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

项目概要/摘要 抗击SARS-CoV-2（导致COVID-19的冠状病毒）的斗争正在通过疫苗接种和 治疗学然而，研究和解决这一流行病的另一面--形态转变--的紧迫性越来越大。 副产品被称为COVID-19的急性后遗症（PASC），以及其他名称。即使几百万 成功接种了抗病毒疫苗，但仍有更多的人可能被感染， 成千上万（如果不是数百万）的人，从COVID-19感染的急性期恢复将是艰苦的， 削弱第二幕。一组持久的物理（例如，疲劳、呼吸困难、胸痛、咳嗽）， 心理的（例如，焦虑、抑郁、创伤后应激障碍），和神经认知症状（例如， 记忆力和注意力受损）可在急性COVID-19后出现并持续数周或数月， 19，阻碍了他们正常运作的能力，并使美国经济每年花费数十亿美元用于医疗保健。 账单和收入损失。然而，人们对COVID-19的急性后后遗症、其程度和 它挥之不去的健康问题的原因，哪些患者可能发展他们，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（EHR）数据， 研究COVID-19急性期后后遗症的演变时间表型的新框架。用于 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 笔记和实验室测试记录在EHR笔记，以策展验证队列与金标准，基于规则， 和PASC表型的银标准（计算内插）标记（Aim 1）。我们将利用这些 队列，以开发和验证一致且可解释的PASC队列识别和风险模型 表型，占时间顺序和进展的演变表型随着时间的推移（目的2）。最后， 我们将评估PASC模型的通用性，以开发一个用于建模演化时态的框架。 通过客观方法将表型与EHR数据结合起来，评估医疗人工智能的偏见。(Aim 3）。这 这项研究将产生新的知识，关于表型特征的急性后的影响，以下已知的 SARS-CoV-2感染和影响其表现和发病的潜在驱动因素。小说 研究PASC表型演变的框架将捕获和表征新的PASC问题， 介绍急性感染后2-3年的情况，并更新风险模型。鉴于目前的有效性存在不确定性， 针对未来突变的疫苗，拟议的学习系统将提高我们的适应能力， 流行病决策。最后，本研究中开发的框架和基本方法将有助于 对理解其他已知/疑似病毒感染的持续后遗症和建模其他 进化时间表型

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.