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：术后肺并发症（PPC）是常见的，是发病率的主要驱动因素大规模住院手术后的死亡率。各种风险预测分数确定患者的高风险开发PPC和观察性研究已将围手术护理实践与随后的风险。但是，麻醉提供者没有患者的特定证据干预措施来防止肺并发症。研究：拟议的研究将利用大量可用的围手术期信息来识别患者的相互作用，治疗因素的过程和过程，使患者处于PPC的风险。这会将数据科学的进步纳入了PPC的术前预测（AIM 1）。然后，我们将修改和根据呼吸机，显示器和患者对在交付麻醉护理期间做出的现实生活决定的反应（AIM 2）。这将允许了解最大的特征有助于患者特定风险（AIM 3）。拟议的研究和培训将为Colquhoun博士提供数据科学技能，以使其过渡到独立研究人员。候选人：道格拉斯博士A Colquhoun是终身轨道助理助理麻醉学的助理教授密歇根大学。他获得麻醉和重症监护医学的董事会认证，并保持接受大手术的患者围手术期护理中的主动临床实践。在T32研究中培训赠款，Colquhoun博士在衍生成果和护理过程中开发了专业知识电子病历数据。他的长期职业目标是防止术后肺部并发症通过提供麻醉提供者数据驱动的管理策略，以进行护理时提供的管理。环境：密歇根大学是多中心围手术期的协调中心成果集团（MPOG）由50个麻醉学和外科部门的国际财团与围手术期信息系统。 MBA医学博士Sachin Kheterpal是Colquhoun博士的主要导师，并且是 MPOG的董事。 Kheterpal博士和麻醉学系有悠久的发展历史并部署创新的软件解决方案来解决围手术医学和研究中的问题。博士此外，将从整个机构中提取的专家联合会和科学领域提出建议术后并发症的预防和管理方面的咨询小组专家。