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

摘要-项目2 杜克大学自闭症卓越中心(ACE)的总体目标是使用翻译的数字健康和 解决对更有效的自闭症筛查工具的迫切需求的计算方法，客观结果 措施，以及可用于自闭症儿童临床试验的基于大脑的生物标记物。本项目 将开发和评估一种新的数字健康方法来筛查自闭症。普遍的自闭症筛查是 推荐给18个月大的儿童使用。这通常是通过护理者问卷来实现的。然而， 研究表明，通常使用的自闭症筛查问卷在用于 现实世界的环境，例如初级保健。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旨在开发一种自动的、客观的工具来预测18个月的自闭症 这可以在初级保健环境中实施。我们将使用常规收集的健康数据来开发一种 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，该工具可以 纳入儿科初级保健，并包括关于转诊和与 服务。我们将首先开发和验证一个可推广的现成模型，以预测18岁时使用的自闭症 使用纵向索赔数据(医疗补助和蓝十字蓝盾)从不同的样本 北卡罗来纳州从出生到6岁(N~230,000)连续覆盖的儿童预测 诊断为自闭症的可能性(N~6,000)。然后我们将使自闭症预测模型适用于公爵 大学卫生系统(DUHS)临床环境，并利用颗粒状电子病历增强其功能 (EHR)数据，通过使用基于机器学习的自然语言处理来嵌入提供商备注。穿过 与DUHS内外的利益相关者接触，并与项目1合作，我们将使用 用于设计临床决策支持原型的预测模型，该原型可以帮助提供者进行 适当和及时的转介。通过设计过程，我们将确定一组关键的优先因素，以 在选择适用于广泛范围的自闭症筛查的临床决策支持时要考虑 不同卫生保健环境中的利益相关者。最后，利用我们关于早期健康接触的强大数据， 我们将描述生命早期疾病模式的性质和流行程度。我们将测试 早期生活中的胃肠道问题与较高的精神病发病率相关的特定假设 到6岁时的状况。

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.