Center for Undiagnosed Diseases at Stanford

项目总结 斯坦福大学未诊断疾病中心是未诊断疾病网络的成员网站，致力于改善未诊断和罕见疾病患者及其家人的生活。该中心的重点是有效和可持续地实施尖端诊断方法。我们率先使用新的分子诊断学和分析策略来调查最具挑战性的病例。在追求诊断进步的同时，我们寻求更深入地了解未诊断患者群体的需求和经验，以便为实施有关参与者体验和纳入历史上未得到充分服务的人群的最佳实践提供信息。保险不足和获得专科护理和高级诊断的机会减少的影响对服务不足的人群的影响不成比例，这使得在罕见疾病研究中开展外联至关重要。斯坦福大学的未诊断疾病中心(CUD)将继续努力实现可持续性，改进方法，并与临床实践相结合。在这里，我们提出了一个研究计划，它将(1)促进对患有未诊断疾病的患者的准确诊断，重点是那些没有或有有限保险、经济或语言障碍的患者；(2)使用新的方法进行数据分析和整合不同的OME以提高诊断率；以及(3)加强我们对多样性对诊断过程影响的理解。在目标1中，我们专注于加强不同参与者的招募，我们建议招募和评估新的患者队列。这将包括表型评估和生物样本收集，以促进基因组、多基因组和细胞疾病的评估。我们正在扩大我们当地的患者倡导伙伴关系，包括一个当地的UDN同龄人小组。在目标2中，我们提出了提高诊断产量的方法和技术，这些方法和技术尚未跨越翻译鸿沟。我们利用转录组学、代谢组学、长阅读测序和免疫组学来揭示未确诊参与者的诊断和机制。我们应用新的计算方法，将多组学和表型数据与整个医学文献进行系统集成，以提高诊断效率。在目标3中，我们通过促进区域伙伴关系，促进历来未得到充分服务的人口参与这项研究，侧重于可持续性。这项工作将包括扩大外展和教育。此外，我们还将系统地调查参与者的体验，包括患者报告的结果，以最好地了解该网络对患者社区的价值。这项工作将为我们的做法提供信息，并有助于提供必要的证据基础，以支持继续和扩大利益攸关方对统一数字网络的投资。

PROJECT SUMMARY The Center for Undiagnosed Diseases at Stanford, a member site of the Undiagnosed Diseases Network, works to improve the lives of patients with undiagnosed and rare diseases and their families. The Center is focused on the efficient and sustainable implementation of cutting-edge methods for diagnosis. We pioneer the use of new molecular diagnostics and analytic strategies to investigate the most challenging cases. In parallel with pursuing diagnostic advances, we seek to more deeply understand the needs and experience of the undiagnosed patient community to inform the implementation of best practices concerning participant experience and the inclusion of historically underserved populations. The impacts of underinsurance and reduced access to subspecialty care and advanced diagnostics fall disproportionately on underserved populations, making it critical to undertake outreach in rare disease studies. The Center for Undiagnosed Diseases (CUD) at Stanford will continue our efforts toward sustainability, refinement of methods, and integration with clinical practice. Here, we propose a program of study that will (1) facilitate accurate diagnosis of patients with undiagnosed diseases, with emphasis on those without or with limited insurance, economic or language barriers; (2) use novel approaches in data analysis and integration of different ‘omes to improve diagnostic rates; and (3) enhance our understanding of the impact of diversity on the diagnostic process. In Aim 1, we focus on enhancing the recruitment of diverse participants we propose to enroll and evaluate a new cohort of patients. This will include phenotypic assessment and biosample collection to facilitate genomic, multi-omic, and cellular disease evaluation. We are expanding our local patient advocacy partnerships, including a local UDN PEER group. In Aim 2, we advance methods and technologies to enhance diagnostic yield that have not yet crossed the translational divide. We leverage transcriptomics, metabolomics, long read sequencing, and immunomics to uncover diagnoses and mechanisms in undiagnosed participants. We apply novel computational approaches for systematic integration of multiomic and phenotypic data with the entire medical literature to improve diagnostic yield. In Aim 3, we focus on sustainability by promoting regional partnerships to promote the participation of historically underserved populations in the study. This work will encompass expanded outreach and education. We additionally will systematically investigate participant experience encompassing patient-reported outcomes to best understand the value of the Network to the patient community. This work will inform our practices and contribute to the evidence base necessary to support continued and expanded stakeholder investment in the UDN.