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

> **NIH NIH K08** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $167,400

## Abstract

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...

## Key facts

- **NIH application ID:** 10907475
- **Project number:** 5K08DK131346-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Nicholas J Douville
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $167,400
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10907475

## Citation

> US National Institutes of Health, RePORTER application 10907475, Predicting Postoperative Acute Kidney Injury through Integration of Genetics and Electronic Health Records (5K08DK131346-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10907475. Licensed CC0.

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