Leveraging routinely collected health data to improve early identification of autism and co-occurring conditions

摘要-项目2 杜克自闭症卓越中心（ACE）的总体目标是使用一个转化的数字健康和 计算方法，以解决更有效的自闭症筛查工具，客观结果的关键需求 措施，以及可用于自闭症儿童临床试验的脑生物标志物。这个项目 将开发和评估一种新的自闭症筛查数字健康方法。普遍的自闭症筛查 推荐给18个月大的孩子。这通常通过护理人员问卷来实现。然而，在这方面， 研究表明，一个常用的自闭症筛查问卷， 现实世界的环境，如初级保健。通过利用收集的与早期医疗状况相关的健康数据， 作为临床护理的一部分，项目2旨在开发一种自动、客观的工具，用于预测18个月大的自闭症 可以在初级保健环境中实施。我们将利用定期收集的健康数据， 自闭症的预测模型，并使用该模型为提供者设计临床决策支持工具， 整合到儿科初级保健中，包括关于转诊和联系的可操作指导， 服务我们将首先开发并验证一个可推广的现成模型，以预测18岁时使用的自闭症 月龄使用纵向索赔数据（医疗补助和蓝十字蓝盾）从不同的样本， 整个北卡罗来纳州的儿童，从出生到6岁（N ~ 230，000）的连续覆盖，以预测 自闭症诊断的可能性（N ~ 6，000）。然后，我们将调整自闭症预测模型，以杜克 大学健康系统（DUHS）临床环境，并通过粒度电子健康记录对其进行增强 (EHR)通过使用基于机器学习的自然语言处理来嵌入提供者注释。通过 与DUHS内外的利益相关者合作，并与项目1合作，我们将使用 预测模型，以设计一个临床决策支持原型，可以帮助提供者， 适当和及时的转介。在设计过程中，我们将确定一系列关键优先因素， 在选择适用于广泛范围的自闭症筛查临床决策支持时， 利益相关者在不同的卫生保健环境。最后，利用我们关于早期健康接触的可靠数据， 我们将描述生命早期的医学状况的性质和流行模式。我们将测试 早期生活中的胃肠道问题与精神疾病的发生率较高有关的特定假设 6岁的条件。

ABSTRACT – Project 2 The overall goal of the Duke Autism Center of Excellence (ACE) is to use a translational digital health and computational approach to address the critical need for more effective autism screening tools, objective outcome measures, and brain-based biomarkers that can be used in clinical trials with young autistic children. This Project will develop and evaluate a novel digital health approach to autism screening. Universal autism screening is recommended for children at 18 months. This is typically achieved via a caregiver questionnaire. However, research has shown that a commonly used autism screening questionnaire has reduced accuracy when used in real-world settings, such as primary care. By leveraging health data related to early medical conditions collected as part of clinical care, Project 2 aims to develop an automatic, objective tool for autism prediction at 18 months that can be implemented in primary care settings. We will use routinely collected health data to develop a prediction model for autism and use the model to design a clinical decision support tool for providers that can be integrated into pediatric primary care and includes actionable guidance regarding referrals and linkage to services. We will first develop and validate a generalizable, off-the-shelf model to predict autism for use at 18 months of age using longitudinal claims data (Medicaid and Blue Cross Blue Shield) from a diverse sample of children across North Carolina with continuous coverage from birth to age 6 years (N ~ 230,000) to predict likelihood of an autism diagnosis (N ~ 6,000). We will then adapt the autism prediction model to the Duke University Health System (DUHS) clinical environment and augment it with granular electronic health record (EHR) data by using machine learning-based natural language processing to embed provider notes. Through engagement with stakeholders both within and outside of DUHS and in collaboration with Project 1, we will use the prediction model to design a clinical decision support prototype that could assist providers in making appropriate and timely referrals. Through the design process, we will identify a set of key priority factors to consider when choosing a clinical decision support for autism screening that are applicable across a broad range of stakeholders in different health care settings. Finally, leveraging our robust data on early health encounters, we will describe the nature and prevalence of patterns of medical conditions during early life. We will test the specific hypothesis that gastrointestinal problems during early life are associated with higher rates of psychiatric conditions by age 6.