The Epidemiology of Influenza Virus Infection and Group A Streptococcal Pharyngitis in Children Between 2011 and 2018 in an Outpatient Pediatric Clinic

The Effects of a Dietary Supplement (PediaFlù) Plus Standard of Care in Children With Acute Tonsillopharyngitis/Rhinopharyngitis: Protocol for a Randomized Controlled Trial

BACKGROUND

A dietary supplement containing Pelargonium sidoides extract, propolis, zinc, and honey has been recently developed and proven to be an effective adjuvant in clinical practice for seasonal diseases and the treatment of respiratory tract disorders.

OBJECTIVE

This trial aims to verify the efficacy of the tested dietary supplement in a pediatric population with acute tonsillopharyngitis/rhinopharyngitis (ATR).

METHODS

The trial includes children aged between 3 and 10 years with ATR ≤48 h, a negative rapid test for beta-hemolytic streptococcus or culture identification of nasal and/or pharyngeal exudates, and SARS-CoV-2 infection. The dietary supplement tested is an oral solution already on the market based on Pelagon P-70 (equivalent to Pelargonium sidoides d.e. 133.3 mg/100 ml), propolis, zinc, and honey. The product is administered at 5 ml 3 times a day for 6 days for children younger than 6 years and 10 ml 3 times a day for 6 days for children older than 6 years. The study design is open label, randomized, and controlled, with the tested dietary supplement plus standard of care (SoC) versus SoC alone. Patients are enrolled from 3 sites in Romania. The change in Tonsillitis Severity Score and number of treatment failures (using ibuprofen or high-dose paracetamol as rescue medication) are the primary end points. Based on the Tonsillitis Severity Score and the 2-sample comparison of the means formula with a 5% significance level, 80% power, and a minimally clinically important difference of 2 (SD 3.85) points, 120 patients are required. To account for potential screening failures and dropouts, we need to screen a population of approximately 150 children.

RESULTS

Patient enrollment began on June 3, 2021 (first patient's first visit), and ended on August 12, 2021 (last patient's last visit). The data collection period was from June 3, 2021, to September 16, 2021. The study was funded in February 2023. Data analysis is currently ongoing (April 2024). We expect the results to be published in a peer-reviewed clinical journal in the third quarter of 2024 and presented at scientific meetings in the last quarter of 2024.

CONCLUSIONS

The data from this trial may help identify new adjuvant treatments for children with ATR when streptococcal infection is excluded by a negative rapid test, thereby avoiding unnecessary antibiotic administration.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04899401 https://clinicaltrials.gov/study/NCT04899401.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/53703.

Leveraging machine learning to distinguish between bacterial and viral induced pharyngitis using hematological markers: a retrospective cohort study

Accurate differentiation between bacterial and viral-induced pharyngitis is recognized as essential for personalized treatment and judicious antibiotic use. From a cohort of 693 patients with pharyngitis, data from 197 individuals clearly diagnosed with bacterial or viral infections were meticulously analyzed in this study. By integrating detailed hematological insights with several machine learning algorithms, including Random Forest, Neural Networks, Decision Trees, Support Vector Machine, Naive Bayes, and Lasso Regression, for potential biomarkers were identified, with an emphasis being placed on the diagnostic significance of the Monocyte-to-Lymphocyte Ratio. Distinct inflammatory signatures associated with bacterial infections were spotlighted in this study. An innovation introduced in this research was the adaptation of the high-accuracy Lasso Regression model for the TI-84 calculator, with an AUC (95% CI) of 0.94 (0.925-0.955) being achieved. Using this adaptation, pivotal laboratory parameters can be input on-the-spot and infection probabilities can be computed subsequently. This methodology embodies an improvement in diagnostics, facilitating more effective distinction between bacterial and viral infections while fostering judicious antibiotic use.