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.

为了促进7000种罕见遗传疾病中的诊断，临床医生已经开发了诊断 列举定义疾病的不同元素的标准。这些包括医疗问题，身体检查 发现，实验室测试结果和成像结果。但是，大多数临床诊断标准都未知 预测价值。尽管对于诊断和提供基因检测至关重要，但通常提出了它们 没有严格的证据或表现估计，例如灵敏度或特异性。次优标准可能 引起对基因检测的错误解释，具有不确定临床意义的变异或使临床医生的变异 忽略诊断，剥夺患者预后，生殖计划或靶向分子疗法。 我们以前的工作已经描述了一种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，新的基底细胞癌综合征的新型临床标准， 与现有的专家共识标准相比，我们估计新标准的灵敏度提高了。 特别是在早期（7年为53％对13％）。我的中心假设是诊断稀有小儿 可以利用基于证据的诊断方法来改善遗传疾病。而且，这样的 方法可能是解决在提供遗传转诊和测试中的不平等的途径 属于历史边缘化群体的个人。因此，我将扩展我们以前的工作 使用综合的临床医生验证表型信息来建立罕见遗传疾病的光谱 和测试诊断方法。 解决这一假设并朝着我成为独立的长期职业目标的进步 医师科学家对所有患有罕见遗传疾病的儿童进行准确，及时的诊断，I 已经制定了一项全面的五年职业发展计划。该计划描述了一种策略来获得 自然语言处理和机器学习，以人为中心的设计和 人为因素和电子健康记录干预。使用这些新技能，我将创造全面的， 37,000名怀疑或确认遗传疾病的儿童的年代表型历史。我会 将工具嵌入了临床工作流程中，该工具会引起临床医生对这些表型的验证。从这些数据，我将 实施一个框架来开发和验证遗传疾病中的诊断标准。我最初将专注于10 特定疾病。我还将开发计算上的机器学习算法，以帮助诊断 规模。接下来，我将开发基于网络的用户界面，以增强其他临床医生的能力开发和测试自己的 诊断标准。最后，我将在健康中应用相同的表型和机器学习方法 系统水平可以预测哪些儿童更有可能被诊断出患有罕见的遗传疾病。这些 努力将为建立我的长期研究计划建立基础，以实施临床 对遗传诊断的决策支持，并使我成为一名独立的R01资助的研究者。

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