Data Driven Approaches to Improving Risk Prediction of Pulmonary Complications After Major Inpatient Surgery

数据驱动的方法改善重大住院手术后肺部并发症的风险预测

基本信息

批准号：
10283012
负责人：
Douglas Alastair Colquhoun
金额：
$ 16.66万
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：
2021
资助国家：
美国
起止时间：
2021-08-15 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10283012
关键词：
Acute Renal Failure with Renal Papillary Necrosis Address Affect Anesthesia procedures Anesthesiology Caring Clinical Competence Complex Complication Computer software Computerized Medical Record Consent Critical Care Data Data Science Decision Making Derivation procedure Development Electronic Health Record Elements Event Evidence based intervention Goals Grant Hospital Costs Hospitals Hypoxemia Induction of neuromuscular blockade Information Systems Inpatients Institution International Intervention Intraoperative Care Intraoperative Period Knowledge Learning Length of Stay Life Light Liquid substance Lung Machine Learning Mechanical ventilation Medicine Mentors Michigan Modernization Monitor Morbidity - disease rate Myocardial Infarction Observational Study Operating Rooms Operative Surgical Procedures Opioid Outcome Patient Care Patient risk Patients Patterns of Care Perioperative Perioperative Care Physiological Physiology Pneumonia Population Postoperative Complications Postoperative Period Preoperative Procedure Prevention Private Hospitals Procedures Process Provider Recording of previous events Records Registries Reporting Research Research Personnel Research Training Resources Risk Risk Estimate Risk Factors S Phase Scientist Series Site Statistical Models Structure Surgical complication Techniques Time Training Transfusion Translating Universities Update Variant Ventilator Work base career career development clinical practice clinically relevant cohort cost data complexity data modeling data streams evidence base high dimensionality high risk improved individual patient innovation inpatient surgery insight interoperability minimally invasive mortality myocardial injury patient registry patient response point of care predictive modeling prevent professor risk prediction risk prediction model skills supervised learning tenure track tool vigilance

项目摘要

ABSTRACT BACKGROUND: Postoperative pulmonary complications (PPCs) are common and major drivers of morbidity and mortality after major inpatient surgery. Various risk prediction scores identify patients at high risk of developing PPCs and observational research has connected peri-operative care practices with subsequent risk. However, anesthesia providers do not have patient specific evidence based interventions to prevent pulmonary complications. RESEARCH: The proposed research will draw on a wealth of perioperative information available to identify the interactions of patient, procedure and process of care factors which place patients at risk of PPCs. This will incorporate advances in data science to the pre-operative prediction of PPCs (Aim 1). We will then revise and improve this estimate in light of the high fidelity intraoperative data stream from ventilators, monitors and patient response to real life decisions being made during the delivery of anesthesia care (Aim 2). This will allow understanding of what features most contributed to patient specific risk (Aim 3). The proposed research and training will provide Dr Colquhoun with the skills in data science to his transition to an independent researcher. CANDIDATE: Dr Douglas A Colquhoun is a tenure track Assistant Professor of Anesthesiology at the University of Michigan. He is board certified in Anesthesiology and Critical Care Medicine and maintains an active clinical practice in the perioperative care of patients undergoing major surgery. During a T32 Research Training Grant, Dr Colquhoun developed expertise in the derivation of outcomes and processes of care from electronic medical record data. His long term career goal is to prevent postoperative pulmonary complications by offering anesthesia providers data driven strategies for management delivered at the point of care. ENVIRONMENT: The University of Michigan is the coordinating center for the Multicenter Perioperative Outcomes Group (MPOG) an international consortium of 50 anesthesiology and surgical departments with perioperative information systems. Sachin Kheterpal, MD, MBA is the primary mentor for Dr. Colquhoun, and is the Director for MPOG. Dr Kheterpal and the Department of Anesthesiology have a rich history of developing and deploying innovative software solutions to address problems in perioperative medicine and research. Dr Colquhoun will additionally be advised from expert co-mentors drawn from across the institution and a scientific advisory panel expert in the prevention and management of postoperative complications.

抽象的背景：术后肺部并发症（PPC）是常见的，也是发病的主要驱动因素以及重大住院手术后的死亡率。各种风险预测评分可识别高风险患者开发 PPC 和观察性研究将围手术期护理实践与后续治疗联系起来风险。然而，麻醉提供者没有基于患者具体证据的干预措施来预防肺部并发症。研究：拟议的研究将利用大量可用的围手术期信息来确定患者、程序和护理过程等因素的相互作用使患者面临 PPC 的风险。这将将数据科学的进步纳入 PPC 的术前预测（目标 1）。然后我们将修改并根据来自呼吸机、监视器和设备的高保真术中数据流改进这一估计患者对麻醉护理期间做出的现实生活决策的反应（目标 2）。这将允许了解哪些特征对患者特定风险影响最大（目标 3）。拟议的研究和培训将为 Colquhoun 博士提供数据科学技能，帮助他转型为独立研究员。候选人：Douglas A Colquhoun 博士是麻醉学终身教授助理教授密歇根大学。他获得了麻醉学和重症监护医学委员会认证，并保持着积极开展大手术患者围手术期护理的临床实践。在 T32 研究期间通过培训补助金，Colquhoun 博士发展了从以下方面推导出护理结果和过程的专业知识：电子病历数据。他的长期职业目标是预防术后肺部并发症通过为麻醉提供者提供数据驱动的护理策略来进行管理。环境：密歇根大学是多中心围手术期的协调中心 Outcomes Group (MPOG) 是一个由 50 个麻醉科和外科科室组成的国际联盟，围手术期信息系统。 Sachin Kheterpal，医学博士、工商管理硕士是 Colquhoun 博士的主要导师， MPOG 总监。 Kheterpal 博士和麻醉科拥有丰富的发展历史部署创新的软件解决方案来解决围手术期医学和研究中的问题。博士 Colquhoun 还将获得来自整个机构的专家联合导师和科学界人士的建议。预防和管理术后并发症的顾问小组专家。