Advancing Outcome Metrics in Trauma Surgery Through Utilization of Big Data

点击翻译按钮获取中文摘要

 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.