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Post-COVID Neuropsychiatric Complications in Children and Adolescents: a Study Design for Early Diagnosis and
Dania Gadelshina1, Timur Syunyakov, Arseny J Gayduk
1Samara State Medical University, Samara, Russia.
Insights
This study investigates COVID-19's impact on children's neuropsychiatric health, developing predictive models for complications and long COVID symptoms. Findings aim to improve prevention and personalized care strategies for pediatric patients.
Area of Science:
- Pediatric neuropsychiatry
- Infectious disease epidemiology
- Machine learning in healthcare
Background:
- The COVID-19 pandemic poses long-term risks to children's physical health, socio-psychological well-being, and cognitive development.
- Further investigation is needed to understand and address these neuropsychiatric complications.
- This study protocol aims to identify and manage these issues in pediatric populations.
Purpose of the Study:
- To design a study protocol for recognizing neuropsychiatric complications in children and adolescents post-COVID-19.
- To develop evidence-based prevention and treatment strategies.
- To create mobile applications for diagnosing and treating affective, cognitive, and behavioral conditions.
Main Methods:
- Two cohorts of 163 participants (ages 7-18) each: one with a history of COVID-19, one without.
- Comprehensive assessments including neuropsychiatric evaluations, blood tests, and validated questionnaires.
- Machine learning techniques for predictive modeling of COVID-19-associated neuropsychiatric complications.
Main Results:
- Development of a binary classification model to distinguish between participants with and without COVID-19 history.
- Clustering of significant indicators for the persistence of somatic and neuropsychiatric symptoms.
- Predictive modeling for personalized trajectories of affective, behavioral, cognitive, and somatic symptoms in long COVID.
Conclusions:
- The study protocol enhances understanding of COVID-19 neuropsychiatric effects in children and adolescents.
- Aims to develop mobile applications for diagnosing and treating affective, cognitive, and behavioral conditions.
- Informs improved preventive and personalized care strategies for pediatric COVID-19 patients.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
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