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

基本信息

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
项目类别：
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

项目摘要

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新的协作方法，以改善原发性癌症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，导致严重感染、自身免疫和炎症。 PID 的患病率约为 1:10,000，大约一半的人以抗体缺陷为主要免疫表型。大多数医生不知道这些疾病和许多患者多年都没有得到诊断，这给系统带来了数万美元的损失患者的发病率和死亡率每年不必要地增加。有一个巨大的、尚未开发的促进 PID 患者诊断的机会。我们建议利用新的机器学习方法通过算法识别 PID 患者来自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 (UCLA)、加州大学旧金山分校 (UCSF) 和范德比尔特大学 (计算团队) 的基因组学小组，以及以下大学的临床免疫学小组加州大学的五个医学中心（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：通过表型风险识别患有罕见免疫疾病的患者得分（目标 1）。我们将通过监测罕见免疫疾病患者，加快识别速度。 EHR 使用表型风险评分方法，以最近在《科学》杂志上发表的工作为基础。我们将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存储库来识别潜在病例。我们将通过考虑性别、年龄和种族/民族来提高风险评分。我们将按照以下方式对患者进行分类：他们是否具有感染表型或免疫失调表型。后续我们将拓展到更大的 UC Health-wide 数据仓库 (UCHWDW)，涵盖所有 UC 的 15+ 百万患者医疗中心。然后我们将鉴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堡大学未确诊所采用的最先进的方法疾病网络 (UDN) 站点。我们将首先对整个地区所有已知的抗体缺乏症患者进行测序免疫学团队在每月视频通话中协作预审 Aim 1 中发现的病例。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。