The COVID-19 pandemic has had significant impacts on the child and adolescent population,
with long-term consequences for physical health, socio-psychological well-being, and
cognitive development, which require further investigation. We herein describe a study
design protocol for recognizing neuropsychiatric complications associated with pediatric
COVID-19, and for developing effective prevention and treatment strategies grounded
on the evidence-based findings.The study includes two cohorts, each with 163 participants,
aged from 7 to 18 years old, and matched by gender. One cohort consisted of individuals
with a history of COVID-19, while the other group presents those without such a history.
We undertake comprehensive assessments, including neuropsychiatric evaluations, blood
tests, and validated questionnaires completed by parents/guardians and by the children
themselves. The data analysis is based on machine learning techniques to develop predictive
models for COVID-19-associated neuropsychiatric complications in children and adolescents.The
first model is focused on a binary classification to distinguish participants with
and without a history of COVID-19. The second model clusters significant indicators
of clinical dynamics during the follow-up observation period, including the persistence
of COVID-19 related somatic and neuropsychiatric symptoms over time. The third model
manages the predictors of discrete trajectories in the dynamics of post-COVID-19 states,
tailored for personalized prediction modeling of affective, behavioral, cognitive,
disturbances (academic/school performance), and somatic symptoms of the long COVID.The
current protocol outlines a comprehensive study design aiming to bring a better understanding
of COVID-19-associated neuropsychiatric complications in a population of children
and adolescents, and to create a mobile phone-based applications for the diagnosis
and treatment of affective, cognitive, and behavioral conditions. The study will inform
about the improved management of preventive and personalized care strategies for pediatric
COVID-19 patients. Study results support the development of engaging and age-appropriate
mobile technologies addressing the needs of this vulnerable population group.
