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.

项目概要/摘要随着疫苗接种和疫苗接种的开展，针对 SARS-CoV-2（导致 COVID-19 的冠状病毒）的斗争正在加强。然而，研究和解决这一流行病的另一面（一种正在发生变化的疾病）的治疗紧迫性越来越大副产品被称为 COVID-19 急性后遗症（PASC）等，即使有数百万。尽管已经成功接种了病毒疫苗，但仍有更多人可能被感染，并且数百人对于成千上万（如果不是数百万）的人来说，从 COVID-19 感染的急性期恢复将是一项艰巨的任务持续的身体虚弱症状（例如疲劳、呼吸困难、胸痛、咳嗽），心理（例如焦虑、抑郁、创伤后应激障碍）和神经认知症状（例如急性新冠肺炎后患者的记忆力和注意力受损可能会出现并持续数周或数月。 19 阻碍了他们正常运作的能力，每年给美国经济造成数十亿美元的医疗损失然而，人们对 COVID-19 的急性后遗症、程度和收入知之甚少。造成其挥之不去的健康问题的原因、哪些患者可能会出现这些问题以及如何解决这些问题。利用来自美国 7 个医院系统的电子健康记录 (EHR) 数据来开发和验证用于研究 COVID-19 急性后遗症演变的时间表型的新框架。在每位患者感染 SARS-CoV-2 后的四年内，我们将跟踪他们的临床数据，包括临床数据 EHR 注释中记录的注释和实验室测试可通过黄金标准、基于规则、以及 PASC 表型的银标准（计算插值）标签（目标 1）。队列开发和验证一致且可解释的队列识别和 PASC 风险模型表型，解释表型随时间演变的时间顺序和进展（目标 2）。我们将评估 PASC 模型的普遍性，以开发一个用于建模演化时间的框架通过评估医学人工智能偏差的客观方法来评估 EHR 数据的表型（目标 3）。研究将产生关于已知的急性后效应表型特征的新知识 SARS-CoV-2 感染以及影响其表现和发病的潜在驱动因素。用于研究不断演变的 PASC 表型的框架将捕捉并表征新的 PASC 问题，这些问题可能鉴于当前疗效的不确定性，呈现急性感染后 2-3 年的情况并更新风险模型。针对未来突变的疫苗，所提出的学习系统将提高我们的适应能力最后，本研究开发的框架和基本方法将有助于大流行决策。深入了解其他已知/疑似病毒感染的持续后遗症，并对其他病毒感染进行建模进化的时间表型。