UDN@CHOP/UPENN: transition to sustainability

未确诊的疾病和罕见疾病的发生与年龄、地理、社会经济地位或教育水平无关。令人沮丧的是，它们很难科学地定义和分类，但罕见疾病影响着美国3000万人，而未确诊的人尚未统计。参考CHOP/UPENN UDN反映了比四个州地理区域更大的集水区，并反映了未诊断和罕见疾病患者人群的固有二分法。我们的UDN计划有一个成熟的协作方法来解决未确诊患者群体的需求，我们准备通过改进工作流程和计算方法来实现增长。这项申请提出了三个目标，以服务超过我们原来分配的预期的额外病人。目标1描述了我们的整体诊断方法以及我们将在招募和评估工作流程中实施的变更。在目标2中，我们描述了新的诊断策略，重点是改进的生物信息学。在目标3中，我们描述了我们的长期可持续发展计划和向这种新模式的过渡。我们一直在临床和转化研究中心（名为人类表型科学中心（CHPS））观察患者，该中心促进了标准化方法，我们开发了一个用于窄数据捕获的模板，以更容易地定义关键表型。新设想的工作流程采用了分层诊断方法。患者在就诊前将进行下一代测序、代谢组学和/或免疫学测试，因为可以通过这种方式轻松进行一些诊断，从而简化患者体验。那些尚未解决的问题将有深入的表型分析和多组学诊断方法。我们的临床研究中心拥有宝贵的专业知识，并已成功地为近四分之一的患者进行了诊断。我们未来的模式在很大程度上受到我们目前UDN实践的影响，并将进一步整合简化基因组分析方法的机会，与我们机构正在进行的努力相结合，将“组学”思维更充分地融入诊断过程。我们的可持续发展模式还将改善多样性，共享技术基础设施和计算方法，加速推广和传播方法，并与研究界更广泛地进行变异和基因验证。我们有机构资金的承诺，并准备扩大我们的计划，以容纳更多的患者，改善人口统计学的多样性，提高患者诊断的多样性，并使用UDN模型，以提高临床医生的熟练程度与技术方法的诊断。

Undiagnosed diseases and rare diseases occur without respect to age, geography, socioeconomic status or level of education. They are frustratingly hard to define scientifically and to classify, yet rare diseases affect 30 million people in the USA and the undiagnosed are as yet uncounted. Referrals to the CHOP/UPENN UDN reflect a larger catchment area than just the four-state geographical region and reflect the inherent dichotomy of the undiagnosed and rare disease patient populations. Our UDN program has a mature collaborative approach to address the needs of the undiagnosed patient population and we are poised to grow through improved workflows and computational approaches. This application proposes three Aims to serve additional patients beyond the expectation of our original allocation. Aim 1 describes our overall diagnostic approach and changes we will implement in enrollment and evaluation workflows. In Aim 2, we describe new diagnostic strategies, focusing on improved bioinformatics. In Aim 3, we describe our long term sustainability plans and transition to this new model. We have been seeing patients in the Clinical and Translational Research Center, named Center for Human Phenomic Science (CHPS), which facilitates a standardized approach and we have developed a template for a narrow data capture to more easily define the key phenotypes. The newly envisioned workflow incorporates a tiered approach to diagnostics. Patients will have next generation sequencing, metabolomics, and/or immunology testing prior to being seen, reasoning that some diagnoses can be easily made in this way and thus streamlining the patient experience. Those that remain unsolved will have deep phenotyping and a multi-omics approach to diagnosis. Our Clinical Site has valuable expertise and has been successful in achieving diagnosis for nearly a quarter of the patients. Our future model is informed heavily by our current UDN practices and will additionally incorporate opportunities to streamline genomic analytic methods, interfacing with ongoing efforts at our institutions to incorporate the “omics” mindset more fully into the diagnostic journey. Our sustainability model will also improve diversity, sharing technology infrastructure and computational methods, accelerating outreach and dissemination approaches, and interfacing more widely with the research community for variant and gene validation. We have the promise of institutional funds and are poised to grow our program to accommodate more patients, improve the demographic diversity, improve the patient diagnostic diversity, and use the UDN model to improve clinician proficiancy with technologic approaches to diagnosis.