Machine-Assisted Interdisciplinary Approach For Early Clinical Evaluation of Neurodevelopmental Disorders

机器辅助跨学科方法对神经发育障碍的早期临床评估

基本信息

批准号：
10394658
负责人：
Seth I Berger
金额：
$ 35.58万
依托单位：
CHILDREN'S RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10394658
关键词：
Address Affect Algorithms Appointment Area Biometry Caregivers Caring Child Child Health Childhood Clinic Clinical Clinical Data Clinical assessments Code Computerized Medical Record Data Demographic Factors Development Developmental Delay Disorders Diagnosis Diagnostic Disease District of Columbia Early Diagnosis Early Intervention Early identification Ensure Epidemiologic Factors Evaluation Evaluation Research Genetic Genetic Counseling Genetic Risk Genetic Screening Genomics Guidelines Healthcare Hospitals Individual Intake Investigation Knowledge Lead Machine Learning Medical Medical Genetics Neurodevelopmental Disorder Notification Pathway interactions Patients Pediatrics Phase Phenotype Physicians Pilot Projects Population Primary Health Care Process Public Health Questionnaires Rare Diseases Recording of previous events Records Resources Risk Site Specialist Standardization System Telemedicine Testing Time Training United States Variant Visit Well Child Visits base care providers clinical encounter clinical sequencing cost feature extraction genetic disorder diagnosis genetic testing genome sequencing high risk improved innovation insurance claims interdisciplinary approach machine learning algorithm metropolitan molecular diagnostics multidisciplinary patient screening pediatrician personalized management preservation prevent primary care setting programs remote visit research clinical testing routine screening screening standard of care support tools targeted treatment variant of unknown significance whole genome

项目摘要

ABSTRACT Neurodevelopmental delay is a feature of a majority of rare diseases and is often the first presenting sign. Nonspecific early presentations of rare disorders challenge both patients and caregivers who often struggle for years without diagnoses, and physicians who must distinguish between common concerns and rare disease. Early evaluations can streamline the diagnostic process and lead to rapid implementation of targeted therapies. In this proposal, our primary objective is to shorten the pathway to comprehensive genetic evaluations for suspected neurodevelopmental disorders (NDDs) through primary care electronic medical record (EMR) based machine-learning algorithmic identification of patients clinically eligible for genetic evaluation. We discuss our plan for integration of pretest genetic counseling in the primary care setting through video and telemedicine, and will develop a paradigm that can be adapted to the pediatric primary care workflow. We will implement and iteratively improve upon our algorithms during the UG3 Phase through a close partnership between academic geneticists, neurodevelopmental pediatricians, and the primary care pediatricians of Children’s Health Center (CHC) in Washington DC, and transition the mature program during the UH3 to all CNH Goldberg Center practices. We will thus bring early genetic evaluations to the largest network of primary pediatric practices in the D.C. Metropolitan area by leveraging our multidisciplinary team dedicated to early identification and characterization of NDDs. We will address the following aims: Aim 1 (UG3): Assess utility of a scalable machine-assisted pipeline for early identification of patients with NDDs based on automated feature extraction from EMR. We will train and iteratively refine a machine- learning algorithm to identify children at high risk of genetic NDDs based on their EMR. Aim 2 (UG3): Assess utility of a primary care clinician-initiated multidisciplinary evaluation to expedite genetic evaluation and neurodevelopmental phenotyping. Our workflow starting with automated chart identification will permit primary care providers access to our multidisciplinary neuro-developmental-genetics team. Technical innovations including telemedicine, application based videos, and electronic intakes will facilitate this process. Aim 3 (UH3): Evaluate generalizability of machine-assisted identification of NDDs from EMR by expanding access to entire network of Goldberg Center Pediatric practices. We will expand to all CNH primary care clinics serving the highly diverse Washington DC metropolitan area and ensure approach is robust to the specific demographic and epidemiologic factors of different sites. Our approach will identify patients with developmental delay in the primary care setting at the beginning of a diagnostic odyssey and expedite deep phenotyping and genetic investigations, as well as reevaluate sequencing results for early diagnosis in the diverse DC metropolitan population.

抽象的神经发育迟缓是大多数罕见疾病的一个特征，并且通常是第一个出现的症状。罕见疾病的非特异性早期表现对患者和护理人员来说都是一个挑战，他们经常为治疗而苦苦挣扎。多年没有诊断出来，医生必须区分常见问题和罕见疾病。早期评估可以简化诊断过程并导致快速实施有针对性的措施在这项提案中，我们的主要目标是缩短全面遗传疗法的途径。通过初级保健电子医疗评估疑似神经发育障碍（NDD）基于记录（EMR）的机器学习算法识别临床上符合遗传条件的患者我们讨论了通过评估将测试前遗传咨询纳入初级保健机构的计划。视频和远程医疗，并将开发一种适用于儿科初级保健的范例我们将在 UG3 阶段通过一个流程来实施并迭代改进我们的算法。学术遗传学家、神经发育儿科医生和初级保健之间的密切合作华盛顿特区儿童健康中心 (CHC) 的儿科医生，并在期间过渡成熟的计划因此，我们将把 UH3 应用于所有 CNH Goldberg 中心的实践中。利用我们的多学科团队，在华盛顿特区建立初级儿科诊所网络致力于 NDD 的早期识别和表征，我们将实现以下目标：目标 1 (UG3)：评估可扩展的机器辅助管道对于早期识别患者的效用通过基于 EMR 的自动特征提取的 NDD，我们将训练并迭代地改进机器。学习算法根据 EMR 识别遗传 NDD 高风险儿童。目标 2 (UG3)：评估初级保健临床医生发起的多学科评估的效用，以加快我们的工作流程从自动化图表开始。鉴定将使初级保健提供者能够获得我们的多学科神经发育遗传学信息团队的技术创新包括远程医疗、基于应用的视频和电子摄入。促进这一过程。目标 3 (UH3)：通过 EMR 评估机器辅助识别 NDD 的普遍性访问不断扩大的 Goldberg 中心儿科诊所的整个网络我们将扩展到所有 CNH。初级保健诊所为高度多样化的华盛顿特区大都市区提供服务，并确保方法是对不同地点的特定人口和流行病学因素具有鲁棒性。我们的方法将在治疗开始时在初级保健机构中识别发育迟缓的患者诊断奥德赛并加速深入的表型分析和遗传研究，以及重新评估测序结果可在不同的 DC 大都市人群中进行早期诊断。