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：
10698195
负责人：
Benjamin Alan Goldstein
金额：
$ 34.1万
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/10698195
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 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 Care Process Provider Public Health Questionnaires Recommendation Research Sampling Screening procedure Services Testing Toddler Universities Visit Work autism spectrum disorder autistic children 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 predictive tools 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 杜克大学自闭症卓越中心 (ACE) 的总体目标是利用转化型数字健康和计算方法可满足对更有效的自闭症筛查工具、客观结果的迫切需求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志物。将开发和评估一种新颖的数字健康方法来进行自闭症筛查。建议 18 个月大的儿童使用。不过，这通常是通过看护者调查问卷来实现的。研究表明，常用的自闭症筛查问卷在用于自闭症筛查时，准确性会降低。现实世界的环境，例如初级保健，利用收集到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、客观的工具来预测 18 个月大的自闭症我们将使用定期收集的健康数据来制定可在初级保健机构中实施的计划。自闭症预测模型，并使用该模型为提供者设计临床决策支持工具，纳入儿科初级保健，并包括有关转诊和联系的可行指导我们将首先开发并验证一个通用的、现成的模型来预测 18 岁的自闭症。使用来自不同样本的纵向索赔数据（医疗补助和蓝十字蓝盾）计算月龄对北卡罗来纳州从出生到 6 岁连续覆盖的儿童 (N ~ 230,000) 进行预测然后，我们将根据杜克大学的情况调整自闭症预测模型。大学医疗系统 (DUHS) 临床环境并通过精细的电子健康记录对其进行增强 (EHR) 数据，使用基于机器学习的自然语言处理来嵌入提供者注释。与 DUHS 内部和外部的利益相关者互动并与项目 1 合作，我们将使用预测模型设计临床决策支持原型，可以帮助提供者做出通过设计过程，我们将确定一组关键的优先因素。在选择适用于广泛范围的自闭症筛查临床决策支持时要考虑最后，利用我们关于早期健康遭遇的可靠数据，我们将描述生命早期健康状况的性质和患病率。具体假设是，生命早期的胃肠道问题与较高的精神病发病率有关 6岁之前的条件。