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

利用定期收集的健康数据来改善自闭症和并发疾病的早期识别

基本信息

批准号：
10523408
负责人：
Benjamin Alan Goldstein
金额：
$ 23.47万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-07 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10523408
关键词：
1 year old 6 year old Academy Address Age Age-Months American Attitude Autism Diagnosis Biological Markers Birth Blue Cross Blue Shield Caregivers Caring Child Childhood Clinical Clinical Data Clinical Trials Collaborations Computing Methodologies Data Data Collection Data Science Development Diagnosis Diagnostic Early Intervention Early identification Electronic Health Record Environment Evaluation Future Gastroenterologist Goals Health Health system Human Infant Learning Life Machine Learning Medicaid Medical Methods Modeling Monitor Natural Language Processing Nature Neurologist North Carolina Ophthalmologist Outcome Outcome Measure Participant Pattern Pediatrics Positioning Attribute Predictive Value Prevalence Primary Health Care Process Provider Public Health Questionnaires Research Sampling Screening procedure Services Testing Time Toddler Universities Visit Work autism spectrum disorder autistic children base behavioral outcome biomarker discovery brain based clinical care clinical decision support demographics design digital health early screening follow-up gastrointestinal health care settings health data implementation science improved improved outcome innovation iterative design novel predictive modeling primary care setting prototype routine care screening sex support tools tool usability

项目摘要

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.

摘要 - 项目2 杜克自闭症卓越中心（ACE）的总体目标是使用翻译的数字健康和计算方法以满足对更有效自闭症筛查工具的关键需求，客观结果措施和基于大脑的生物标志物，可用于与年轻攻击性儿童的临床试验中。这个项目将开发和评估一种新型的自闭症筛查数字健康方法。普遍的自闭症筛查是建议在18个月时针对儿童。这通常是通过护理人员问卷来实现的。然而，研究表明，一份常用的自闭症筛查问卷在使用时的准确性降低了现实世界中的设置，例如初级保健。通过利用与收集早期医疗状况有关的健康数据作为临床护理的一部分，项目2旨在在18个月时开发自闭症预测的自动，客观的工具可以在初级保健设置中实施。我们将使用常规收集的健康数据来开发自闭症的预测模型，并使用该模型为提供者设计临床决策支持工具集成到儿科初级保健中，包括有关推荐和联系的可行指导服务。我们将首先开发和验证可概括的现成模型，以预测自闭症以供18岁使用使用纵向索赔数据（Medicaid和Blue Cross Blue Shield）的几个月，潜水员样本北卡罗来纳州的儿童从出生到6岁（n〜230,000）的连续覆盖范围进行预测自闭症诊断的可能性（n〜6,000）。然后，我们将使自闭症预测模型适应公爵大学卫生系统（DUHS）临床环境并用颗粒电子健康记录增强（EHR）通过使用基于机器学习的自然语言处理来嵌入提供商注释的数据。通过与DUHS内部和外部的利益相关者互动，并与项目1合作，我们将使用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制作适当及时的推荐。通过设计过程，我们将确定一组关键优先级因素考虑在适用于广泛范围的自闭症筛查的临床决策支持时考虑在不同的医疗保健环境中的利益相关者。最后，利用我们在早期健康遭遇中的强大数据，我们将描述早期医疗状况模式的性质和普遍性。我们将测试具体假设，即早期生命中的胃肠道问题与较高的精神病率有关按照6岁的条件。