A Big Data Approach Toward the Development of New Quantitative Autism Severity Scores from Existing Instruments

利用现有仪器开发新的定量自闭症严重程度评分的大数据方法

基本信息

  • 批准号:
    10438954
  • 负责人:
  • 金额:
    $ 38.46万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-06-07 至 2025-06-06
  • 项目状态:
    未结题

项目摘要

Abstract Autism spectrum disorder (ASD) symptom scores from existing measures are influenced by a range of factors including age, sex, cognitive ability, language level, co-occurring psychiatric symptoms and behavioral problems. Inadequate adjustment of assessment scores can lead to reduced specificity and mis-estimation of autism severity. Adjustment for these factors could enhance research and clinical practice, including more accurate estimation of true relationships with other variables, including etiologic factors, and better case identification, particularly for complex cases. In addition, the majority of currently available ASD measures have reduced ability to track change and response to interventions and are not linked to adaptive functioning, which is being increasingly recognized as one of the most relevant to long term outcomes in ASD. Considering these noted limitations, enhancing the performance of current measures could advance clinical practice and future research. The proposed project will focus on the Social Responsiveness Scale (SRS-2), the most widely used quantitative symptom questionnaire, and the Autism Diagnostic Observation Schedule–Second Edition (ADOS- 2), the gold-standard clinician observation measure. The investigation will utilize existing multiple high-quality large data sets and apply modern psychometric approaches in order to create new continuous-range, regression-adjusted, normative SRS-2 and ADOS-2 scores that can supplement existing scores. These scores will have better specificity for autism symptom domains, more direct links to adaptive function and, when used alone or in conjunction with existing scores, may yield greater validity for ASD diagnosis in complex, highly comorbid cases (Specific Aims 1 and 2). By virtue of having dynamic range and being more specifically related to core ASD symptom domains, newly created SRS-2 and ADOS-2 scores could have greater utility in longitudinal applications, including treatment studies and clinical monitoring of intervention response (Specific Aim 3). Knowledge gained from this innovative secondary data analysis will then be used to develop and pilot software-implemented scoring algorithms to account for relevant demographic, cognitive and clinical factors (Specific Aim 4). If successful, this approach will provide a low-cost enhancement to existing, widely-used measures that can be rapidly disseminated to clinicians and scientists for improving practice and research. The proposed AREA project will also include three cohorts of undergraduate students to build a cross-departmental and cross-institutional mental health data science training experience that can be sustained as a future undergraduate research track and as an international research collaboration focused on autism spectrum disorder. Students will be involved in all aspects of the project from database building to dissemination and the project will build key undergraduate research infrastructure as well as augment training for the next generation of mental health data scientists.
摘要 自闭症谱系障碍(ASD)的症状评分从现有的措施是由一系列因素的影响 包括年龄、性别、认知能力、语言水平、同时出现的精神症状和行为 问题评估分数调整不当可能导致特异性降低和对 自闭症严重程度调整这些因素可以加强研究和临床实践,包括更多 准确估计与其他变量的真实关系,包括病因因素,以及更好的病例 特别是对于复杂的案件。此外,大多数目前可用的ASD措施 跟踪变化和对干预措施作出反应的能力降低,与适应功能无关, 越来越多地被认为是与ASD的长期结局最相关的因素之一。考虑到这些 注意到的局限性,提高当前措施的性能可以推进临床实践和未来 research.拟议的项目将侧重于社会反应量表(SRS-2), 定量症状问卷和自闭症诊断观察表-第二版(ADOS- 2)、临床观察测量的金标准。调查将利用现有的多个高质量 大数据集和应用现代心理测量方法,以创建新的连续范围, 回归调整的标准SRS-2和ADOS-2评分,可补充现有评分。这些分数 将对自闭症症状领域有更好的特异性,与适应功能有更直接的联系, 单独或与现有评分结合使用,可能会在复杂、高度复杂的ASD诊断中产生更大的有效性。 共病病例(具体目标1和2)。由于具有动态范围并且更具体地涉及 对于核心ASD症状领域,新创建的SRS-2和ADOS-2评分在以下方面可能具有更大的效用: 纵向应用,包括治疗研究和干预反应的临床监测(具体 目标3)。从这一创新的二级数据分析中获得的知识将用于开发和试点 软件实现的评分算法,以考虑相关的人口统计学、认知和临床因素 (具体目标4)。如果成功,这种方法将为现有的广泛使用的 这些措施可以迅速传播给临床医生和科学家,以改善实践和研究。的 拟议的区域项目还将包括三批本科生,以建立一个跨部门的 和跨机构的心理健康数据科学培训经验,可以作为一个未来持续 本科生研究轨道,并作为一个国际研究合作,重点是自闭症谱 disorder.学生将参与项目的各个方面,从数据库建设到传播, 该项目将建立关键的本科生研究基础设施,并加强对下一代的培训 心理健康数据科学家

项目成果

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Thomas William Frazier其他文献

7.4 MAPPING THE RESEARCH DOMAIN CRITERIA (RDOC) SOCIAL COMMUNICATION SUB-CONSTRUCTS TO THE SOCIAL RESPONSIVENESS SCALE
  • DOI:
    10.1016/j.jaac.2019.07.713
  • 发表时间:
    2019-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Antonio Hardan;Mirko Uljarevic;Thomas William Frazier;Jennifer M. Phillips;Booil Jo;Sandy Littlefield
  • 通讯作者:
    Sandy Littlefield
6.23 Examining the Contribution of Transdiagnostic Risk Factors in the Presentation of Distinct Facets of Restricted and Repetitive Behaviors
  • DOI:
    10.1016/j.jaac.2024.08.421
  • 发表时间:
    2024-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Katherine Paszek;Ru Ying Cai;Madeline Clark;Antonio Hardan;Thomas William Frazier;Mirko Uljarevic
  • 通讯作者:
    Mirko Uljarevic
CAN BIG DATA LEAD TO BIG GAINS FOR INDIVIDUALS WITH NEURODEVELOPMENTAL CONDITIONS AND THEIR FAMILIES: CURRENT TRENDS AND FUTURE DIRECTIONS
  • DOI:
    10.1016/j.jaac.2024.07.790
  • 发表时间:
    2024-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Mirko Uljarevic;Thomas William Frazier
  • 通讯作者:
    Thomas William Frazier
5.67 Evaluating the Reliability of the Widely Used Diagnostic and Quantitative Severity Measures of ASD
  • DOI:
    10.1016/j.jaac.2024.08.388
  • 发表时间:
    2024-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Gaelle Rached;Thomas William Frazier;Antonio Hardan;Mirko Uljarevic
  • 通讯作者:
    Mirko Uljarevic
1.4 Development of Webcam-Collected and Artificial-Intelligence–Derived Social and Cognitive Performance Measures for Neurodevelopmental and Neurogenetic Conditions
  • DOI:
    10.1016/j.jaac.2024.07.641
  • 发表时间:
    2024-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Thomas William Frazier;Mirko Uljarevic;Antonio Hardan
  • 通讯作者:
    Antonio Hardan

Thomas William Frazier的其他文献

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