Novel Approaches to Infant Screening for ASD in Pediatric Primary Care

摘要-儿科初级保健中ASD婴儿筛查的新方法 该项目的总体目标是开发和验证新的工具，以识别风险， 儿童初级保健中6-12个月婴儿的自闭症谱系障碍（ASD） 设置.研究证实，ASD与大脑和行为的变化有关 这在婴儿期很明显，概念验证研究表明， 在婴儿时期可以改善早期脑功能和发育结果。 此外，早期普遍自闭症筛查已被证明可以减少现有的差异， 社会经济地位（SES）和种族/种族在获得早期诊断和治疗。 该项目利用了目前作为杜克国立卫生研究院自闭症研究项目的一部分资助的正在进行的工作 卓越中心（ACE）奖（NICHD P50 HD 093074; Dawson，中心主任）， 评估一种用于幼儿早期ASD风险评估的新型数字表型分析工具 在儿科初级护理环境中。我们的新型筛选工具SenseToKnow是基于主动 闭环感应，孩子们被显示简短，发展适当，动态 智能平板电脑或智能手机上的刺激，而同一设备中的传感器捕获 用于自动、客观量化若干行为风险标记的信息， 注意力、定向、情感、发声和运动行为的模式。拟议 研究将1）评估一个新的婴儿版本的应用程序，SenseToKnow-Infant，在一个大的 在常规儿科护理的背景下，婴儿人群，和2）检查多个 将SenseToKnow-I中的信息与 婴儿和产妇的电子健康记录（EHR）。使用直接数字 通过SenseToKnow-I和EHR中现成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 月龄），可以部署在一般人群中。

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.