Data-Driven Identification of the Acute Respiratory Distress Syndrome

数据驱动的急性呼吸窘迫综合征识别

• 批准号: 9292908
• 负责人: Michael William Sjoding
• 金额: $ 17.24万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9292908
• 关键词: Acute; Address; Adult Respiratory Distress Syndrome; Affect; Biometry; Caring; Clinical; Clinical Informatics; Clinical Research; Clinical Trials; Clinical Trials Network; Complement; Complex; Critical Illness; Data; Data Science; Development; Diagnosis; Diagnostic; Doctor of Philosophy; Educational Status; Electronic Health Record; Enrollment; Ensure; Environment; Epidemiology; Evidence based practice; Evidence based treatment; Future; Goals; Hour; Individual; Intensive Care Units; Investigation; Knowledge; Laboratories; Life; Lung; Lung diseases; Machine Learning; Mentored Research Scientist Development Award; Mentors; Methods; Michigan; Modeling; Monitor; National Heart, Lung, and Blood Institute; Natural History; Natural Language Processing; Participant; Patient Care; Patient risk; Patients; Performance; Physicians; Positioning Attribute; Prevention trial; Process; Real-Time Systems; Records; Recruitment Activity; Reference Standards; Research; Research Design; Research Infrastructure; Research Personnel; Research Project Grants; Resolution; Retrospective cohort; Retrospective cohort study; Risk; Risk Estimate; Risk Factors; Savings; Specificity; Statistical Models; Syndrome; System; Techniques; Testing; Text; Time; Training; Universities; Validation; Work; base; big biomedical data; career; clinical phenotype; clinical practice; cohort; computer based statistical methods; design; digital; electronic data; evidence base; health data; high risk; human disease; improved; insight; learning strategy; meetings; monitoring device; mortality; novel; novel strategies; novel therapeutics; phenotypic data; predictive modeling; prevent; prospective; respiratory health

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.

项目总结/摘要 这K 01提案将完成迈克尔Sjoding，医学博士，理学硕士的培训，对他的长期职业目标， 改善急性呼吸道疾病患者的护理。Sjoding博士是一名肺部和重症医师， 密歇根大学，接受过临床研究设计和生物统计学硕士培训。这项建议 建立在Sjoding博士先前的专业知识基础上，为数据科学的额外培训提供了受保护的时间， 从富人的“生物医学大数据”中获取人类疾病新知识的技术方法 密歇根大学的培训环境。该项目的研究目标是开发实时系统 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是一种严重的疾病综合征，每年影响20万人， mortality.对该综合征认识不足是为患者提供循证护理的关键障碍 关于ARDS该研究将在主要导师西奥多J. Iwashyna，MD的指导下完成， 博士和共同导师Timothy P.霍费尔，医学博士，理学硕士和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。五年计划包括教学 课程作业，指导研究和专业发展活动，具有明确的里程碑，以确保 成功过渡到独立。指导研究有两个具体目标： 目标1。开发一种新的系统，用于识别电子健康数据中的ARDS数字签名， 确定符合ARDS标准的患者。 目标二。明确发展为ARDS的早期自然史，以更准确地预测患者未来的ARDS 风险 这两个目标都将采用严格的两部分设计，其中ARDS诊断和预测模型是在 相同的回顾性队列，并在时间上不同的队列中得到验证。在实现这些高层次目标的过程中， 研究将利用高分辨率电子健康记录和监测设备数据来研究 以前所未有的细节，为ARDS流行病学和早期自然史提供了新的见解。 这项工作将建立在至少两个R 01提案的基础上：（1）测试实时电子健康记录的影响- 基于ARDS诊断系统，以改善循证护理实践，（2）使用 深入的临床表型数据。这项工作将建立一个纲领性的研究路线，使用高分辨率 电子健康数据，以提高对重大疾病和呼吸系统疾病的了解。在完成这一 Sjoding博士将在数据科学方法方面获得独特的计算专业知识，以补充他的 以前的培训，然后他可以随时应用到解决呼吸健康的其他研究挑战。 在K 01奖期间提出的雄心勃勃但可行的培训和指导研究将使他能够 他的目标是成为一名独立调查员。