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COVID-19 disease course analysis using multi-site large-scale EHR data

使用多站点大规模 EHR 数据进行 COVID-19 病程分析

基本信息

批准号：
10380682
负责人：
XIA NING
金额：
$ 17.09万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10380682
关键词：
Address Age Ageusia Algorithmic Software Algorithms Artificial Intelligence COVID-19 COVID-19 pandemic COVID-19 patient Cardiovascular Diseases Caring Case Study Centers for Disease Control and Prevention (U.S.)Chills Clinical Clinical Course of Disease Clinical Data Cluster Analysis Communicable Diseases Computer Analysis Coronavirus Coughing Country Critical Care Data Data Collection Data Set Diabetes Mellitus Diagnosis Disadvantaged Disease Disease Management Disease Progression Dyspnea Electronic Health Record Fever Functional disorder Geographic Locations Goals Grouping Guidelines Headache Health Hypoxia Image Indiana Interdisciplinary Study Kidney Diseases Knowledge Lung Medical center Methods Modeling Myalgia Ohio Outcome Patient Care Patients Phenotype Physicians Play Pneumonia Postdoctoral Fellow Public Health Publishing Research Research Personnel Respiratory Failure Role SARS-CoV-2 infection Salvelinus Severities Severity of illness Shock Shortness of Breath Signs and Symptoms Site Smell Perception Source Symptoms System Time Treatment outcome Universities Work base clinical effect clinical practice cohort comorbidity coronavirus disease disease transmission electronic data health economics improved innovation novel pandemic disease patient population respiratory response social symptomatology tool

项目摘要

Project Summary/Abstract Since its ﬁrst case reported in December 2019, the coronavirus disease-2019 (COVID-19) has caused a pan- demic in 188 countries/regions, and has precipitated an unprecedented health, economic and social crisis. In order to cope with the volatile dynamic and severity of the pandemic, it is imperative that we characterize the various clinical courses of COVID-19 infection, and determine whether and how demographic, clinical and other variables inﬂuence them. Knowledge of the disease's transmission, symptomatology, clinical course, treatment and outcomes is rapidly evolving based on many sources. An important source for advancing this knowledge is data from electronic health records (EHR) and health information exchanges (HIE) because they can pro- vide a real-time, unvarnished view of the disease. Using large-scale, well-integrated and rich EHR data enables comprehensive proﬁling and quantiﬁcation of the COVID-19 disease course that can directly inform clinical prac- tice. The long-term goal of our research is to develop Artiﬁcial Intelligence (AI) tools to facilitate access to and analysis of clinical data. The goal of this application is to develop effective algorithms and tools to mine clinical data to categorize disease courses of COVID-19, and determine the effect of clinical and other variables asso- ciated with them. We will develop our algorithms using data from a large and comprehensive health information exchange, the Indiana Network for Patient Care (INPC), which has about 40,000 COVID-19 patients and fairly complete EHR data about them. We will evaluate the algorithms against other data sets, including EHR data from the OSU Wexner Medical Center and the National COVID Cohort Collaborative (N3C). The speciﬁc aims of this project are to (1) develop COVID-19 disease course groupings, (2) relate comorbidities and other clinical variables to the COVID-19 disease course, and (3) validate the developed algorithms on N3C data. This pro- posal is signiﬁcant because the methods developed in this project have the potential to signiﬁcantly increase our capability for computational analysis of large and rich patient data during the pandemic and beyond; the knowl- edge derived from our comprehensive proﬁling of COVID-19 courses over large, inclusive patient populations supported by rich EHR data can positively impact clinical practice; and the tools developed in this project will be released to the public as a free COVID-19 research re- source. It is innovative because our methods integrate novel methods such as patient clustering using clinical variables and disease progression trajectories, and pa- tient trajectory comparison, with established univariate and predictive analysis; our primary approach will lever- age the oldest and one of the country's largest HIEs to derive detailed and comprehensive knowledge about a large patient population; and the strong preliminary data generated by this project can help improve COVID-19 patient phenotyping, disease characterization and diagnosis.

项目概要/摘要自 2019 年 12 月报告第一例病例以来，2019 年冠状病毒病 (COVID-19) 已引起广泛关注疫情在 188 个国家/地区爆发，并引发了前所未有的健康、经济和社会危机。在为了应对这一流行病的不稳定动态和严重性，我们必须确定这一流行病的特征 COVID-19 感染的各种临床过程，并确定人口统计、临床和其他方面是否以及如何变量会影响它们。了解疾病的传播、症状、临床过程、治疗其结果正在根据多种来源迅速演变。推进这方面知识的重要来源是来自电子健康记录（EHR）和健康信息交换（HIE）的数据，因为它们可以促进提供疾病的实时、真实的视图。使用大规模、集成良好且丰富的 EHR 数据可以对 COVID-19 病程进行全面分析和量化，可以直接为临床实践提供信息泰斯。我们研究的长期目标是开发人工智能 (AI) 工具，以方便访问和临床数据分析。该应用程序的目标是开发有效的算法和工具来挖掘临床数据数据对 COVID-19 的病程进行分类，并确定临床和其他变量的影响与他们交谈。我们将使用来自大量综合健康信息的数据来开发我们的算法印第安纳州患者护理网络 (INNPC) 拥有约 40,000 名 COVID-19 患者，有关他们的完整 EHR 数据。我们将根据其他数据集（包括 EHR 数据）评估算法来自 OSU Wexner 医疗中心和国家新冠肺炎队列协作组织 (N3C)。具体目标该项目的目标是 (1) 制定 COVID-19 病程分组，(2) 将合并症和其他临床疾病联系起来 COVID-19 病程的变量，以及 (3) 在 N3C 数据上验证开发的算法。这个亲 posal 很重要，因为该项目中开发的方法有可能显着提高我们的在大流行期间及之后对大量丰富的患者数据进行计算分析的能力；知识- 优势源自我们对大量包容性患者群体的 COVID-19 课程的全面分析丰富的 EHR 数据支持可以对临床实践产生积极影响；该项目中开发的工具将是作为免费的 COVID-19 研究资源向公众发布。它是创新的，因为我们的方法整合了新方法，例如使用临床变量和疾病进展轨迹进行患者聚类，以及通过已建立的单变量和预测分析进行tient轨迹比较；我们的主要方法将利用—— 年龄最大、全国最大的 HIE 之一，以获得有关某个领域的详细而全面的知识患者群体庞大；该项目生成的强有力的初步数据可以帮助改善 COVID-19 患者表型、疾病特征和诊断。