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Machine Learning Tools for Predicting Pediatric Urinary Tract Infections Caused by ESBL-producing Bacteria
Chen Hajaj1,2, Shani Alkoby1,2, Shai Ashkenazi3,4
1From the Department of Industrial Engineering & Management, Ariel University, Ariel, Israel.
Insights
Machine learning models can predict pediatric urinary tract infections (UTIs) caused by extended-spectrum β-lactamases (ESBL)-producing bacteria. These tools help clinicians identify high-risk cases for appropriate antibiotic selection.
Area of Science:
- Pediatric infectious diseases
- Medical informatics
- Machine learning in healthcare
Background:
- Increasing global prevalence of pediatric urinary tract infections (UTIs) caused by extended-spectrum β-lactamases (ESBL)-producing bacteria.
- ESBL-UTIs necessitate specialized antibiotic treatment, often leading to delayed empirical therapy, increased ICU admissions, morbidity, and prolonged hospital stays.
- Predicting ESBL-UTIs is challenging but crucial for timely and appropriate patient management.
Purpose of the Study:
- To develop and validate machine learning (ML) models for predicting pediatric UTIs caused by ESBL-producing bacteria.
- To assist pediatricians in identifying children at higher risk for ESBL-positive UTIs.
- To enable earlier initiation of appropriate empiric antibiotic therapy for ESBL-UTIs.
Main Methods:
- Retrospective analysis of electronic medical records for pediatric patients (1 month to 18 years) with confirmed UTIs from January 2010 to August 2020.
- Data extraction included demographics, clinical information, and laboratory results.
- Development of five ML models using available patient data at UTI presentation for predicting ESBL-positive bacterial infections.
Main Results:
- The study analyzed 35,830 pediatric UTI events.
- Factors significantly associated with ESBL-positive UTIs included age, sex, socioeconomic status, infection site, prior antibiotic use, previous ESBL-UTI history, and specific uropathogen.
- The developed ML models demonstrated a high negative predictive value (~0.98), indicating strong performance in ruling out ESBL-positive UTIs.
Conclusions:
- Machine learning models utilizing data available at UTI presentation can aid clinicians in assessing the probability of ESBL-producing bacterial UTIs in children.
- These models show promise in supporting clinical decision-making for empiric antibiotic selection.
- Further prospective studies are needed to refine model performance and evaluate their impact on clinical outcomes.
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.
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