Translating the Clinical Knowledge of Mendelian Diseases to Real-world EHR Data to Improve Identification of Undiagnosed Patients

将孟德尔疾病的临床知识转化为现实世界的 EHR 数据，以提高对未确诊患者的识别

• 批准号: 10518136
• 负责人: Lisa Bastarache
• 金额: $ 102.76万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10518136
• 关键词: Address; Adult; Affect; Algorithms; Awareness; Case Series; Catalogs; Characteristics; Clinical; Clinical Data; Complex; Data; Demographic Factors; Detection; Diagnosis; Diagnostic; Disease; Education; Electronic Health Record; Family; Genetic; Genetic Databases; Genetic Diseases; Genetic Services; Genetic study; Goals; Growth; Health; Healthcare; Individual; Informatics; Knowledge; Lead; Learning; Masks; Medical Genetics; Mendelian disorder; Methods; Morbidity - disease rate; Nature; Online Mendelian Inheritance In Man; Patients; Pattern; Phenotype; Provider; Rare Diseases; Research Personnel; Resources; Risk; Socioeconomic Factors; Socioeconomic Status; Symptoms; Technology; Test Result; Testing; Time; Translating; Variant; Visit; Work; World Health; base; clinical care; clinical database; clinical diagnosis; cost; data tools; disease diagnostic; electronic data; experience; genetic disorder diagnosis; genetic testing; health care disparity; health care service; improved; knowledge base; machine learning algorithm; mortality; novel; novel strategies; pediatric patients; portability; rare condition; rare genetic disorder; socioeconomics; targeted treatment; tool

PROJECT SUMMARY The last two decades have seen extraordinary advances in the cost, accessibility, and interpretability of genetic testing. In the context of this astonishing progress, it is striking that for many rare genetic diseases, diagnostic delay – the time between onset of symptoms and a diagnosis – has not improved. Current health care services are unable to effectively identify patients that would benefit most from genetic testing. As a result, many patients affected by genetic disease are not diagnosed for years after symptoms develop, or are never diagnosed at all, leading to costly diagnostic odysseys, health care disparities in genetic services, and preventable morbidity and mortality for those with conditions that have an effective, targeted treatment. Much of what we know about genetic disease is based on studies of individuals and their families. This has proven to be a powerful method for discerning the clinical characteristics of genetic disease, generating one of the most enduring and useful resources in medical genetics: the online Mendelian inheritance in man (OMIM). However, clinical descriptions in OMIM do not always match the way diseases are described in real- world EHR data. To improve our ability to use genetic testing effectively, we can learn, at scale, from the data clinically captured while testing and diagnosing patients. EHRs provides an opportunity to study genetic disease from a new perspective, enabling scalable methods that augment existing the knowledge base to include phenotypes observed in real-world health care data. This proposal builds on our prior work curating genetic testing data from the EHR and developing tools to identify undiagnosed patients from characteristic genetic disease profiles. Specifically, we have built a database of clinical genetic testing information extracted from the EHR for over 20,000 individuals, with detailed information regarding test results, variant interpretation, and diagnosis. From this resource, we can define EHR-based cases series of individuals with genetically-confirmed clinical diagnoses of genetic disease. We will use a data-driven approach to discern characteristic phenotypes from the EHR-based case series, and merge these results with clinical descriptions from OMIM. This approach seeks to translate the curated, durable knowledge cataloged in OMIM to a portable and scalable product that can layered on any set of EHRs to identify undiagnosed patients with genetic disease. The ultimate goals of this proposal are leverage these data and tools to 1) translate and add to clinical curations of genetic diseases using real world EHR data, 2) assess diagnostic yield of EHR-based tools that identify undiagnosed patients and 3) characterize the contribution of demographic and phenotypic features that lead to earlier or later diagnosis.

项目摘要 在过去的二十年里，在成本、可访问性和可解释性方面取得了非凡的进步， 基因检测在这一惊人进展的背景下，令人震惊的是，对于许多罕见的遗传疾病， 诊断延迟-症状出现和诊断之间的时间-没有改善。当前健康 保健服务部门无法有效地确定哪些病人最能从基因检测中受益。因此，在本发明中， 许多受遗传病影响的患者在症状出现后多年未被诊断出来，或者从未被诊断出来。 诊断，导致昂贵的诊断奥德赛，遗传服务的医疗保健差距， 可预防的发病率和死亡率，为那些有条件的有效，有针对性的治疗。 我们对遗传疾病的了解大部分是基于对个人及其家庭的研究。这 已被证明是一种强有力的方法，用于辨别遗传性疾病的临床特征， 医学遗传学中最持久和最有用的资源之一：人类的在线孟德尔遗传 （OMIM）。然而，OMIM中的临床描述并不总是与真实的中描述疾病的方式相匹配- 世界EHR数据。为了提高我们有效使用基因检测的能力，我们可以从数据中大规模学习 在测试和诊断患者时临床捕获。EHR提供了一个研究遗传学的机会 从一个新的角度来看疾病，使可扩展的方法，扩大现有的知识基础， 包括在真实世界的卫生保健数据中观察到的表型。 该提案建立在我们之前从EHR中收集基因检测数据和开发工具的工作基础上 从特征性遗传疾病谱中识别未确诊的患者。具体来说，我们建立了一个 从EHR中提取的超过20，000人的临床基因检测信息数据库， 有关测试结果、变体解释和诊断的详细信息。通过这些资源，我们可以 定义基于EHR的病例系列，这些病例是经遗传学证实的遗传性疾病临床诊断的个体。 我们将使用数据驱动的方法从基于EHR的病例系列中识别特征表型， 将这些结果与OMIM的临床描述合并。这种方法旨在翻译策划， 将OMIM中编目的持久知识转化为可移植和可扩展的产品，该产品可以分层放置在任何一组EHR上 来识别未确诊的遗传病患者 本提案的最终目标是利用这些数据和工具1）翻译并添加到临床 使用真实的世界EHR数据治疗遗传疾病，2）评估基于EHR的工具的诊断率， 识别未诊断的患者，以及3）表征人口统计学和表型特征的贡献， 导致更早或更晚诊断。