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

摘要 现有的自闭症谱系障碍(ASD)症状评分受到一系列因素的影响 包括年龄、性别、认知能力、语言水平、共同出现的精神症状和行为 有问题。评估分数调整不当可能会导致特异性降低和误判 自闭症严重程度。对这些因素进行调整可以加强研究和临床实践，包括 准确估计与其他变量的真实关系，包括病因因素和更好的病例 身份识别，特别是复杂案件的识别。此外，目前可用的大多数ASD措施都已 跟踪变化和对干预措施的反应的能力降低，与适应功能无关，这 越来越多地被认为是与ASD长期结果最相关的因素之一。考虑到这些 注意到局限性，加强当前措施的执行可以促进临床实践和未来 研究。拟议的项目将侧重于社会反应性量表(SRS-2)，这是使用最广泛的 量化症状问卷和自闭症诊断观察表-第二版(ADOS- 2)，临床医生观察的金标准措施。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学方法，以创建新的连续范围， 回归调整，标准化的SRS-2和ADOS-2分数，可以补充现有的分数。这些分数 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，并且在使用时 单独或结合现有评分，可能对复杂、高度的ASD诊断产生更大的有效性 并存案件(具体目标1和2)。凭借具有动态范围和更具体地相关 对于ASD症状域的核心，新创建的SRS-2和ADOS-2评分在 纵向应用，包括治疗研究和干预反应的临床监测(特定 目标3)。从这种创新的二次数据分析中获得的知识将用于开发和试点 软件实施的评分算法，以考虑相关的人口统计、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 可迅速传播给临床医生和科学家以改进实践和研究的措施。这个 建议的区域项目还将包括三批本科生建设一个跨部门的 和跨机构的心理健康数据科学培训经验，可以持续作为未来 本科生研究跟踪和作为一个专注于自闭症谱系的国际研究合作 无序。学生将参与该项目的所有方面，从数据库建设到传播，以及 该项目将建设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。

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.