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.

项目摘要/摘要 该K01提案将完成MSC的迈克尔·萨德林（Michael Sjoding），这是MSC的培训，以实现他的长期职业目标 改善Acciratory疾病的患者。 密歇根大学接受了临床研究设计和生物统计学的硕士培训 建立在Sjoding博士先前的专业知识的基础上，为数据科学的额外培训提供了受保护的时间。 从富人的“大生物医学数据”中获取有关人类疾病的新知识的技术方法。 密歇根大学的培训环境。 提高急性呼吸窘迫综合征（ARDS）诊断的准确性和及时性 电子记录数据是一种危重的疾病综合症，每个人都有200,000人。 死亡率不足。 与ARDS。 博士学位和联合官员蒂莫西·霍弗（Timothy P. 具有数据科学和应用临床信息学方面的其他专业知识。 课程工作，指导研究和专业发展活动，并具有确定的里程碑 成功过渡到独立性。 目标1。开发一种新型系统，用于识别电子健康中的ARDS数字特征以准确 确定符合ARDS标准的患者。 目标2。定义开发ARDS的性质历史，以更准确地预测患者的未来ARDS 风险。 这两个目标都将利用严格的2部分设计，并在ARDS诊断模型中开发了ARDS诊断模型 相同的回顾性队列和有效在暂时的队列中有效。 使用杠杆高分辨率电子健康记录和监控设备数据进行研究的研究 具有前所未有的细节的Ards，为ARDS流行病学和早期自然历史提供了新的。 这项工作将至少提交两个R01提案：（1）测试实时电子健康记录的影响 - 基于ARDS诊断系统以改善基于证据的护理实践，（2）使用 深度临床表型数据。 电子健康数据，以提高对临界和呼吸系统疾病的理解 提案，Sjoding博士将获得数据科学方法中独特的计算专业知识，并补充他的 以前的培训，他可以将其重新应用于呼吸健康中的其他研究挑战。 在此K01奖中，他的良好但可行的培训和指导的研究指导了他 实现他成为独立调查员的目标。