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

A.项目摘要和摘要 NRSA博士后奖学金计划的目标是：1)促进Catherine Gao博士的发展 作为一名独立的内科科学家和处理、集成和计算分析方面的专家 复杂的数据集，以及2)对患者在ICU期间的离散临床状态之间的转换进行建模 患有SARS-CoV-2肺炎。 该建议利用了作为成功的临床响应的一部分而生成的独特数据集 肺炎治疗(SCRIPT)系统生物学中心由候选人的主要赞助人文德里克博士领导。 脚本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判断。 利用这些数据，在目标1中，申请人将使用机器学习方法对DISTINCT进行分类和建模 在ICU住院期间的临床状态。在目标2中，候选人将识别与以下内容相关的特征 过渡到有利或不利的临床状态，特别关注特定药物的管理 药物与呼吸机相关性肺炎的发展。这些数据将进一步告知其他 脚本中的核心，以优化高分辨率但稀少可用的多组数据。 候选人和她的导师利用脚本提供的独特研究环境设计了一个 根据应聘者的具体需求和目标制定详细的培训计划。该计划包括一项严谨的研究 为成功的职业生涯奠定基础的组成部分：1)正式的课程设计(包括硕士学位 健康和生物医学信息学)学习计算技能以管理大型电子病历 数据集和分析多体数据，2)通过研究计划和来自 多学科导师团队成为一名独立的内科科学家。

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.