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.

多动症仍然是一个严重的公共卫生关注，尤其是与长期结果有关的问题，最近 跟进其他项目的数据，例如MTA研究，表明当前的评估和 持续的治疗方法并没有改变临床过程。此外，社会和临床问题 仍然是关于需要护理的ADHD儿童的错过识别的，因为有些孩子似乎 在发展方面改善，而其他人的结果却很差。虽然对 发展过程的相关性，这些知识的几乎没有转化为临床实践，并且 特定于临床预测 - 基本上是，决定是否有多动症的孩子是否需要 干预措施或临床医生可以负担得起的等待和观察。我们以前取得了进步 在这里，通过将多动症重新概念化为自我调节的问题，涉及认知控制和 情绪调节和情绪。这帮助我们找到了临床上的新的和有效的子项目 预测性。我们在这里跟进并添加新的努力来使用高级计算工具来预测 在7-19岁的发育期间，使用一系列不同的措施 分析水平，从而开发可能指向下一代临床方面的算法 纵向研究的翻译。 相关性（请参阅说明）： ADHD仍然是数百万儿童的严重且受损的综合症，影响家庭，学校， 和生活质量。虽然有关多动症的相关性知之甚少，但此信息尚未 足够翻译成更有效的临床决策，以认识到儿童的种类 在这个人群中。该项目试图使用高级分析和长期后续数据来识别 更好的方法来识别具有特征性特征的多动症儿童群体以改善临床护理。