Comprehensive Pediatric Phenotyping for Evidence-Based Diagnosis in Genetic Disease

用于遗传病循证诊断的综合儿科表型分析

基本信息

批准号：
10644205
负责人：
Ian Morgan Campbell
金额：
$ 14.88万
依托单位：
CHILDREN'S HOSP OF PHILADELPHIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10644205
关键词：
Access to Information Address Adopted Age Algorithms American Basal Cell Nevus Syndrome Blinded Child Childhood Chronology Clinical Clinical Decision Support Systems Clinical Informatics Computational algorithm Consensus Data Development Plans Diagnosis Diagnostic Specificity Diagnostic tests Disease Early Diagnosis Electronic Health Record Elements Family Foundations Funding Future Genetic Genetic Diseases Genetic Predisposition to Disease Goals Health Health system Hereditary Malignant Neoplasm Human Image Individual Inequity Informatics Inpatients Intervention Judgment Knowledge Laboratories Lead Machine Learning Medical Medical Genetics Methodology Molecular Natural Language Processing Online Systems Outcome Outpatients Participant Patients Performance Phenotype Physicians Population Heterogeneity Predictive Value Process Rare Diseases Recording of previous events Research Research Personnel Resolution Risk Sampling Scientist Sensitivity and Specificity Specificity Surveys System Test Result Testing Therapeutic Intervention Time Training United States National Institutes of Health Validation Work age related career career development clinical decision support clinical diagnostics cohort diagnostic criteria diagnostic strategy disadvantaged background disease diagnosis effectiveness evaluation empowerment evidence base experience genetic disorder diagnosis genetic testing health inequalities human centered design improved insight machine learning algorithm machine learning framework marginalized population member molecular targeted therapies novel novel diagnostics prognostication programs rare genetic disorder reproductive research clinical testing skills statistical and machine learning targeted treatment tool usability variant of unknown significance

项目摘要

To facilitate the diagnosis of among 7000 rare genetic diseases, clinicians have developed diagnostic criteria that enumerate different elements that define disease. These include medical problems, physical exam findings, laboratory test results, and imaging findings. However, most clinical diagnostic criteria have unknown predictive value. Despite being critical for diagnosis and provision of genetic testing, they are typically proposed without rigorous evidence or estimates of performance such as sensitivity or specificity. Suboptimal criteria may cause faulty interpretations of genetic testing with variants of uncertain clinical significance or lead clinicians to overlook diagnosis, depriving patients of prognostication, reproductive planning, or targeted molecular therapies. Our previous work has delineated an approach to more evidence-based rare disease criteria. We developed novel clinical criteria for nevoid basal cell carcinoma syndrome using survey data and statistical optimization, and we estimate the novel criteria have improved sensitivity compared to the existing expert consensus criteria, particularly at early ages (53% versus 13% at 7 years). My central hypothesis is that diagnosis of rare pediatric genetic disease can be improved by utilizing evidence-based diagnostic approaches. Moreover, such approaches may be one avenue to address inequities in the provision of genetic referral and testing among individuals belonging to historically marginalized groups. Therefore, I will scale our previous work across the spectrum of rare genetic diseases using comprehensive, clinician-validated phenotype information to establish and test diagnostic methodologies. To address this hypothesis and progress towards my long-term career goal of becoming and independent physician-scientist that advances accurate and timely diagnosis for all children with a rare genetic disease, I have developed a comprehensive five-year career development plan. This plan delineates a strategy to gain knowledge and experience with natural language processing and machine learning, human-centered design and human factors, and electronic health record intervention. Using these new skills, I will create comprehensive, chronological phenotype histories for over 37,000 children with suspected or confirmed genetic disease. I will embed a tool in the clinical workflow that elicits clinician validation of these phenotypes. From these data, I will implement a framework to develop and validate diagnostic criteria in genetic disease. I will initially focus on 10 specific diseases. I will also develop computationally tractable machine learning algorithms to aid in diagnosis at scale. Next, I will develop a web-based user interface to empower other clinicians to develop and test their own diagnostic criteria. Finally, I will apply the same phenotyping and machine learning approaches at the health system level to predict which children are more likely to be diagnosed with a rare genetic disease. These endeavors will generate a foundation to establish my long-term research program that will implement clinical decision support for genetic diagnosis and prepare me to become an independent R01-funded investigator.

为了方便诊断 7000 种罕见遗传病，临床医生开发了诊断方法列举了定义疾病的不同要素的标准。这些包括医疗问题、体检结果、实验室检查结果和影像学结果。然而，大多数临床诊断标准尚不明确预测值。尽管对于诊断和提供基因检测至关重要，但通常建议没有严格的证据或性能评估，例如敏感性或特异性。次优标准可能导致对具有不确定临床意义的变异的基因检测的错误解释或导致临床医生忽视诊断，剥夺患者的预测、生殖计划或靶向分子治疗的机会。我们之前的工作描绘了一种更加循证的罕见病标准的方法。我们开发了使用调查数据和统计优化制定痣样基底细胞癌综合征的新临床标准，我们估计，与现有的专家共识标准相比，新标准提高了敏感性，尤其是在早期（53% 对 7 岁时为 13%）。我的中心假设是罕见儿科疾病的诊断遗传病可以通过利用循证诊断方法来改善。而且，这样的方法可能是解决在提供遗传转诊和检测方面的不平等问题的一种途径属于历史上边缘化群体的个人。因此，我将把我们之前的工作扩展到使用全面的、经临床医生验证的表型信息来确定罕见遗传病谱并测试诊断方法。为了解决这个假设并朝着我成为独立的长期职业目标迈进为所有患有罕见遗传病的儿童提供准确及时诊断的医师科学家，我制定了全面的五年职业发展计划。该计划描绘了一项战略，旨在获得自然语言处理和机器学习、以人为本的设计和人为因素和电子健康记录干预。利用这些新技能，我将创造全面的、超过 37,000 名疑似或确诊遗传病儿童的时间表型历史。我会在临床工作流程中嵌入一个工具，引发临床医生对这些表型的验证。根据这些数据，我将实施一个框架来制定和验证遗传病的诊断标准。我首先会关注 10 个特定疾病。我还将开发计算上易于处理的机器学习算法来帮助诊断规模。接下来，我将开发一个基于网络的用户界面，使其他临床医生能够开发和测试自己的用户界面诊断标准。最后，我将在健康领域应用相同的表型分析和机器学习方法系统水平来预测哪些儿童更有可能被诊断出患有罕见遗传病。这些我的努力将为建立我的长期研究计划奠定基础，该计划将实施临床基因诊断的决策支持，并帮助我成为一名独立的 R01 资助的研究者。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Expanding the clinical spectrum of biglycan-related Meester-Loeys syndrome.

扩大双糖链蛋白聚糖相关的 Meester-Loeys 综合征的临床范围。

DOI：
10.1038/s41525-024-00413-z
发表时间：
2024
期刊：
NPJ genomic medicine
影响因子：
5.3
作者：
Meester,JosephinaAN;Hebert,Anne;Bastiaansen,Maaike;Rabaut,Laura;Bastianen,Jarl;Boeckx,Nele;Ashcroft,Kathryn;Atwal,PaldeepS;Benichou,Antoine;Billon,Clarisse;Blankensteijn,JanD;Brennan,Paul;Bucks,StephanieA;Campbell,IanM;Co
通讯作者：
Co