Exploring clinical characteristics of COVID-19 in children and adolescents using a machine-learning approach

IntroductionThe epidemiology and clinical characteristics of COVID-19 evolved due to new SARS-CoV-2 variants of concern (VOCs). The Omicron VOCs higher transmissibility increased pediatric COVID-19 cases and hospital admissions. Most research during the Omicron period has focused on hospitalized cases, leaving a gap in understanding the diseases evolution in community settings. This study targets children with mild to moderate COVID-19 during pre-Omicron and Omicron periods. It aims to identify patterns in COVID-19 morbidity by clustering individuals based on symptom similarities and duration of symptoms and develop a machine-learning tool to classify new cases into risk groups. MethodsWe propose a data-driven approach to explore changes in COVID-19 characteristics analyzing data collected within a pediatric cohort at the University Hospital of Padua. First, we apply an unsupervised machine-learning algorithm to cluster individuals into different groups. Second, we classify new patient risk groups using a Random-Forest classifier model based on sociodemographic information, pre-existing medical conditions, vaccination status, and the VOC as predictive variables. Third, we explore the key features influencing the classification. ResultsThe unsupervised clustering identified three severity risk profile groups. The classification model effectively distinguished these groups, with age, gender, COVID-19 vaccination, VOC, and presence of comorbidities as top predictive features. A high number and longer duration of symptoms were associated with younger age groups, males, unvaccinated individuals, Omicron infections, and those with comorbidities. These results are consistent with evidence of severe COVID-19 in infants, older children with comorbidities, and unvaccinated children. ConclusionOur classification model has the potential to provide clinicians with insights into the childrens risk profile of COVID-19 using readily available data. This approach can support public health efforts by clarifying disease burden and improving patient care strategies. Furthermore, it underscores the importance of integrating risk classification models to monitor and manage infectious diseases.