# Revealing Health Trajectories of Chronic Kidney Disease for Precision Medicine

> **NIH NIH R01** · INDIANA UNIVERSITY INDIANAPOLIS · 2022 · $329,401

## Abstract

Project summary
Chronic kidney disease (CKD) is common, affecting 14.8% of US adults, and disproportionately more in diverse
and underserved communities. CKD significantly reduces life expectancy and quality of life, while imposing
tremendous economic burden on society. A critical need persists for early identification of modifiable risk factors
in susceptible populations and to establish actionable support for medical decision making. Among the modifiable
risk factors, drug induced acute kidney injury (AKI) contributes to CKD development and progression. The current
knowledge of nephrotoxic drug-drug interactions (DDIs) is insufficient to prevent harm in heterogenous patient
subpopulations. Electronic health records (EHRs) from electronic medical records (EMR) and health insurance
claims data can help predict disparate CKD progression trajectories and uncover novel nephrotoxic drug
interactions. The Indiana University School of Medicine (IUSM) EHR collection includes rich clinical information
for 38 million individuals from regional and national populations over two-to-three decades. The IUSM EHR
collection is composed of Optum EHR derived from the Optum Clinformatics™ claim data and the Indiana EHR
incorporated from the EMR data of Indiana Network for Patient Care (INPC) Research Database, Indiana
University Health (IUH), and Eskenazi Health (EH). We propose to develop the DisEase PrOgression Trajectory
(DEPOT), an evidence-driven, graph-based clinical informatics approach to model CKD progression trajectories
and individualize clinical decision support. We hypothesize that there are different CKD progression paths which
are: 1) driven by different pathogenic mechanisms, 2) susceptible to different nephrotoxic drugs, and 3) identified
by unique EHR data patterns. Mathematically, such CKD trajectory landscapes can be learned as principle
graphs representing the topological and temporal characteristics of the observed, fragmented EHR data. The
goal of this work is to use the IUSM EHR data collection to 1) establish EHR-based CKD progression trajectories
and 2) to learn actionable knowledge to prevent drug-induced AKI and CKD. The multi-specialty team proposes
to: Aim 1. Construct CKD progression trajectories using graph artificial intelligence model and the IUSM EHR
data and Aim 2) Identify nephrotoxic DDIs in the general population and trajectory-specific
subpopulations that increase risks of AKI and CKD. The success of the proposed work will generate novel
knowledge about the landscape of CKD health trajectories and nephrotoxic DDIs, bridging gaps between rich
longitudinal EHR data and decision support for precision medicine in CKD. This work will shift paradigms of big
data and complex disease research, enabling EHR data to become part of daily CKD management.

## Key facts

- **NIH application ID:** 10445907
- **Project number:** 1R01LM013771-01A1
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Jing Su
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $329,401
- **Award type:** 1
- **Project period:** 2022-08-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10445907

## Citation

> US National Institutes of Health, RePORTER application 10445907, Revealing Health Trajectories of Chronic Kidney Disease for Precision Medicine (1R01LM013771-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10445907. Licensed CC0.

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