Temporal Phenotypes and Risk Models for the Post-COVID Syndrome and its sub-types

新冠肺炎后综合症及其亚型的时间表型和风险模型

基本信息

批准号：
10666655
负责人：
Hossein Estiri
金额：
$ 81.81万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-15 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10666655
关键词：
2019-nCoV Accounting Acute Address Affect Anxiety Appointment COVID-19 COVID-19 diagnostic COVID-19 long hauler COVID-19 pandemic COVID-19 patient Caring Characteristics Chest Pain Chicago Chronic Clinical Clinical Data Code Collection Computational algorithm Coughing Data Decision Making Diagnosis Diagnostic Digital biomarker Disease Disparity Dyspnea Early identification Electronic Health Record Etiology Fatigue Future Generations Health Healthcare Systems Hospitals Image Income Infection Informatics Institution International Classification of Diseases International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10)Kentucky Knowledge Label Laboratories Learning Long COVID Medical Memory impairment Mental Depression Methodology Methods Modeling Mutation Names Neurocognitive New England Organ Patients Persons Phase Phenotype Philadelphia Population Post-Acute Sequelae of SARS-CoV-2 Infection Post-Traumatic Stress Disorders Predisposing Factor Preparation Procedures Prognosis Public Health Pulmonary function tests Pulmonology Ramp Recovery Research Resolution Risk Risk Factors SARS-CoV-2 infection Sentinel Severities Side Signs and Symptoms Silver Site Strategic Planning Structure Symptoms System Testing Therapeutic Time Uncertainty United States National Institutes of Health Update Vaccinated Vaccination Vaccines Virus Virus Diseases Work acute infection cohort coronavirus disease cost data modeling epidemiologic data fighting improved informatics tool insight member multidisciplinary multimodal data novel pandemic disease patient population post-COVID-19 psychologic repository tool

项目摘要

Project Summary/Abstract The fight against the SARS-CoV-2, the coronavirus that causes COVID-19, is ramping up with vaccinations and therapeutics. Yet there is a growing urgency to study and address the other side of the pandemic, a shapeshifting byproduct known as the post-acute sequelae of COVID-19 (PASC), among other names. Even if several millions are successfully vaccinated against the virus, many more are still likely to be infected and for hundreds of thousands (if not millions) of those, recovery from the acute phase of COVID-19 infection will be grueling with a debilitating second act. A collection of persistent physical (e.g., fatigue, dyspnea, chest pain, cough), psychological (e.g., anxiety, depression, post-traumatic stress disorder), and neurocognitive symptoms (e.g., impaired memory and concentration) can appear and last for weeks or months in patients after acute COVID- 19, impeding their ability to function normally and costing the U.S. economy billions of dollars annually in medical bills and lost incomes. However, little is known about the post-acute sequelae of COVID-19, the extent and causes of its lingering health issues, which patients might develop them, and how to address them. We seek to leverage electronic health records (EHRs) data from 7 hospital systems across the U.S. to develop and validate a novel framework for studying evolving temporal phenotypes of the post-acute sequelae of COVID-19. For a period of four years after each patient’s SARS-CoV-2 infection, we will track their clinical data, including clinical notes and laboratory tests recorded in EHR notes to Curate validated cohorts with gold-standard, rule-based, and silver-standard (computationally interpolated) labels for PASC phenotypes (Aim 1). We will utilize these cohorts to develop and validate consistent and interpretable cohort identification and risk models of PASC phenotypes, accounting for temporal ordering and progression of evolving phenotypes over time (Aim 2). Finally, we will evaluate the generalizability the PASC models to develop a framework for modeling evolving temporal phenotypes with EHR data through an objective methodology for evaluating bias in medical AI. (Aim 3). This study will yield new knowledge regarding the phenotypic characteristics of the post-acute effects following known SARS-CoV-2 infection and the underlying drivers that influence their presentation and onset. The novel framework for studying evolving PASC phenotypes will capture and characterize new PASC problems that may present 2-3 years post-acute infection and update risk models. Given uncertainties around the efficacy of current vaccines against future mutations, the proposed learning systems will improve our capacity for adaptive pandemic decision-making. Finally, the framework and underlying methodology developed in this study will lend insights towards understanding persistent sequelae of other known/suspected viral infections and modeling other evolving temporal phenotypes.

项目摘要/摘要与SARS-COV-2（导致Covid-19的冠状病毒病毒）与SARS-COV-2的斗争正在加强疫苗接种和疗法。然而，越来越紧迫地研究和解决大流行的另一端，这是一个变形的副产品被称为covid-19（PASC）的急性后遗症，以及其他名称。即使数百万已成功接种该病毒，还有更多的可能被感染，数百种其中成千上万的（如果不是数百万），从COVID-19感染的急性阶段恢复将是艰苦的衰弱的第二幕。持续的身体（例如疲劳，呼吸困难，胸痛，咳嗽）的集合，心理学（例如焦虑，抑郁，创伤后应激障碍）和神经认知症状（例如，急性共卷后，患者可能会出现并持续数周或数月的记忆和浓度受损） 19，阻碍他们正常运作的能力，每年在医疗中损失数十亿美元的经济账单和损失的收入。但是，关于covid-19的急性后遗症，程度和程度和其挥之不去的健康问题的原因，患者可能会发展出来以及如何解决这些问题。我们寻求利用来自美国7个医院系统的电子健康记录（EHRS）数据来开发和验证研究Covid-19的急性后遗症不断发展的临时表型的新型框架。对于每个患者的SARS-COV-2感染后四年，我们将跟踪他们的临床数据，包括临床 EHR注释中记录的注释和实验室测试，以策划经过金标准的基于规则的金标准，以及用于PASC表型的银标准（计算插值）标签（AIM 1）。我们将利用这些共同开发和验证PASC的一致且可解释的队列识别和风险模型的队列表型，随着时间的流逝，临时排序和进化表型的进展（AIM 2）。最后，我们将评估PASC模型的普遍性，以开发建模临时性的框架具有EHR数据的表型通过客观方法来评估医学AI的偏差。（目标3）。这研究将产生有关已知后急性后作用的表型特征的新知识 SARS-COV-2感染以及影响其表现和发作的潜在驱动因素。小说研究不断发展的PASC表型的框架将捕获并表征可能的新PASC问题目前急性感染后2 - 3年更新风险模型。鉴于围绕当前效率的不确定性反对未来突变的疫苗，拟议的学习系统将提高我们的适应性能力大流行决策。最后，本研究中开发的框架和基础方法将借出了解其他已知/怀疑的病毒感染的持续后遗症的见解，并建模其他不断发展的临时表型。