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

项目概要/摘要 自2019年12月报告首例病例以来，2019冠状病毒病（COVID-19）已引起了广泛的关注， 在188个国家/地区流行，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多变动态和严重性，我们必须将 COVID-19感染的各种临床过程，并确定是否以及如何人口统计学，临床和其他 变量影响它们。了解疾病的传播、生殖病学、临床过程、治疗 和结果是迅速发展的基础上，许多来源。一个重要的来源，为推进这方面的知识 是来自电子健康记录（EHR）和健康信息交换（HIE）的数据，因为它们可以提供 提供了一个实时的、不加修饰的疾病视图。使用大规模、集成良好且丰富的EHR数据， 全面描述和量化COVID-19病程，可直接为临床实践提供信息， Tice。我们研究的长期目标是开发阿尔蒂官方智能（AI）工具，以促进访问和 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，以挖掘临床 对COVID-19病程进行分类的数据，并确定临床和其他变量的影响， 与他们交往。我们将使用来自大型综合健康信息的数据开发我们的算法 交换，印第安纳州病人护理网络（INPC），其中有大约40,000名COVID-19患者， 完整的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医学中心和国家COVID队列协作组织（N3C）。具体目标 该项目的目的是（1）制定COVID-19疾病病程分组，（2）相关合并症和其他临床 变量对COVID-19疾病进程的影响，以及（3）在N3C数据上验证所开发的算法。这个亲- 这是重要的，因为在这个项目中开发的方法有可能显着提高我们的 在大流行期间及以后对大量和丰富的患者数据进行计算分析的能力; 优势来自于我们对COVID-19课程在大型包容性患者人群中的全面描述 由丰富的EHR数据支持，可以积极影响临床实践;本项目开发的工具将 作为免费的COVID-19研究资源向公众发布。它是创新的，因为我们的方法集成了 新的方法，如使用临床变量和疾病进展轨迹的患者聚类，以及 时间轨迹比较，建立单变量和预测分析;我们的主要方法将杠杆- 年龄最大的和全国最大的高等教育机构之一，以获得详细和全面的知识， 庞大的患者群体;该项目产生的强有力的初步数据可以帮助改善COVID-19 患者表型分析、疾病表征和诊断。

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.