Transitions Among Discrete Clinical States During ICU Stays in Patients with SARS-CoV-2 Pneumonia

SARS-CoV-2 肺炎患者入住 ICU 期间离散临床状态的转变

• 批准号: 10537554
• 负责人: Catherine A. Gao
• 金额: $ 8.56万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10537554
• 关键词: 2019-nCoV; Address; Adoption; Adrenal Cortex Hormones; Antibiotic Therapy; Bronchoalveolar Lavage; COVID-19 pandemic; COVID-19 patient; COVID-19 pneumonia; Cessation of life; Clinical; Clinical Data; Clinical Medicine; Complex; Computer Analysis; Computerized Medical Record; Critical Care; Critical Illness; Data; Data Science; Data Set; Databases; Development; Disease; Electronic Health Record; Enrollment; Environment; Feedback; Fellowship; Foundations; Future; Generations; Goals; Intensive Care; Intensive Care Units; Interleukin 6 Receptor; Intervention; Laboratories; Learning; Machine Learning; Mechanical ventilation; Medical; Mentored Patient-Oriented Research Career Development Award; Mentors; Microbiology; Modeling; Morbidity - disease rate; Multiomic Data; National Institute of Allergy and Infectious Disease; National Research Service Awards; Outcome; Patients; Pharmacologic Substance; Physicians; Pneumonia; Procedures; Public Health; Public Health Informatics; Publications; Research; Resolution; Sampling; Scientist; Systems Biology; Techniques; Testing; Training; United States; United States National Institutes of Health; Vaccines; Virus; Visualization; Work; adjudication; antagonist; autoencoder; betacoronavirus; biomedical informatics; career; clinical predictors; cohort; coronavirus disease; design; high dimensionality; immunomodulatory therapies; improved; individual patient; insight; machine learning algorithm; medication administration; mortality; multidisciplinary; multiple omics; pandemic disease; pathogenic bacteria; pathogenic virus; patient subsets; pneumonia treatment; recurrent neural network; response; severe COVID-19; skills; tool; ventilator-associated pneumonia

A. Project Summary and Abstract The goals of this NRSA postdoctoral fellowship proposal are: 1) to facilitate Dr. Catherine Gao’s development as an independent physician-scientist and an expert in the handling, integration, and computational analyses of complex datasets, and 2) to model transitions between discrete clinical states during the ICU stays of patients with SARS-CoV-2 pneumonia. This proposal takes advantage of a unique dataset generated as part of the Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center led by Dr. Wunderink, the candidate's primary sponsor. SCRIPT contains the electronic health record data, as well as rich expert clinician adjudication of outcomes. Leveraging those data, in Aim 1, the applicant will use machine learning approaches to cluster and model distinct clinical states over the course of ICU stays. In Aim 2, the candidate will identify features associated with transitions towards favorable or unfavorable clinical states, looking specifically at the administration of specific pharmaceuticals and the development of ventilator associated pneumonia. These data will further inform other cores within SCRIPT to optimize the high resolution but sparsely available multiomic data. The candidate and her mentors have used the unique research environment provided by SCRIPT to design a detailed training plan tailored to the candidate’s specific needs and goals. The plan includes a rigorous research component that lays the foundation for a successful career: 1) formalized coursework (including a Master’s in Health and Biomedical Informatics) to learn computational skills to manage large electronic medical record datasets and analyze multiomic data, 2) hands-on training through research plan and feedback from a multidisciplinary team of mentors to become an independent physician-scientist.

A. 项目概要和摘要 NRSA博士后奖学金提案的目标是：1）促进Catherine Gau博士的发展 作为一名独立的医师科学家和处理、整合和计算分析方面的专家 复杂的数据集，2) 对患者入住 ICU 期间离散临床状态之间的转换进行建模 患有 SARS-CoV-2 肺炎。 该提案利用了作为成功临床反应的一部分生成的独特数据集 肺炎治疗 (SCRIPT) 系统生物学中心由候选人的主要发起人 Wunderink 博士领导。 SCRIPT 包含电子健康记录数据，以及丰富的专家临床医生对结果的裁决。 利用这些数据，在目标 1 中，申请人将使用机器学习方法对不同的数据进行聚类和建模 在 ICU 住院期间的临床状态 在目标 2 中，考生将识别与相关的特征。 向有利或不利临床状态的转变，特别关注特定的管理 这些数据将进一步为其他人提供信息。 SCRIPT 中的核心可优化高分辨率但可用的多组学数据。 候选人和她的导师利用 SCRIPT 提供的独特研究环境设计了一个 根据候选人的具体需求和目标量身定制的详细培训计划 该计划包括严格的研究。 为成功的职业生涯奠定基础的组成部分：1）正式的课程作业（包括硕士学位） 健康和生物医学信息学）学习管理大型电子病历的计算技能 数据集并分析多组学数据，2）通过研究计划和反馈进行实践培训 多学科导师团队成为一名独立的医师科学家。