Informatics Approach to Identification and Deep Phenotyping of PASC Cases

PASC 病例识别和深度表型分析的信息学方法

基本信息

批准号：
10574753
负责人：
Xiaoming Li
金额：
$ 21.79万
依托单位：
UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
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：
2022-09-06 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10574753
关键词：
Acute Address Algorithms Back Biological Markers COVID-19 COVID-19 patient Cardiovascular system Caring Chest Pain China Clinic Clinical Clinical Data Clinical Trials Design Collection Community Health Data Data Reporting Data Science Data Sources Disease Dyspnea Electronic Health Record Epidemiology Europe Event Fatigue Foundations Funding Gold Graph Guidelines Health Health Services Health Status Health system Heterogeneity Immune Individual Informatics Kidney Link Longevity Longitudinal cohort Longitudinal cohort study Lung Machine Learning Manuals Mediating Methodology Mining Modeling Morphology National Institute of Allergy and Infectious Disease Natural Language Processing Neurologic Observational Study Outcome Palpitations Patients Persons Phase Phenotype Physiological Post-Acute Sequelae of SARS-CoV-2 Infection Public Health Reaction Time Records Recovery Reporting Research Risk Factors SARS-CoV-2 infection Semantics Social Behavior South Carolina Structure Supervision Symptoms Testing Therapeutic Time United States National Institutes of Health acute infection base biomedical informatics biomedical ontology burden of illness clinical care cohort data repository evidence base experience health record improved individual response machine learning method machine learning model multimodal data outcome prediction persistent symptom phenotyping algorithm post-COVID-19 preventive intervention programs research clinical testing supervised learning symptom cluster trait treatment response unstructured data

项目摘要

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.

项目概要/摘要越来越多关于持续症状和多器官多系统表现的报道（例如，从急性期恢复的个体中的肺部、心血管、肾脏和神经系统） COVID-19，表示为 SARS-CoV-2 感染的急性后遗症 (PASC)。鉴于 7670 万人截至 2022 年 2 月，已知在美国已被感染，数百万人可能会经历帕斯卡。这种预计的疾病负担将对患者的临床症状产生深远的公共卫生影响 COVID-19 后护理期间的结果和美国卫生系统。及时识别 PASC 患者 PASC 诊所迫切需要从现有的 COVID-19 队列和新发现的 COVID-19 患者中获取 PASC 的纵向队列研究。基于生物医学信息学方法，我们提出了一种高吞吐量和半监督深度表型方法来识别具有 PASC 和表征他们的表型。我们的方法基于构建的图表示模型南卡罗来纳州 COVID-19 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，n=~1,400, 000 名 COVID-19 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，包含个人层面每个数据的完整时间轨迹。建立在图表之上模型中，我们将使用无监督聚类算法检测候选 PASC 患者的表型。我们然后将通过合并来识别和验证临床上合理的 PASC 病例和相应的表型临床评估和监督算法。这项研究将带来高通量算法应用用于识别和描述来自 COVID-19 EHR 队列的 PASC 病例。由此产生的 EHR 和机器学习模型是可解释的、可推广的，并将成为测试和实施的基础全州和国家新冠疫情后诊所/项目。