Machine Learning for Clinical Decision Support of Acute Streptococcal Pharyngitis: A Pilot Study.

BACKGROUND: Group A Streptococcus (GAS) is the predominant bacterial pathogen of pharyngitis in children. However, distinguishing GAS from viral pharyngitis is sometimes difficult. Unnecessary antibiotic use contributes to unwanted side effects, such as allergic reactions and diarrhea. It also may increase antibiotic resistance. OBJECTIVES: To evaluate the effect of a machine learning algorithm on the clinical evaluation of bacterial pharyngitis in children. METHODS: We assessed 54 children aged 2-17 years who presented to a primary healthcare clinic with a sore throat and fever over 38°C from 1 November 2021 to 30 April 2022. All children were tested with a streptococcal rapid antigen detection test (RADT). If negative, a throat culture was performed. Children with a positive RADT or throat culture were considered GAS-positive and treated antibiotically for 10 days, as per guidelines. Children with negative RADT tests throat cultures were considered positive for viral pharyngitis. The children were allocated into two groups: Group A streptococcal pharyngitis (GAS-P) (n=36) and viral pharyngitis (n=18). All patients underwent a McIsaac score evaluation. A linear support vector machine algorithm was used for classification. RESULTS: The machine learning algorithm resulted in a positive predictive value of 80.6 % (27 of 36) for GAS-P infection. The false discovery rates for GAS-P infection were 19.4 % (7 of 36). CONCLUSIONS: Applying the machine-learning strategy resulted in a high positive predictive value for the detection of streptococcal pharyngitis and can contribute as a medical decision aid in the diagnosis and treatment of GAS-P.

Pathogens Causing Pediatric Community Acquired Urinary Tract Infections and Their Increasing Antimicrobial Resistance: A Nationwide Study.

Urinary tract infections (UTIs) in childhood are common and are associated with considerable acute morbidity and long-term complications. The need for updated data to optimize empiric antibiotic therapy is crucial. We aimed to investigate the pathogens causing pediatric community acquired UTIs, their correlation with demographic characteristics, and trends in their antimicrobial resistance. This nationwide cross-sectional study included all 53,203 children (<18 years) diagnosed with UTI in community outpatient clinics in the following selected years: 2007, 2011, 2015, 2019 and 2021. Escherichia coli (E. coli) (82.1%) was the most common uropathogen, followed by Enterobacter, Klebsiella, Proteus, Pseudomonas, and Enterococcus species. The bacterial distribution displayed statistically significant (p < 0.0001) gender- and sector-specific patterns with a higher relative prevalence of non-E. coli UTI in Jewish and males. The rate of extended-spectrum beta-lactamase-positive E. coli increased substantially and significantly (p < 0.001) from only 6.1% in 2007 to 25.4% in 2021. Most non-E. coli uropathogens exhibited resistance to commonly used empiric antibiotics for UTIs in children. These findings are significant in guiding optimal empiric antibiotic treatment for pediatric community acquired UTIs. The resistance of uropathogens to antimicrobials is region- and time-dependent. Therefore, the periodic and local assessment of antibiotic resistance trends is essential to update guidelines and provide the most appropriate antibacterial therapy for children with UTIs.