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.

总结 该提案的主题是一种新的协作方法，以改善原发性肝癌的诊断。 免疫缺陷疾病（PID）。这些患者患有个别罕见的单基因疾病， 严重感染、自身免疫和炎症。PID的患病率约为1：10，000， 半数人的主要免疫表型是抗体缺陷。大多数医生都不知道这些 许多患者多年来没有得到诊断，每个系统花费数万美元， 患者每年和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PID患者的诊断。 我们建议利用新的机器学习方法来算法识别PID患者 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（计算团队）的基因组学小组， 加州大学的五个医学中心（洛杉矶、旧金山弗朗西斯科、欧文、圣地亚哥和戴维斯） （免疫学）。我们建议：通过表型风险识别罕见免疫性疾病患者 评分（目标1）。我们将通过监测患者的免疫系统， EHR使用表型风险评分方法，建立在最近发表在《科学》上的工作基础上。我们将 将此方法应用于UCLA、UCSF和范德比尔特临床数据库，以识别潜在病例。 我们将通过考虑性别、年龄和种族/民族来改善风险评分。我们将根据以下因素对患者进行分类： 无论它们是否具有感染表型或免疫失调表型。随后，我们将扩大 到更大的UC健康范围数据仓库（UCHWDW），需要所有UC的1500多万患者 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 (Aim 2）。我们将遵循加州大学洛杉矶分校和范德比尔特未确诊的最先进的方法 疾病网络（UDN）。我们将首先对所有已知的抗体缺乏症患者进行测序， 免疫学小组地点，同时通过每月视频电话合作预先审查目标1中确定的病例。 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 测试将为这些受试者的诊断之旅画上句号。 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 演示免疫学站点如何协同工作，共享电子病历和基因组 数据，以促进护理。