Development and validation of a nomogram prediction model for ADHD in children based on individual, family, and social factors

Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in childhood, with the symptoms lasting through adolescence and into adulthood (Mechler et al., 2022). Individuals with ADHD show inappropriate levels of overactivity, inattention, and impulsivity, which troubled 3–7 % of children and their families (Dalsgaard et al., 2020; Fang et al., 2019). The healthcare expenditures for children with ADHD are $15,036 per child, about twice as much as those for children without ADHD in the United States (González-Castro et al., 2016). In addition, children with ADHD may bring different degrees of harm to individuals, families, and society, such as suicidality, self-harm, dangerous driving, automobile crashes, traffic fatalities, and so on (Faraone et al., 2021; Ruiz-Goikoetxea et al., 2018; Aduen et al., 2018; Mulraney et al., 2021). It has been well established that ADHD is an environmentally related disorder that can be influenced by multiple factors, such as perinatal factors (Thygesen et al., 2020), prematurity (Ask et al., 2018; Sucksdorff et al., 2015), low birth weight (Faraone et al., 2021; Rahman et al., 2021), socioeconomic status (Rowland et al., 2018), and adverse childhood experiences (ACEs) (Lugo-Candelas et al., 2021; Merrick et al., 2018). Therefore, as pediatricians, we call on schools, families, and society to participate in the prevention and early identification of ADHD.

Currently, the diagnosis of ADHD is primarily based on the presence of the two core symptoms of hyperactivity/impulsivity and inattention, as defined by the American Psychiatric Association (Sanz Cortes, 2022). This diagnosis typically requires repeated evaluations by professional psychiatrists, which may not be convenient for early identification of children with ADHD. Furthermore, the absence of early prediction models that incorporate various factors such as social family factors, parent-child interaction patterns, and Adverse Childhood Experiences (ACEs) makes it challenging for families to effectively manage ADHD.

A nomogram is a valuable and user-friendly statistical prediction tool that enables psychiatrists to comprehensively assess the risk of ADHD in individuals by considering various patient and disease characteristics. Developing a comprehensive nomogram that takes into account individual, family, and social-environmental factors is of paramount importance for the early identification of children at high risk for ADHD. Such a nomogram can serve as a valuable tool in establishing a collaborative management model involving society, families, and healthcare institutions. By considering multiple factors, such as family dynamics and environmental influences, this nomogram can provide a holistic approach to ADHD assessment and guide the implementation of intervention to some extent. The involvement of multiple stakeholders in the management process can ensure a more comprehensive and tailored approach to support children with ADHD and their families.

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