Document Type
Article
Publication Date
2024
Publication Title
Journal of Urgent Care Medicine
Volume
19
Issue
2
Pages
35-43
Abstract
Background: Urinary tract infections (UTIs) are a commonly encountered diagnosis at pediatric urgent care (UC) centers. The urinalysis (UA) is usually the initial study in UC settings used to guide decisions regarding initiating empiric antibiotics and/or pursuing urine culture. However, studies in pediatric UC settings examining the ideal threshold for a positive result are lacking.
Methods: UA result data were extracted from the records of 6,327 pediatric patients, which were collected as part of a previous QI project. Logistic regression was used to determine the predictors of positive urine cultures. Decision trees for a positive UA result for both clean catch and catheterized specimens were created, and test performance and characteristics were assessed.
Results: The presence of a positive nitrite result was found to be a strong predictor for a positive urine culture. For nitrite negative in specimens obtained by catheterization, the presence of leukocyte esterase (LE) and ≥ to 5 white blood cells per high powered field (WBC/HPF) had the greatest accuracy. For clean catch specimens, the presence of at least moderate LE was the best predictor.
Conclusion: Using a machine learning approach, criteria for a positive urinalysis were developed for the pediatric UC setting.
Rights
© Copyright 2024 by Braveheart Group, LLC.
No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without written permission from the Publisher. For information on reprints or commercial licensing of content, please contact the Publisher.
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Original Publication Citation
Flicker, K., Parrott, J., Speerhas, T., Vazifedan, T., Guins, T., Bobrowitz, J., McEvoy, A., Eves, J., Conrad, D., & Klick, B. (2024). Development of a positive urinalysis criteria using a machine learning approach. Journal of Urgent Care Medicine, 19(2), 35-43. https://www.jucm.com/development-of-a-positive-urinalysis-criteria-using-a-machine-learning-approach/
Repository Citation
Flicker, Kari; Parrott, Jessica; Speerhas, Tammy; Vazifedan, Turaj; Guins, Theresa; Bobrowtiz, Jeffrey; McEvoy, Anne; Eves, Jade; Conrad, Debra; and Klick, Benjamin, "Development of a Positive Urinalysis Criteria Using a Machine Learning Approach" (2024). Ellmer School of Nursing Faculty Publications. 87.
https://digitalcommons.odu.edu/nursing_fac_pubs/87
Included in
Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Artificial Intelligence and Robotics Commons, Pediatric Nursing Commons, Pediatrics Commons
