ADHD heterogeneity, mechanisms, and risk profile

ADHD 异质性、机制和风险状况

• 批准号: 10083596
• 负责人: JOEL T NIGG
• 金额: $ 11.14万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-17 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10083596
• 关键词: 19 year old; Affect; Algorithms; Attention deficit hyperactivity disorder; Caring; Characteristics; Child; Clinical; Data; Development; Developmental Course; Emotional; Family; Heterogeneity; Impairment; Informal Social Control; Instruction; Intervention; Knowledge; Longitudinal Studies; Longterm Follow-up; Measures; Outcome; Population; Public Health; Quality of life; Risk; Schools; Syndrome; Translating; advanced analytics; clinical care; clinical decision-making; clinical practice; clinical predictors; clinical translation; cognitive control; computerized tools; emotion regulation; follow-up; improved; next generation

ADHD remains a serious public health concern particularly in relation to long-term outcome, with recent follow up data from other projects, such as the MTA study, suggesting that current assessment and sustained treatment practices do not much alter clinical course. In addition, societal and clinical concerns remain about mis-identification of children with ADHD who need care, because some children seem to improve developmentally, while others have poor outcomes. While a great deal is known about the correlates of developmental course, little of this knowledge has translated to clinical practice, and in particular to clinical prediction—essentially, deciding whether a child, presenting with ADHD, requires intervention or for whom a clinician can afford to wait and observed. We have previously made progress here by re-conceptualizing ADHD as a problem in self-regulation involving cognitive control as well as emotion regulation and emotionality. This has helped us find new and valid sub-profiles that are clinically predictive. We follow that up here and add new effort to use advanced computational tools to predict clinical course over a developmental period from 7-19 years of age, using a range of measures at different levels of analysis, and thus to develop algorithms that could point the way toward next-generation clinical translation from longitudinal studies. RELEVANCE (See instructions): ADHD continues to be a serious and impairing syndrome for millions of children, affecting families, schools, and life quality. While a great deal is known about the correlates of ADHD, this information has not translated sufficiently into more efficient clinical decision-making that recognizes the variety of children within this population. This project seeks to use advanced analytics and long-term follow up data to identify better ways to identify groups of children with ADHD with characteristic profiles to improve clinical care.

ADHD 仍然是一个严重的公共卫生问题，特别是与长期结果相关，最近 跟进其他项目的数据，例如 MTA 研究，表明当前的评估和 持续的治疗实践不会太大改变临床病程此外，社会和临床问题。 仍然存在对需要护理的多动症儿童的错误识别，因为有些孩子似乎 尽管人们对此有很多了解，但其他人的发展却很糟糕。 与发育过程相关的知识很少转化为临床实践，并且在 特别是临床预测——本质上是确定患有多动症的儿童是否需要 干预或临床医生有能力等待和观察的人我们之前已经取得了进展。 在此，将多动症重新概念化为涉及认知控制以及自我调节的问题 这帮助我们找到了临床上新的、有效的子特征。 我们在此继续进行预测，并添加新的努力来使用先进的计算工具进行预测。 7-19岁发育期的临床过程，在不同时期使用一系列措施 分析水平，从而开发可以为下一代临床指明道路的算法 纵向研究的翻译。 相关性（参见说明）： 对于数百万儿童来说，多动症仍然是一种严重且有害的综合症，影响着家庭、学校、 虽然人们对多动症的相关性了解很多，但尚不清楚这些信息。 充分转化为更有效的临床决策，识别儿童的多样性 该项目旨在利用先进的分析和长期跟踪数据来识别这一人群。 更好的方法来识别具有特征特征的多动症儿童群体，以改善临床护理。