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

摘要 候选人：尼古拉斯·道维尔博士是一名拥有麻醉学董事会认证的重症监护麻醉师， 密歇根大学。通过完成医学科学家培训计划(MSTP)和临床 在麻醉学和重症监护医学方面的培训，道维尔博士在生物信息学方面积累了专业知识 以及围手术期结局研究。这项提议建立在道维尔博士的专业知识之上，提供了受保护的时间 用于生物信息学、数据科学和统计技术方面的培训，以推动预测 有术后急性肾损伤(PoAKI)风险的患者。 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)，一个由50多个麻醉和外科部门组成的国际财团， 围手术期信息系统。萨钦·克吕帕尔博士，医学博士，MBA，是道维尔博士的主要导师， MPOG主任和NIH精密医学倡议咨询小组的前成员。建议数 这项研究将在克拉斯帕尔博士和共同导师克里斯滕·威勒博士的指导下完成 (遗传学)和Michael Heung医学博士(肾脏病)，Daniel Clauw医学博士(一般职业指导)。 背景：急性肾损伤(AKI)发生在6%-13%的非心脏手术后，并与 术后死亡率增加6倍。识别高危患者的许多指标已经被 制定合并了术前和术中数据的研究。家庭和关联研究表明 肾功能不全是一种可遗传的特征，然而，急性肾功能不全的特定遗传基础与 慢性肾损伤直到最近才在围手术期被发现。这些研究是有限的 由于样本量小，没有一致地识别变异，也没有利用先进的遗传分析，如 作为多基因风险评分(PR)。此外，poAKI的预测算法没有纳入任何遗传 数据，尽管有证据表明这可能解释了总体风险的很大一部分。 研究：我们的目标是帮助围手术期提供者通过统一的 识别可能影响其护理的患者属性并对关键围手术期风险进行分层的平台 并发症。我们建议的算法将结合临床信息(分为术前和 术中数据)和遗传信息，以识别具有比基线更大的发病风险的患者 POAKI。我们将使用我们密歇根机构的临床和基因数据来验证我们的方法 基因组学倡议(MGI)，在那里我们有超过7万名在美国做过手术的人的基因数据 密歇根大学。我们将首先开发poAKI的多基因风险评分(目标1)。多基因风险评分 (在目标1中开发)然后将与电子健康记录(EHR)的其他变量相结合，以 提供全面的风险评估，并以经过验证的指标为基准(目标2)。

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).