National Antimicrobial Prescribing Survey (NAPS) tool. The study aimed to evaluate the prevalence of antibiotic use among inpatients and monitor antibiotic prescribing quality in 2022 compared to 2019 in a Malaysian teaching hospital as part of AMS core elements. Results: The prevalence of antibiotic use remained relatively stable between 2019 and 2022 (44.1% vs. 42.3%), with no significant change observed. Prescription patterns, including the type of antimicrobials, treatment modalities, and prescriptions per patient, showed insignificant differences between the two surveys. Antibiotics from the World Health Organisation (WHO) Access group constituted up to 47% of prescriptions in 2022, while usage of antibiotics from Watch group decreased from 57% to 53%, albeit insignificantly. Notably, there was a non- significant increase in appropriate prescribing for surgical prophylaxis in 2022 (40% vs 16.7%, p=0.078), alongside a less prevalent in prolonged surgical prophylaxis (28% in 2022 vs. 50% in 2019). Despite static prevalence and prescribing patterns, compliance with guidelines (p<0.006) and appropriate prescribing (p<0.002) showed significant improvement. The likelihood of compliance and appropriate prescribing was approximately 1.8-fold higher in 2022 compared to 2019. However, an increase in prescription of unnecessary broad-spectrum antibiotics was observed (23.1% vs 48%, p=0.002). Multiple logistic regression revealed that inappropriate prescribing significantly occurred when antibiotic indication was poorly documented (adjusted OR 3.67;95% CI 1.28–10.53; p=0.016). Conclusion : While prescribing patterns remained relatively unchanged, our findings highlight notable improvement in antibiotic prescribing quality. However, challenges persist, including the increased use of unnecessary broad-spectrum antibiotics. Continued efforts in AMS are imperative to address these issues and further enhance prescribing practices.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".