Predicting Postoperative Acute Kidney Injury through Integration of Genetics and Electronic Health Records

通过整合遗传学和电子健康记录来预测术后急性肾损伤

基本信息

批准号：
10689663
负责人：
Nicholas J Douville
金额：
$ 16.74万
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：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10689663
关键词：
Acceleration Acute Acute Renal Failure with Renal Papillary Necrosis Adoption Affect Algorithms Anesthesia procedures Anesthesiology Apolipoprotein E Assessment tool Benchmarking Bioinformatics Board Certification Caring Chronic Classification Clinical Clinical Data Clinical Research Complex Complication Critical Care Data Data Science Development Diagnosis Diagnostic Disease Doctor of Philosophy Electronic Health Record Ensure Environment Exposure to Family Frequencies Genetic Genomics Goals Growth Health Personnel Heritability Hospitals Individual Information Systems Injury to Kidney Institution International Investigation Knowledge Laboratories Learning Medical Medicine Mentors Mentorship Methodology Michigan Modeling Morbidity - disease rate Nephrology Operative Surgical Procedures Outcome Outcomes Research Patient-Focused Outcomes Patients Peptidyl-Dipeptidase A Perioperative Perioperative complication Pharmacologic Substance Physicians Population Postoperative Period Precision Medicine Initiative Prevention Probability Procedures Process Provider Publishing Records Recovery Research Risk Risk Assessment Risk Factors Role Sample Size Scientist TNF gene Techniques Testing Time Training Training Programs United States National Institutes of Health Universities Update Variant Vocational Guidance clinical risk clinical training clinically significant cohort diverse data genetic analysis genetic information genetic variant genome wide association study genomic data genomic predictors improved kidney dysfunction member mortality next generation novel patient variability polygenic risk score prediction algorithm predictive modeling public health relevance risk prediction risk stratification trait

项目摘要

ABSTRACT Candidate: Dr. Nicholas Douville is a critical care anesthesiologist with board certification in anesthesiology at the University of Michigan. Through completion of the Medical-Scientist Training Program (MSTP) and clinical training in Anesthesiology and Critical Care Medicine, Dr. Douville has developed expertise in bioinformatics and perioperative outcomes research. This proposal builds on Dr. Douville’s expertise, providing protected time for training in bioinformatics, data science, and statistical techniques necessary to drive forward the prediction of patients at risk for postoperative acute kidney injury (poAKI). Environment: The University of Michigan is the coordinating center for the Multicenter Perioperative Outcomes Group (MPOG), an international consortium of over 50 anesthesiology and surgical departments with perioperative information systems. Dr. Sachin Kheterpal, MD, MBA is the primary mentor for Dr. Douville, and is the Director for MPOG and ex-member of the NIH Precision Medicine Initiative Advisory Panel. The proposed research will be completed under the guidance of Dr. Kheterpal, as well as co-mentors Cristen Willer, PhD (genetics) and Michael Heung, MD (nephrology), and Daniel Clauw, MD (general career guidance). Background: Acute Kidney Injury (AKI) occurs after 6-13% of non-cardiac procedures, and is associated with a six-fold increase in postoperative mortality. Numerous metrics for identifying at-risk patients have been developed incorporating preoperative and intraoperative data. Family and linkage studies have demonstrated renal dysfunction to be a heritable trait, however, the specific genetic underpinnings of acute, as opposed to chronic, kidney injury has only recently been explored in the perioperative period. These studies were limited by small sample size, did not consistently identify variants, and failed to utilize advanced genetic analysis, such as polygenic risk scores (PRS). Furthermore, predictive algorithms for poAKI fail to incorporate any genetic data, despite evidence that this may explain a substantial portion of the overall risk. Research: Our goal is to assist perioperative providers in improving patient outcomes through a unified platform that identifies patient attributes that may affect their care and stratifies the risk of key perioperative complications. Our proposed algorithm will combine clinical information (divided into preoperative and intraoperative data) with genetic information to identify patients with greater than baseline risk for developing poAKI. We will validate our methodology using clinical and genetic data from our institutional Michigan Genomics Initiative (MGI), where we have genetic data on over 70,000 individuals who have had surgery at the University of Michigan. We will first develop a polygenic risk score for poAKI (Aim 1). The polygenic risk score (developed in Aim 1) will then be integrated with other variables from the electronic health record (EHR) to provide a comprehensive risk assessment which will be benchmarked against a validated metric (Aim 2).

抽象的候选人：尼古拉斯·杜维尔（Nicholas Douville）博士是一名重症监护学家，并获得麻醉学董事会认证密歇根大学。通过完成医学科学家培训计划（MSTP）和临床 Douville博士在麻醉和重症监护医学方面的培训已开发出生物信息学专业知识和定期结果研究。该提案以杜维尔博士的专业知识为基础，提供了受保护的时间用于培训生物信息学，数据科学和统计技术，以推动预测术后急性肾脏损伤风险的患者（POAKI）。环境：密歇根大学是多中心围手术期结果的协调中心集团（MPOG），由50多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博士是Douville博士的主要精神，是 NIH Precision Medicine Initiative咨询小组的MPOG和前成员的主任。提议研究将在Kheterpal博士的指导下完成，以及CRISTEN WILLER博士（遗传学）和医学博士迈克尔·亨（Michael Heung）和医学博士丹尼尔·克劳（Daniel Clauw）（一般职业指导）。背景：急性肾脏损伤（AKI）发生在6-13％的非心脏手术后发生，与A相关术后死亡率增加了六倍。许多用于识别高危患者的指标开发了术前和术中数据。家庭和联系研究已经证明但是，肾功能障碍是一种可遗传的特征慢性，肾脏受伤直到最近才在周期性期间探索。这些研究受到限制按小样本量，并未始终如一地识别变体，并且未能利用先进的遗传分析，例如作为多基因风险评分（PRS）。此外，poaki的预测算法无法纳入任何遗传数据，目的地证据，表明这可能解释了总体风险的很大一部分。研究：我们的目标是协助定期提供者通过统一的标识可能影响其护理并分层关键期次风险的患者属性的平台并发症。我们提出的算法将结合临床信息（分为术前和术中数据）带有遗传信息，以识别出大于基线风险的患者 poaki。我们将使用密歇根州机构的临床和遗传数据来验证我们的方法论基因组学倡议（MGI），我们有超过70,000名在该手术的人的遗传数据密歇根大学。我们将首先为Poaki开发多基因风险评分（AIM 1）。多基因风险评分（在AIM 1中开发）然后将与从电子健康记录（EHR）到其他变量集成到提供全面的风险评估，该评估将对经过验证的度量标准进行基准测试（AIM 2）。