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: 10359836
• 负责人: MANISH J BUTTE
• 金额: $ 79.21万
• 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/10359836
• 关键词: Academic Medical Centers; Age; Algorithms; Antibodies; Autoimmunity; Awareness; Bronchiectasis; California; Caring; Case Report Form; Clinic; Clinical; Clinical Immunology; Clinical Research; Code; Common Variable Immunodeficiency; Computer Models; Computerized Medical Record; Data; Data Collection; Data Set; Diagnosis; Diagnostic; Disease; Drops; Electronic Health Record; Ethnic Origin; Evaluation; 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; Publishing; Quality of life; Race; Rare Diseases; Risk; San Francisco; Schedule; Science; Scientist; Site; Speed; Structure; System; Testing; Thinking; Time; Training; Universities; Visit; Work; adaptive immunity; algorithm development; base; clinical data repository; clinical data warehouse; collaborative approach; congenital immunodeficiency; cost; 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; psychosocial; 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.

摘要 这项建议的主题是一种新的、合作的方法，以改善对原发性心脏病的诊断 免疫缺陷疾病(PID)。这些患者都有个别罕见的单基因疾病，导致 严重感染、自身免疫和炎症。PID的患病率约为1：10,000，约 半数患者的主要免疫表型为抗体缺陷。大多数医生都没有意识到这些 许多疾病和许多患者数年来没有得到诊断，导致该系统每年花费数万美元 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 有机会提高对PIDs患者的诊断。 我们建议利用新的机器学习方法来通过算法识别患有PID的患者。 从他们的电子健康记录(EHR)。为了实现我们的目标，我们建立了一个计算联盟 加州大学洛杉矶分校、加州大学旧金山分校和范德比尔特大学的基因组学小组(计算团队)，以及临床免疫学小组 加州大学的五个医疗中心(洛杉矶、旧金山、欧文、圣地亚哥和戴维斯) (免疫学小组)。我们建议：通过表型风险识别罕见免疫疾病患者 得分(目标1)。我们将通过监测罕见的免疫疾病患者的 EHR使用表型风险评分方法，建立在最近发表在《科学》杂志上的工作基础上。我们会 将这种方法应用于加州大学洛杉矶分校、加州大学旧金山分校和范德比尔特临床数据库，以确定潜在的病例。 我们将通过考虑性别、年龄和种族/民族来提高风险评分。我们将按以下标准对病人进行分类 他们是否有感染表型或免疫调节失调表型。随后，我们将扩大 对于更大的UC健康范围数据仓库(UCHWDW)，需要所有UC中的1500万名患者 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大学采用的最先进的方法 疾病网络(UDN)站点。我们将首先对所有已知的抗体缺乏症患者进行测序 免疫学团队网站，同时通过每月视频通话协作预先审查AIM 1的已确认病例。 对于选定的受试者，我们将进行全基因组和RNA测序。临床和研究实验室 检测将结束这些受试者的诊断奥德赛。 这项工作的整体影响加速了对PIDs的诊断和治疗。该项目还将作为一个 演示免疫学网站如何协同工作，共享电子病历和基因组 数据，以推进医疗保健。