Advancing Outcome Metrics in Trauma Surgery Through Utilization of Big Data

通过利用大数据推进创伤手术的结果指标

• 批准号: 9320947
• 负责人: Rachael A Callcut
• 金额: $ 22.18万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9320947
• 关键词: Address; Affect; Age; Algorithms; American; Benchmarking; Big Data; Bioinformatics; Biology; Biometry; Blood coagulation; California; Caring; Cause of Death; Cellular Phone; Cessation of life; Characteristics; Clinical; Clinical Research; Collaborations; Comorbidity; Complex; Complication; Computerized Medical Record; Data; Data Analyses; Data Element; Data Science; Data Set; Data Sources; Deltastab; Detection; Development; Effectiveness; Foundations; Funding; Future; General Hospitals; Geographic Locations; Goals; Growth; High Performance Computing; Hospitalization; Hospitals; Hybrids; Informatics; Information Technology; Injury; Knowledge; Laboratories; Lead; Long-Term Effects; Machine Learning; Medical; Mentored Research Scientist Development Award; Mentors; Metadata; Methods; Modeling; Modernization; Morbidity - disease rate; Myocardial Ischemia; Operative Surgical Procedures; Outcome; Outcome Measure; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Performance; Physiological; Play; Pneumonia; Process; Reporting; Research; Research Personnel; Risk; Risk Factors; San Francisco; Severities; Socioeconomic Status; Solid; Source; Standardization; Statistical Methods; Surgeon; System; Systems Biology; Techniques; Technology; Testing; Time; Training; Translational Research; Trauma; Trauma patient; Traumatic injury; Universities; Work; base; big biomedical data; care systems; career; cloud based; cost; demographics; disability; experience; follow-up; improved; improved outcome; individual patient; injured; modifiable risk; mortality; multidisciplinary; novel; novel strategies; portability; prediction algorithm; professor; public health relevance; trauma care; trauma centers

 DESCRIPTION (provided by applicant) My goal in seeking a K01 Award is to acquire the necessary training to become an independently funded investigator focused on exploiting the power of biomedical Big Data Science to improve outcome following severe injury. I am a trauma surgeon at San Francisco General Hospital, one of the Nation's leading trauma centers, and an Assistant Professor of Surgery at the University of California San Francisco (UCSF). UCSF has recently entered into collaboration with the National Laboratories to study the use of biomedical Big Data in complex clinical conditions and my main mentor, Dr. Mitchell J. Cohen is the lead investigator at UCSF for this collaboration. I believe that given the complexity of the factors that likely affect traum outcome including patient injury patterns, medical co-morbidities, patient biology, and the system of care, trauma provides a solid foundation to study the utility of Big Data Science for solving complex medical questions. To facilitate my growth as an expert in this field, I am proposing to develop a framework for integrating multiple data sources necessary to forecast patient outcomes following trauma. These novel datasets combined with biologic and metadata will then be utilized to create improved metrics that better predict complication risk from modifiable and non-modifiable factors. The net result of this work is a new approach to data ascertainment for measuring outcome, leveraging new data types to improve prediction of patient trajectory, and creating a platform to interface with existing information technology to ultimately be used for an early warning detection system for patients at risk of complications. The future long-term goal of this work would be to identify early patients predicted to do more poorly and then apply refinements to the process of care to minimize complication development. The creation of early warning detection systems has significant theoretic potential to improve quality and ultimately decrease costs. Nearly $30 billion per year in the US is spent on care for the traumatically injured and the development of post-traumatic complications is believed to be major contributor to the overall costs of care. The ability to report performance has been hampered by a lack of standard definitions, reporting bias, access to datasets, and the analysis techniques that fail to account for the highly confounded relationships contributing to patient outcome. This K01 award will provide me with the support necessary to accomplish the following goals: (1) to become an expert in applying biologic big data to trauma care (2) to elucidate the relationship of modifiable factors affecting complication development (3) to gain experience with advanced biostatistical techniques and bioinformatics; and (4) to develop an independent clinical research career. To achieve these goals, I have assembled a multidisciplinary team including Dr. Cohen, a National expert in trauma systems biology and biologic big data, and two co-mentors: Dr. Michael Matthay, a translational research expert in complications after severe illness, and Dr. Alan Hubbard, an expert in advanced biostatistical techniques including biologic big data analysis.

 描述（由申请人提供） 我寻求K 01奖的目标是获得必要的培训，成为一名独立资助的研究人员，专注于利用生物医学大数据科学的力量来改善严重损伤后的结果。我是一名创伤外科医生在旧金山弗朗西斯科总医院，全国领先的创伤中心之一，并在加州大学旧金山弗朗西斯科（UCSF）外科助理教授。UCSF最近与国家实验室合作，研究生物医学大数据在复杂临床条件下的应用，我的主要导师Mitchell J. Cohen博士是UCSF这项合作的首席研究员。我相信，考虑到可能影响创伤结果的因素的复杂性，包括患者损伤模式，医疗合并症，患者生物学和护理系统，创伤为研究大数据科学解决复杂医疗问题的实用性提供了坚实的基础。为了促进我作为这一领域专家的成长，我建议开发一个框架，用于整合预测创伤后患者结局所需的多个数据源。这些新的数据集与生物学和元数据相结合，将用于创建改进的指标，更好地预测可修改和不可修改因素的并发症风险。这项工作的最终结果是一种新的数据确认方法，用于测量结果，利用新的数据类型来改善患者轨迹的预测，并创建一个平台，与现有的信息技术对接，最终用于有并发症风险的患者的早期预警检测系统。这项工作的未来长期目标是确定早期预测做得更差的患者，然后对护理过程进行改进，以尽量减少并发症的发生。早期预警探测系统的建立在提高质量和最终降低成本方面具有重要的理论潜力。在美国，每年有近300亿美元用于创伤性受伤者的护理，创伤后并发症的发展被认为是护理总成本的主要贡献者。由于缺乏标准定义、报告偏倚、数据集访问以及分析技术无法解释导致患者结局的高度混杂关系，因此报告性能的能力受到阻碍。这个K 01奖将为我提供必要的支持，以实现以下目标：（1）成为将生物大数据应用于创伤护理的专家（2）阐明影响并发症发展的可修改因素的关系（3）获得先进的生物统计技术和生物信息学的经验;（4）发展独立的临床研究生涯。为了实现这些目标，我组建了一个多学科团队，其中包括创伤系统生物学和生物大数据方面的国家专家Cohen博士，以及两位共同导师：严重疾病后并发症转化研究专家Michael Matthay博士和高级生物统计技术专家Alan Hubbard博士，包括生物大数据分析。