1353. Pediatric Urinary Tract Infections: A Choosing Wisely Initiative to Advance Antimicrobial Stewardship and Diagnostic Accuracy in the Emergency Department
Bibliographic record
Abstract
Abstract Background Urinary tract infection (UTI) is a common diagnosis in the pediatric emergency department (ED) that often results in empiric antibiotic treatment prior to culture results. A 2016 cohort study from our centre found that 47% of children diagnosed with a UTI and prescribed antibiotics had a negative urine culture. None of these patients were notified of the misdiagnosis or told to discontinue antibiotics. Figure 1: Choosing Wisely pediatric urinary tract infection diagnostic algorithm Figure 2: Patients included/excluded with exclusion criteria Methods Institutional approval was obtained for a quality improvement project in our quaternary pediatric ED. For uncomplicated pediatric UTIs, the aim was to reduce misdiagnosis by 50% and promote antimicrobial stewardship over a 24-month period. Using the Model for Improvement, two interventions were implemented using PDSA cycles: (1) a UTI diagnostic algorithm embedded in the electronic medical record, (2) a urine culture callback system. Outcome measures included the percentage of patients with UTI misdiagnosis (urine culture negative) and antibiotic-days saved. Process measures included adherence to the UTI algorithm and callback system as well as antibiotic duration standardization. As a balancing measure, patients developing positive urine cultures without UTI diagnosis were reviewed for potential harm. Figure 3: Run chart of urinary tract infection misdiagnosis rate Figure 4: Callback system - Percent patients contacted and antibiotics-days saved Results From June 2017-April 2020, 2,183 children (0.97% of all visits) were diagnosed with a UTI in the ED. 1,381 (63.3%) met inclusion criteria for analysis. Following UTI algorithm launch, median UTI misdiagnosis decreased by 20% (52.5% vs. 32.5%), median correct antibiotic duration increased by 30% (45.2% vs. 75.1%), and algorithm adherence was 78.9%. With implementation of the callback system, 1,678 antibiotic-days were saved as mean patients contacted to discontinue antibiotics increased from 0% to 76.8%. Of 106 patients with positive urine cultures with missed UTI diagnosis over a 29-month period, 8 patients returned to the ED within 72 hours and 2 patients required admission for intravenous antibiotics. Conclusion Implementation of a UTI diagnostic algorithm and urine culture callback system for uncomplicated pediatric UTIs reduced UTI misdiagnosis and promoted antimicrobial and resource stewardship in the ED. Future directions include improving UTI algorithm adherence through targeted clinician audit and feedback, plus sustainability planning. Disclosures Olivia Ostrow, MD, Choosing Wisely Canada (Advisor or Review Panel member)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".