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: 10349621
• 负责人: Nicholas J Douville
• 金额: $ 16.89万
• 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/10349621
• 关键词: 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; Conflict (Psychology); 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; International; Investigation; Knowledge; Laboratories; Learning; Medical; Medical Genetics; 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; Play; Population; Postoperative Period; Precision Medicine Initiative; 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; clinically significant; cohort; diverse data; genetic analysis; genetic information; genetic variant; genome wide association study; genomic data; genomic predictors; improved; injury prevention; kidney dysfunction; member; mortality; next generation; novel; polygenic risk score; prediction algorithm; predictive modeling; public health relevance; 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).

摘要