Machine Learning for Clinical Decision Support of Acute Streptococcal Pharyngitis: A Pilot Study
Oshrit Hoffer1, Moriya Cohen2, Maya Gerstein3
1Department of Electrical Engineering, Afeka Tel Aviv, Academic College of Engineering, Tel Aviv.
Insights
A machine learning algorithm showed an 80.6% positive predictive value for detecting Group A Streptococcus (GAS) pharyngitis in children. This AI tool can aid clinicians in diagnosing and treating GAS infections, reducing unnecessary antibiotic use.
Area of Science:
- Pediatric Infectious Diseases
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Group A Streptococcus (GAS) is a common cause of bacterial pharyngitis in children.
- Differentiating GAS from viral pharyngitis is challenging, leading to potential overuse of antibiotics.
- Unnecessary antibiotic use can result in adverse effects and increased antibiotic resistance.
Purpose of the Study:
- To evaluate the effectiveness of a machine learning algorithm in diagnosing bacterial pharyngitis in pediatric patients.
- To assess the algorithm's impact on clinical decision-making for GAS infections.
Main Methods:
- A cohort of 54 children (aged 2-17) with sore throat and fever were evaluated.
- Standard diagnostic tests including RADT and throat culture were performed.
- A linear support vector machine algorithm was applied for classification of GAS-P and viral pharyngitis.
Main Results:
- The machine learning algorithm achieved a positive predictive value of 80.6% for identifying GAS-P infections.
- The false discovery rate for GAS-P infection was 19.4%.
Conclusions:
- Machine learning demonstrates high predictive value in detecting streptococcal pharyngitis.
- This AI strategy can serve as a valuable decision support tool for clinicians managing GAS-P in children.
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.
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