Comprehensive Pediatric Phenotyping for Evidence-Based Diagnosis in Genetic Disease

为了便于诊断7000种罕见遗传病，临床医生开发了诊断 这些标准列举了定义疾病的不同要素。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清楚， 预测值尽管对诊断和提供基因检测至关重要，但它们通常被提议 没有严格的证据或性能评估，如灵敏度或特异性。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，剥夺了患者的选择、生殖计划或靶向分子治疗。 我们以前的工作已经描绘了一种更基于证据的罕见病标准的方法。我们开发 使用调查数据和统计优化的痣样基底细胞癌综合征的新临床标准， 并且我们估计与现有的专家共识标准相比，新标准具有改进的灵敏度， 特别是在早期（53%比7岁时的13%）。我的中心假设是， 遗传性疾病可以通过利用循证诊断方法得到改善。而且这样的 这些方法可能是解决在提供遗传转诊和检测方面存在的不公平现象的一个途径， 属于历史上被边缘化群体的个人。因此，我将扩展我们以前的工作， 罕见遗传疾病谱使用全面的，临床验证的表型信息，以建立 和测试诊断方法。 为了解决这个假设，并朝着我的长期职业目标取得进展， 作为一名医生兼科学家，我为所有患有罕见遗传病的儿童提供了准确及时的诊断， 制定了全面的五年职业发展计划。该计划描绘了一个战略， 自然语言处理和机器学习的知识和经验，以人为本的设计， 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名疑似或确诊遗传性疾病的儿童的时间表型史。我会 在临床工作流程中嵌入工具，帮助临床医生验证这些表型。根据这些数据，我将 实施一个框架，以制定和验证遗传疾病诊断标准。我将首先集中在10 特定疾病我还将开发计算上易于处理的机器学习算法，以帮助诊断。 规模接下来，我将开发一个基于Web的用户界面，以使其他临床医生能够开发和测试他们自己的 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患有罕见遗传疾病。这些 努力将产生一个基础，以建立我的长期研究计划，将实施临床 基因诊断的决策支持，并准备我成为一个独立的R01资助的调查员。

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.