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

• 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
• 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

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：Nicholas Douville 博士是一名重症监护麻醉师，拥有麻醉学委员会认证 密歇根大学。通过完成医学科学家培训计划（MSTP）和临床 Douville 博士接受过麻醉学和重症监护医学方面的培训，积累了生物信息学方面的专业知识 和围手术期结果研究。该提案建立在 Douville 博士的专业知识之上，提供受保护的时间 用于推动预测所需的生物信息学、数据科学和统计技术方面的培训 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 多个麻醉科和外科科室组成的国际联盟， 围手术期信息系统。 Sachin Kheterpal 博士（医学博士、工商管理硕士）是 Douville 博士的主要导师， MPOG 主任和 NIH 精准医学计划咨询小组前成员。拟议的 研究将在 Kheterpal 博士以及共同导师 Cristen Willer 博士的指导下完成 （遗传学）、Michael Heung 医学博士（肾病学）和 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，并且与 术后死亡率增加六倍。许多用于识别高危患者的指标已被 结合术前和术中数据开发。家庭和联系研究表明 肾功能障碍是一种可遗传的特征，然而，急性肾功能障碍的特定遗传基础，与 最近才在围手术期探索慢性肾损伤。这些研究是有限的 由于样本量小，无法一致地识别变异，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 的预测算法未能纳入任何遗传因素 尽管有证据表明这可以解释整体风险的很大一部分。 研究：我们的目标是通过统一的方法协助围手术期提供者改善患者的治疗结果 该平台可识别可能影响护理的患者属性并对关键围手术期风险进行分层 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）和遗传信息，以识别具有高于基线风险的患者 波阿基。我们将使用我们密歇根机构的临床和遗传数据来验证我们的方法 基因组计划 (MGI)，我们拥有超过 70,000 名在该中心接受过手术的个体的基因数据 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）将与电子健康记录 (EHR) 中的其他变量集成到 提供全面的风险评估，该评估将以经过验证的指标为基准（目标 2）。