Background: The prevalence of pediatric urinary tract infections (UTIs) caused by e xtended-spectrum β-lactamases (ESBL)-producing bacteria is increasing worldwide and is difficult to predict. As these infections require special antibiotic treatment, which is often not started empirically, they are associated with higher rates of intensive care unit admission, morbidity and prolonged hospitalization. We aimed to develop machine learning–based tools to aid pediatricians in predicting ESBL-positive UTIs and initiate appropriate empiric antibiotics. Methods: The electronic medical records of a large Health Maintenance Organization were searched for all children one month to 18 years of age with confirmed UTIs during January 1, 2010, to August 31, 2020. Data on demographics, clinical and laboratory information were retrieved, and following univariate analysis, machine learning–based tools were used to develop models to predict a UTI caused by an ESBL-producing bacterium. Results: A total of 35,830 pediatric UTI events comprised the study group. Age, sex, socioeconomic status, site of infection (community or hospital), prior antibiotic use, previous ESBL-positive UTI and the specific uropathogen were significantly associated with the rates of ESBL-positive infection. Using patients’ data available on presentation, the 5 models developed had a very high negative predictive value of ~0.98, indicating strong rule-out performance for ESBL-positive UTIs. Conclusions: Our study indicates that machine learning models based on data available at UTI presentation may support clinicians in estimating the likelihood of ESBL-producing bacteria UTIs. Prospective studies are required to improve the models’ performance and determine their actual impact on clinical outcomes.
Hajaj et al. (Thu,) studied this question.