Impact of Diagnostic Stewardship on Urine Culture Ordering in Saudi Arabia: Prospective Pre- and Postintervention Study
Bibliographic record
Abstract
Background: Inappropriate testing of urine cultures can lead to overuse of antibiotics, antimicrobial resistance, Clostridioides difficile infections, and increased cost. In Saudi Arabia, antimicrobial stewardship programs have improved antibiotic use but lack focus on asymptomatic bacteriuria. Targeted interventions are needed to address this gap. Objective: We assessed the implementation of a clinical decision support (CDS) tool in diagnostic stewardship, focusing on the appropriateness of urine culture orders and antibiotic use. Methods: We examined differences in urine culture testing and antibiotic use before and after implementation of a CDS tool in a 400-bed hospital in Riyadh, Saudi Arabia, from August 2021 to July 2022. We included adult patients with urine culture orders. Our outcomes were the percentage of urine cultures ordered that were inappropriate and antibiotic use after the implementation of the CDS intervention. We used a multivariable logistic regression model to determine factors associated with inappropriate urine culture testing and antibiotic use. Results: The percentage of inappropriate urine culture orders were significantly lower in the postintervention period compared to the preintervention period (821/2254, 36.4% vs 754/1814, 41.6%; P=.001). The CDS intervention was associated with 16.7% lower odds of inappropriate urine culture ordering (adjusted odds ratio [aOR] 0.83, 95% CI 0.73-0.95; P=.008). Unnecessary antibiotics were significantly lower in the postintervention period (310/2254, 72.9% vs 288/1814, 85.7%; P<.001). The CDS intervention was associated with a 52% reduction in unnecessary antibiotic use (aOR 0.487, 95% CL 0.332-0.713; P<.001). Conclusions: A CDS initiative can reduce unnecessary urine culture testing and antibiotic overuse.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".