Informatics Approach to Identification and Deep Phenotyping of PASC Cases

项目摘要/摘要 越来越多地有关于持续性症状和多器官多系统表现的报道(例如， 肺、心血管、肾脏和神经系统)从急性期恢复的个体 新冠肺炎的后遗症，称为SARS-CoV-2感染的急性后遗症。鉴于7670万人 截至2022年2月在美国已知已被感染，数百万人可能会经历 PASC。这种预计的疾病负担将对患者的临床健康产生深远的影响 后新冠肺炎护理期间的结果和美国卫生系统。及时识别患有PASC的个人 现有的新冠肺炎队列和新发现的新冠肺炎患者迫切需要用于PASC诊所和 PASC的纵向队列研究。在生物医学信息学方法学的基础上，我们提出了一个高度- 吞吐量和半监督深度表型方法识别PASC和PASC患者 来描述它们的表型。我们的方法是基于基于 关于南卡罗来纳州新冠肺炎队列(S3C)，由国家过敏和传染病研究所资助 疾病(NIAID)(R01A127203-4S1)。S3C(截至2022年2月，约1,400,000名新冠肺炎患者)是 多模式数据存储库，包括电子病历、卫生系统数据、基于社区的卫生服务数据和 索赔数据，每个数据在个人层面上的完整时间轨迹。建立在图形的顶部 模型中，我们将使用非监督聚类算法检测候选PASC患者的表型。我们 然后，通过将PASC病例和相应的表型 临床评估和监督算法。这项研究将导致高通量算法的应用 用于识别和描述新冠肺炎电子病历队列中的PASC病例。由此产生的EHR和机器 学习模型是可解释的、可概括的，并将形成测试和实现的基础 全州和全国的COVID后诊所/计划。

PROJECT SUMMARY/ABSTRACT Increasingly there have been reports of persistent symptoms and multi-organ multi-system manifestations (e.g., pulmonary, cardiovascular, renal, and neurological) among individuals who were recovered from the acute phase of COVID-19, denoted as Post-Acute Sequela of SARS-CoV-2 infection (PASC). Given that 76.7 million people are known to have been infected in the US as of February of 2022, millions of people will potentially experience PASC. This projected disease burden will have a profound public health impact with respect to patients' clinical outcomes and US health systems during post-COVID-19 care. Timely identification of individuals with PASC from existing COVID-19 cohorts and newly identified COVID-19 patients is urgently needed for PASC clinics and longitudinal cohort studies on PASC. Building on biomedical informatics methodologies, we propose a high- throughput and semi-supervised Deep Phenotyping approach to identifying individuals with PASC and characterizing their phenotypes. Our approach is based on a Graph representational model constructed based on the South Carolina COVID-19 Cohort (S3C), funded by the National Institute of Allergy and Infectious Diseases (NIAID) (R01A127203-4S1). S3C (n=~1,400, 000 COVID-19 patients by the February of 2022) is a multi-modal data repository consisting of EHR, health systems data, community-based health services data, and claims data, with complete temporal trajectory of every datum at individual-level. Building on top of the Graph model, we will detect phenotypes of candidate PASC patients by using unsupervised clustering algorithms. We will then identify and validate clinically plausible PASC cases and corresponding phenotypes by incorporating clinical evaluation and supervised algorithms. This study will result in a high-throughput algorithm application for identifying and characterizing PASC cases from COVID-19 EHR cohorts. The resulted EHR and machine learning models are interpretable, generalizable, and will form a foundation for testing and implementing in state-wide and national post-COVID clinics/programs.