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.

抽象的神经发育延迟是大多数罕见疾病的特征，通常是第一个呈现迹象。罕见疾病的非特异性早期介绍挑战了患者和经常为之奋斗的护理人员几年没有诊断，必须区分常见问题和罕见疾病的医生。早期评估可以简化诊断过程，并导致目标快速实施疗法。在此提案中，我们的主要目标是缩短通往综合遗传的途径通过初级保健电子医疗评估可疑神经发育障碍（NDDS）基于记录（EMR）的机器学习算法鉴定患者临床上有资格获得遗传评估。我们讨论了通过通过视频和远程医疗，并将开发一个可以适应儿科初级保健的范式工作流程。我们将在UG3阶段通过A实施并迭代地对我们的算法进行改进学术遗传学家，神经发育儿科医生与初级保健之间的密切合作伙伴关系华盛顿特区儿童健康中心（CHC）的儿科医生，并在成熟计划过渡期间所有CNH Goldberg中心的UH3练习。因此，我们将将早期的遗传评估带到最大通过利用我们的多学科团队，在华盛顿大都市地区的主要小儿实践网络致力于NDD的早期识别和表征。我们将解决以下目标： AIM 1（UG3）：评估可扩展的机器辅助管道的效用，以及早确定患者使用基于EMR自动化特征提取的NDD。我们将训练并迭代地完善机器 - 学习基于EMR的遗传NDD高风险的儿童识别算法。 AIM 2（UG3）：评估初级保健临床启动的多学科评估的效用以加快遗传评估和神经发育表型。我们的工作流程从自动图表开始识别将允许初级保健提供者访问我们的多学科神经发展遗传学团队。远程医疗，基于应用程序的视频和电子摄入包括的技术创新将促进此过程。 AIM 3（UH3）：评估机器辅助从EMR识别NDD的概括性扩大访问Goldberg Center儿科实践的整个网络的访问。我们将扩展到所有CNH 为华盛顿特区大都会地区提供高度多样化的初级保健诊所，并确保方法是对不同站点的特定人口统计学和流行病学因素的强大鲁棒性。我们的方法将确定在A开始时在初级保健环境中发育延迟的患者诊断奥德赛和加急的深层表型和遗传研究，并重新评估潜水员DC大都市人口早期诊断的测序结果。