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

• 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
• 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

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7000种罕见遗传病的诊断，临床医生开发了诊断 列举定义疾病的不同元素的标准。这些问题包括医疗问题、体检 发现，实验室测试结果，和成像发现。然而，大多数临床诊断标准是未知的。 预测性价值。尽管它们对诊断和提供基因检测至关重要，但它们通常被建议 没有严格的证据或对表现的估计，如敏感性或特异性。次优标准可能 用不确定的临床意义的变种导致对基因检测的错误解释或导致临床医生 忽视诊断，剥夺患者的预后、生殖计划或有针对性的分子治疗。 我们之前的工作描绘了一种更基于证据的罕见疾病标准的方法。我们开发了 使用调查数据和统计优化的新生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， 尤其是在早期儿童(53%比7岁时的13%)。我的中心假设是罕见的儿科疾病的诊断 遗传疾病可以通过利用循证诊断方法得到改善。而且，这样的 方法可能是解决在提供遗传转介和检测方面的不平等的一种途径 属于历史上被边缘化群体的个人。因此，我将把我们以前的工作扩展到 使用全面的、临床医生验证的表型信息来建立罕见遗传病的谱系 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前进 内科科学家，为所有患有罕见遗传病的儿童提供准确和及时的诊断，我 制定了全面的五年职业发展计划。这项计划勾勒出了一个战略，以获得 具有自然语言处理和机器学习、以人为中心的设计和 人为因素，以及电子健康记录干预。使用这些新技能，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病史。这就做 在临床工作流程中嵌入一个工具，促使临床医生对这些表型进行验证。从这些数据中，我将 实施一个框架，以制定和验证遗传病诊断标准。我首先将重点放在10个 特定的疾病。我还将开发易于计算的机器学习算法，以帮助在 比例。接下来，我将开发一个基于Web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Health中应用相同的表型和机器学习方法 系统水平预测哪些儿童更有可能被诊断出患有一种罕见的遗传病。这些 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 为基因诊断提供决策支持，并让我做好准备，成为一名由R01资助的独立研究员。

项目成果

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