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

基本信息

批准号：
9908166
负责人：
Michael William Sjoding
金额：
$ 17.27万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
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/9908166
关键词：
Acute Address Adult Respiratory Distress Syndrome Affect Bayesian Network 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 Infrastructure 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 Patients Performance Physicians Positioning Attribute Prevention trial Process Real-Time Systems Records Reference Standards Research Research Design 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 design digital electronic data evidence base health data high risk human disease improved insight machine learning method meetings monitoring device mortality novel novel strategies novel therapeutics phenotypic data predictive modeling prevent prospective recruit respiratory health risk prediction model statistical learning

项目摘要

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.

项目概要/摘要该 K01 提案将完成 Michael Sjoding 医学博士、理学硕士的培训，以实现他的长期职业目标： Sjoding 博士是一名肺科和重症医师，致力于改善急性呼吸道疾病患者的护理。密歇根大学提供临床研究设计和生物统计学硕士学位培训。以 Sjoding 博士先前的专业知识为基础，为数据科学方面的额外培训提供受保护的时间，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 ARDS 是一种严重疾病综合症，每年影响 20 万人。对这种综合征的认识不足是向患者提供循证护理的主要障碍。该研究将在主要导师 Theodore J. Iwashyna 医学博士的指导下完成。博士和共同导师 Timothy P. Hofer（医学博士、理学硕士）和 Kayvan Najarian（博士）以及科学顾问委员会拥有数据科学和应用临床信息学方面的额外专业知识。五年计划包括教学。课程作业、指导研究和专业发展活动，并具有明确的里程碑，以确保成功过渡到独立。指导研究有两个具体目标：目标 1. 开发一种新颖的系统，用于识别电子健康数据中的 ARDS 数字签名，以准确地识别 ARDS 数字签名。识别符合 ARDS 标准的患者。目标 2. 定义发生 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在同一回顾性队列中，并在时间上不同的队列中进行验证，以完成这些高级目标。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进行研究 ARDS 具有前所未有的详细信息，为 ARDS 流行病学和早期自然史提供了新的见解。这项工作将构建至少两个 R01 提案：(1) 测试实时电子健康记录的影响 - 基于 ARDS 诊断系统，以改善循证护理实践，(2) 使用定义 ARDS 亚型这项工作将建立一个使用高分辨率的程序化研究路线。电子健康数据，以提高对危重疾病和呼吸道疾病的了解。根据提案，Sjoding 博士将获得数据科学方法方面独特的计算专业知识，补充他的他可以轻松地将其应用于解决呼吸健康方面的其他研究挑战。 K01 奖项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实现了成为一名独立调查员的目标。