Antibiotic Prescription Practice for Pediatric Urinary Tract Infection in a Tertiary Center
Bibliographic record
Abstract
Abstract Background Urinary tract infection (UTI) is a leading cause for acute care visits in pediatrics. A suspected UTI diagnosis is made based on typical clinical presentation and pyuria and confirmed by significant growth in an appropriate urine sample. Prescribing antibiotics for suspected UTI is a common practice, and may lead to unnecessary antibiotic exposure. We aimed to review the practice of UTI diagnosis and management in the Emergency Department (ED) to identify targets to improve antimicrobial prescribing practices. Methods Children (< 18 years) who were discharged from the ED at the Hospital for Sick Children with a diagnosis of UTI between October to December 2016 were included. Patients were excluded if they were (1) under 12 weeks of age, (2) had underlying genitourinary abnormalities, (3) were admitted or transferred to another center, (4) were on antibiotics on presentation, (5) had urine testing done in another laboratory, or (6) were given conditional prescription. Demographic, clinical history, laboratory findings, and urine culture results were collected from patient charts. The sensitivity and specificity of nitrite and leukocyte esterase (LE) for UTI diagnosis were calculated. Logistic regression was used to examine the relationship between urinalysis characteristics and confirmed UTI. Results A total of 186 children with a median age of 4.2 (IQR 1.2, 7.3) were included; 82.3% were female. Almost all children were discharged home on antibiotics (n = 183, 98%) for a median duration of 7 days (IQR 7, 10). A total of 87 patients (46.8%) received antibiotics despite negative urine cultures and none of these patients received notification to stop. This led to 652 unnecessary antibiotic days. The presence of nitrites was the strongest predictor of UTI (OR 13.3, P < 0.001) and was highly specific. An LE result of 2+ (OR 2.4, P = 0.04) or 3+ (OR 2.23, P = 0.016) was also predictive of UTI. Conclusion Current practice in managing suspected pediatric UTIs in our ED resulted in significant and unnecessary antibiotic exposure. We identified targets to reduce unnecessary antibiotic exposure including improving the diagnostic accuracy of UTIs, a process to discontinue antibiotics for negative cultures and standardizing antimicrobial duration. Disclosures All authors: No reported disclosures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".