Collaborative multi-site project to speed the identification and management of rare genetic immune diseases

加速罕见遗传免疫疾病的识别和管理的多站点合作项目

• 批准号: 10549340
• 负责人: MANISH J BUTTE
• 金额: $ 79.16万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-25 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10549340
• 关键词: Academic Medical Centers; Acceleration; Age; Algorithms; Antibodies; Autoimmunity; Awareness; Bronchiectasis; California; Caring; Case Report Form; Categories; Classification; Clinic; Clinical; Clinical Immunology; Code; Common Variable Immunodeficiency; Computer Models; Computerized Medical Record; Data; Data Collection; Data Set; Diagnosis; Diagnostic; Disease; Drops; Electronic Health Record; Ethnic Origin; Evaluation; Fibrosis; Future; Gender; Genes; Genetic; Genetic Diseases; Genomics; Goals; Health; Health Care Costs; Health system; Healthcare Systems; Hospitals; Immune; Immune System Diseases; Immunogenetics; Immunologic Deficiency Syndromes; Immunological Diagnosis; Immunologics; Immunology; Individual; Infection; Inflammation; Knowledge; Laboratories; Laboratory Research; Link; Longevity; Los Angeles; Lung; Machine Learning; Manuals; Medical; Medical center; Medicine; Mendelian disorder; Modeling; Morbidity - disease rate; Natural Immunity; Patients; Phenotype; Predisposition; Prevalence; Process; Psychosocial Assessment and Care; Publishing; Quality of life; Race; Rare Diseases; Research; Risk; San Francisco; Schedule; Science; Scientist; Site; Speed; State-of-the-Art Reviews; Structure; Subjects Selections; System; Testing; Thinking; Time; Training; Universities; Visit; Work; adaptive immunity; algorithm development; clinical data repository; clinical data warehouse; collaborative approach; congenital immunodeficiency; cost; data sharing; data standards; data warehouse; disease diagnosis; disease phenotype; falls; genetic disorder diagnosis; genome sequencing; genomic data; health data; improved; innovation; medical specialties; mortality; neglect; next generation; peer; risk prediction; screening; transcriptome sequencing; video chat; whole genome

Summary The subject of this proposal is a new, collaborative approach to improve the diagnosis of primary immunodeficiency diseases (PIDs). These patients have individually rare, monogenic disorders leading to severe infections, autoimmunity, and inflammation. The prevalence of PIDs is ~1:10,000 and approximately half have antibody deficiencies as their main immunological phenotype. Most doctors are unaware of these diseases and many patients go years without a diagnosis, costing the system tens of thousands of dollars per patient yearly and unnecessarily increasing morbidity and mortality. There is a tremendous, untapped opportunity to advance the diagnosis of patients with PIDs. We propose to utilize new machine-learning approaches to algorithmically identify patients with PIDs from their electronic health records (EHR). To accomplish our goals, we have built a coalition of computational genomics groups at UCLA, UCSF, and Vanderbilt (Computational team), and clinical immunology groups at the five University of California medical centers (Los Angeles, San Francisco, Irvine, San Diego, and Davis) (Immunology team). We propose to: Identify patients with rare immune diseases by phenotype risk scoring (Aim 1). We will speed the identification of patients with rare immune diseases by surveilling the EHR using a phenotype risk scoring approach, building upon recently published work in Science. We will apply this approach to the UCLA, UCSF, and Vanderbilt clinical data repositories to identify potential cases. We will improve risk scoring by considering gender, age, and race/ethnicity. We will classify patients by whether they have an infection phenotype or immune dysregulation phenotype. Subsequently, we will expand to the larger, UC Health-wide Data Warehouse (UCHWDW), entailing 15+ million patients across all UC medical centers. We will then Identify the genetic immune diseases for these newly found subjects (Aim 2). We will follow the state-of-the-art approach employed by the UCLA and Vanderbilt Undiagnosed Disease Network (UDN) sites. We will start by sequencing all the known antibody deficiency patients across the Immunology team sites while collaboratively pre-reviewing identified cases from Aim 1 on monthly video-calls. For selected subjects, we will perform whole genome and RNA sequencing. Clinical and research laboratory testing will bring closure to the diagnostic odyssey for these subjects. The overall impact of this work accelerates the diagnosis and cure of PIDs. This project will also serve as a demonstration of how immunology sites can work together sharing electronic medical records and genomic data to advance care.

概括 该提案的主题是一种改善主要诊断的新型协作方法 免疫缺陷疾病（PID）。这些患者具有单独罕见的单基因疾病，导致 严重的感染，自身免疫性和炎症。 PID的患病率为〜1：10,000，大约为 一半的抗体缺陷是其主要免疫表型。大多数医生都不知道这些 疾病和许多患者几年没有诊断，使系统成千上万美元 患者每年，不必要地增加发病率和死亡率。有一个巨大的，未开发的 有机会推进PID患者的诊断。 我们建议利用新的机器学习方法来识别PID的患者 从他们的电子健康记录（EHR）中。为了实现我们的目标，我们建立了一个计算联盟 UCLA，UCSF和Vanderbilt（计算小组）的基因组学组和临床免疫学组 加利福尼亚大学五个医学中心（洛杉矶，旧金山，欧文，圣地亚哥和戴维斯） （免疫学团队）。我们建议：通过表型风险鉴定患有罕见免疫疾病的患者 得分（目标1）。我们将通过监视罕见免疫疾病的患者加快鉴定 EHR使用表型风险评分方法，基于最近发表的科学工作。我们将 将此方法应用于UCLA，UCSF和Vanderbilt临床数据存储库，以识别潜在病例。 我们将通过考虑性别，年龄和种族/种族来提高风险评分。我们将通过 它们是否具有感染表型或免疫失调表型。随后，我们将扩展 到较大的UC健康范围内的数据仓库（UCHWDW），所有UC需要15百万患者 医疗中心。然后，我们将确定这些新发现的受试者的遗传免疫疾病 （目标2）。我们将遵循加州大学洛杉矶分校和范德比尔特采用的最先进方法 疾病网络（UDN）地点。我们将首先测序所有已知的抗体缺乏症患者 免疫学团队站点在协作预审的同时，在每月视频通话中确定了AIM 1的案例。 对于选定的受试者，我们将执行整个基因组和RNA测序。临床和研究实验室 测试将使这些受试者的诊断奥德赛结束。 这项工作的总体影响加速了PID的诊断和治疗。该项目也将作为一个 证明免疫学部位如何共享电子病历和基因组 数据以提高护理。