An innovative model of early ASD diagnosis in the primary care setting: Integrating clinical evaluation and biomarkers to improve diagnostic accuracy

项目总结/摘要 自闭症谱系障碍（ASD）的早期识别和随后的循证参与 干预措施与巨大的发展收益和较低的生命周期成本有关。尽管核心 症状出现在生命的第一年，全国ASD诊断的平均年龄是4到5岁， 对低收入、少数民族和农村家庭儿童的诊断更加滞后。因此， 一个关键的公共卫生需要开发和测试模型的准确，精简的社区为基础的ASD 诊断.迄今为止的大部分研究都集中在对基于社区的模式进行独立审查上 早期ASD诊断和潜在生物过程的测量作为替代方法， 识别自闭症儿童然而，考虑到ASD表型的异质性以及 标准诊断工具的局限性，整合临床和生物行为的多方法方法 这些措施可能对提高社区ASD诊断的准确性产生最大的影响 设置.我们的目标是测试一种创新的ASD诊断方法，该方法将临床评估和 在一个大的高风险社区转介样本中评估小学儿童的生物行为标志物 护理设置。我们提出了三个具体目标：1）评估早期评估（EE）的诊断准确性 社区初级保健环境中ASD诊断的中心模型，2）确定生物行为是否 标记物可以可靠地区分在高危社区中有和没有ASD的幼儿 样本，以及3）确定临床和生物行为测量的组合是否可以用于 准确预测ASD诊断结果的高风险样本的幼儿评估的主要 护理设置。印第安纳州的EE中心将对120名16-30岁的儿童进行连续抽样 个月，由ASD专家使用标准化协议进行诊断确认，包括 自闭症诊断观察表-2以及发展水平和适应技能的措施。一 一系列眼动跟踪测量（瞳孔扩张、瞳孔对光反射、眨眼率、扫视潜伏期和注视 时间）将提供神经调质活性的间接测量（即，去甲肾上腺素乙酰胆碱 多巴胺）和非社会注意力脱离效率和社会偏好 与非社会性刺激相比。我们的方法展示了高水平的科学创新，因为它 整合临床评估和生物行为标记物测定，以开发和测试早期 ASD诊断在当地初级保健机构。这项研究之所以重要，是因为它有可能 为了减少ASD初次诊断的等待时间，并允许更早地进入循证干预， 从而改善儿童的预后并降低与该疾病相关的社会成本。未来这些 临床和生物行为特征可用于预测个体对干预的反应， 从而为干预分配提供了更精确和成本效益更高的方法。

Project Summary / Abstract Early identification of autism spectrum disorder (ASD) and subsequent engagement in evidence-based interventions is associated with substantial developmental gains and lower lifetime costs. Despite core symptoms emerging in the first year of life, the national average age of ASD diagnosis is not until 4 to 5 years, with diagnosis of children from lower income, minority, and rural families lagging further behind. Thus, there is a critical public health need to develop and test models of accurate, streamlined community-based ASD diagnosis. Much of the research to date has focused on independent examination of community-based models of early ASD diagnosis and measures of underlying biological processes as alternative approaches to identifying children with ASD. However, given the heterogeneous nature of the ASD phenotype as well as limitations in standard diagnostic tools, multi-method approaches that integrate clinical and biobehavioral measures are likely to have the most impact on advancing the accuracy of ASD diagnosis in the community setting. Our objective is to test an innovative method of ASD diagnosis that integrates clinical evaluation and assessment of biobehavioral markers in a large high-risk community-referral sample of children in the primary care setting. We propose three specific aims: 1) Evaluate the diagnostic accuracy of the Early Evaluation (EE) Hub model of ASD diagnosis in the community primary care setting, 2) Determine whether biobehavioral markers can reliably differentiate young children with and without ASD in a high-risk community referred sample, and 3) Determine whether a combination of clinical and biobehavioral measures can be used to accurately predict ASD diagnostic outcome in a high-risk sample of young children evaluated in the primary care setting. EE Hubs across the state of Indiana will refer a consecutive sample of 120 children, ages 16-30 months, for diagnostic confirmation by an expert ASD-specialist using a standardized protocol including the Autism Diagnostic Observation Schedule – 2 as well as measures of developmental level and adaptive skills. A series of eye-tracking measures (pupil dilation, pupillary light reflex, blink rate, saccadic latency, and looking time) will provide indirect measures of neuromodulator activity (i.e., norepinephrine, acetylcholine, and dopamine, respectively) and non-social attentional disengagement efficiency and preferences for social compared to non-social stimuli. Our approach demonstrates a high level of scientific innovation because it integrates both clinical evaluation and assays of biobehavioral markers to develop and test a model of early ASD diagnosis in local primary care settings. The proposed research is significant because it has the potential to decrease wait times for initial ASD diagnosis and allow for earlier entry into evidence-based interventions, thereby improving child outcomes and reducing societal costs associated with the disorder. In the future, these clinical and biobehavioral profiles could be used to predict how an individual may respond to interventions, allowing for a more precise and cost effective method for intervention allocation.