Israel Medical Association Journal

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.

Background: On 18 March 2020, the Israeli Health Ministry issued lockdown orders to mitigate the spread of coronavirus disease 2019 (COVID-19).

Objectives: To assess the association of lockdown orders on telemedicine practice and the effect of social distancing on infectious diseases in a primary care community pediatric clinic as well as the rate of referrals to emergency departments (ED) and trends of hospitalization.

Methods: Investigators performed a retrospective secondary data analysis that screened for visits in a large pediatric center from 1 January to 31 May 2020. Total visits were compared from January to December 2020 during the same period in 2019. Visits were coded during the first lockdown as being via telemedicine or in-person, and whether they resulted in ED referral or hospitalization. Month-to-month comparisons were performed as well as percent change from the previous year.

Results: There was a sharp decline of in-person visits (24%) and an increase in telemedicine consultations (76%) during the first lockdown (p < 0.001). When the lockdown restrictions were eased, there was a rebound of 50% in-person visits (p < 0.05). There was a profound decrease of visits for common infectious diseases during the lockdown period. Substantial decreases were noted for overall visits, ED referrals, and hospitalizations in 2020 compared to 2019.

Conclusions: COVID-19 had a major impact on primary care clinics, resulting in fewer patient-doctor encounters, fewer overall visits, fewer ED referrals, and fewer hospitalizations