Data-Driven Identification of the Acute Respiratory Distress Syndrome

项目摘要/摘要 这份K01计划将完成Michael Sjoding，医学博士，硕士的培训，朝着他的长期职业目标 改善对急性呼吸道疾病患者的护理。Sjoding博士是一名肺部和危重医生， 密歇根大学在临床研究设计和生物统计学方面接受硕士水平的培训。这项建议 以Sjoding博士以前的专业知识为基础，为数据科学方面的额外培训提供了受保护的时间 从丰富的“生物医学大数据”中获取人类疾病新知识的技术方法 密歇根大学的训练环境。该项目的研究目标是开发实时系统 为提高急性呼吸窘迫综合征(ARDS)诊断的准确性和及时性 电子健康记录数据。ARDS是一种危重疾病综合征，每年影响20万人，并伴有高 死亡率。对这种综合征认识不足是向患者提供循证护理的关键障碍 和ARDS一起。这项研究将在西奥多·J·伊瓦希纳医学博士的指导下完成。 博士和共同导师Timothy P.Hofer，医学博士，硕士和Kayvan Najarian博士，以及科学顾问委员会 在数据科学和应用临床信息学方面拥有额外的专业知识。五年计划包括说教 课程作业、有指导的研究和专业发展活动，有明确的里程碑以确保 成功地过渡到独立。指导研究有两个具体目标： 目的1.开发一种新的系统来识别电子健康数据中的ARDS数字签名，以准确地 确定符合ARDS标准的患者。 目的2.明确ARDS发生的早期自然病史，以便更准确地预测患者未来的ARDS 风险。 这两个目标都将使用严格的两部分设计，ARDS诊断和预测模型在 相同的回顾队列，并在时间上不同的队列中得到验证。在完成这些高级别目标的过程中， 研究将利用高分辨率电子健康记录和旁边的监测设备数据来研究 提供了对ARDS流行病学和早期自然历史的新见解。 这项工作将建立在至少两个R01建议：(1)测试实时电子健康记录的影响- 基于ARDS诊断系统以改进循证护理实践，(2)定义ARDS亚型 深厚的临床表型数据。这项工作将朝着使用高分辨率的程序性研究路线发展 电子健康数据，以提高对危重疾病和呼吸道疾病的了解。在完成这项工作时 提议，Sjoding博士将在数据科学方法方面获得独特的计算专业知识，补充他的 以前的培训，然后他就可以很容易地应用于解决呼吸健康方面的其他研究挑战。 在K01奖项期间提出的雄心勃勃但可行的培训和有指导的研究将使他能够 实现他成为独立调查员的目标。

PROJECT SUMMARY/ABSTRACT This K01 proposal will complete Michael Sjoding, MD, MSc's training towards his long-term career goal of improving care of patients with acute respiratory disease. Dr. Sjoding is a Pulmonary and Critical Physician at the University of Michigan with master's level training in clinical study design and biostatistics. This proposal builds on Dr. Sjoding's prior expertise, providing protected time for additional training in data science, the technical methods for deriving new knowledge about human disease from “Big Biomedical Data” in the rich training environment at the University of Michigan. The project's research goal is to develop real-time systems to improve accuracy and timeliness of Acute Respiratory Distress Syndrome (ARDS) diagnosis using electronic health record data. ARDS is a critical illness syndrome affecting 200,000 people each year with high mortality. Under-recognition of this syndrome is the key barrier to providing evidence-based care to patients with ARDS. The research will be completed under the guidance of primary mentor Theodore J. Iwashyna, MD, PhD and co-mentors Timothy P. Hofer, MD, MSc, and Kayvan Najarian, PhD, and a scientific advisory board with additional expertise in data science and applied clinical informatics. The 5-year plan includes didactic coursework, mentored research, and professional development activities, with defined milestones to ensure successful transition to independence. The mentored research has 2 specific Aims: Aim 1. Develop a novel system for identifying ARDS digital signatures in electronic health data to accurately identify patients meeting ARDS criteria. Aim 2. Define the early natural history of developing ARDS, to more accurately predict patients' future ARDS risk. Both Aims will utilize rigorous 2-part designs, with the ARDS diagnostic and prediction models developed in the same retrospective cohort and validated in temporally distinct cohorts. In completing these high-level aims, the research will leverage high-resolution electronic health record and beside-monitoring device data to study ARDS with unprecedented detail, providing new insights into ARDS epidemiology and early natural history. This work will build to at least two R01 proposals: (1) testing the impact of a real-time electronic health record- based ARDS diagnostic system to improve evidence-based care practice, (2) defining ARDS subtypes using deep clinical phenotypic data. The work will build toward a programmatic line of research using high-resolution electronic health data to improve understanding of critical illness and respiratory disease. In completing this proposal, Dr. Sjoding will acquire unique computational expertise in data science methods, complementing his previous training, which he can then readily apply to address other research challenges in respiratory health. The ambitious but feasible training and mentored research proposed during this K01 award will allow him to achieve his goal of becoming an independent investigator.