Novel Approaches to Infant Screening for ASD in Pediatric Primary Care

摘要-在儿科初级保健中筛查ASD婴儿的新方法 该项目的总体目标是开发和验证识别风险的新工具 儿科初级保健中6-12个月大婴儿的自闭症谱系障碍(ASD) 布景。研究证实，ASD与大脑和行为的变化有关 这在婴儿期就很明显，概念验证研究表明，早期干预 在婴儿期可以改善早期大脑功能和发育结果。 此外，早期普遍的自闭症筛查已被证明可以减少现有的差异。 关于获得早期诊断和治疗的社会经济地位(SES)和族裔/种族。 该项目利用了目前作为杜克国立卫生研究院自闭症一部分资助的正在进行的工作 卓越中心(ACE)奖(NICHD P50 HD093074；中心主任道森)，这是 评估一种新的用于幼儿ASD早期风险评估的数字表型工具 在儿科初级保健环境中。我们的新型筛选工具SenseToKnow基于Active 闭环感知，向儿童展示简短的、适合发展的、有活力的 在智能平板电脑或智能手机上进行刺激，同时同一设备中的传感器捕获 用于自动、客观地量化几个行为风险标记的信息，基于 注意力、定向、情感、发声和运动行为的模式。建议数 研究人员将对该应用程序的新婴儿版SenseToKnow-Infant进行大规模评估 在常规儿科护理的背景下的婴儿人口，以及2)检查多- 结合SenseToKnow-I和 来自婴儿和产妇电子健康记录的信息。使用两种直接数字 通过SenseToKnow-I和EHR中随时可用的数据进行行为测量，我们的目标是 开发并验证用于婴儿的多模式ASD风险评估算法(6-12 3个月)，可在普通人群中部署。

ABSTRACT – Novel Approaches to Infant Screening for ASD in Pediatric Primary Care The overall goal of this project is to develop and validate novel tools to identify risk for autism spectrum disorder (ASD) in infants 6-12 months of age in a pediatric primary care setting. Research has confirmed that ASD is associated with changes in brain and behavior that are evident during infancy, and proof-of-concept studies suggest that early intervention during the infant period can improve early brain function and developmental outcomes. Moreover, early universal autism screening has been shown to reduce existing disparities based on socioeconomic status (SES) and ethnicity/race in access to early diagnosis and treatment. This project leverages ongoing work that is currently funded as part of the Duke NIH Autism Center of Excellence (ACE) Award (NICHD P50 HD093074; Dawson, Center Director), which is evaluating a novel digital phenotyping tool for early ASD risk assessment in toddler-age children in a pediatric primary care setting. Our novel screening tool, SenseToKnow, is based on active closed-loop sensing, where children are shown brief, developmentally-appropriate, dynamic stimuli on a smart tablet or smartphone, while the sensors in the same device capture information for automatic, objective quantification of several behavioral risk markers, based on patterns of attention, orienting, affect, vocalizations, and motor behavior. The proposed research will 1) evaluate a novel infant version of the app, SenseToKnow-Infant, in a large population of infants in the context of routine pediatric care, and 2) examine the utility of a multi- modal approach to risk assessment that combines information from SenseToKnow-I with information from infant and maternal electronic health records (EHRs). Using both direct digital behavioral measurement via SenseToKnow-I and data readily available in the EHR, we aim to develop and validate a multimodal ASD risk assessment algorithm for use in infants (6-12 months of age) that can be deployed in the general population.