# Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2021 · $621,756

## Abstract

PROJECT SUMMARY
Acute kidney injury (AKI) occurs in up to 20% of hospitalized patients and is associated with increased risk of
readmission, morbidity, and mortality. The estimated annual cost of AKI care in the US is over 10 billion dollars,
and, with the incidence rising, these costs will continue to increase. The current gold standards for diagnosing
AKI, creatinine and urine output, are often delayed in their recognition of tubular injury. Prior work on AKI has
typically focused on patients who have already developed AKI based on these standards, and interventions at
this late time point have had mixed success. In contrast, emerging data suggest that intervening earlier can
improve outcomes. Therefore, it is critical to optimize the early detection of AKI in hospitalized patients.
 We have previously developed a machine learning tool to identify patients at high risk of severe (stage 2
or greater) AKI more than a day earlier than clinically apparent using structured electronic health record (EHR)
data. Although more accurate than prior methods, it suffers from a high rate of false positives, which limits its
value in clinical practice. There is a large amount of valuable information that is stored in unstructured free-text
fields (e.g., clinical notes) that could be utilized using natural language processing (NLP) within advanced deep
learning neural network models that could significantly improve the detection of early AKI. Furthermore, there
are established and emerging kidney injury biomarkers that could be combined with EHR-based models to
improve accuracy even further. Finally, it remains unclear what interventions will have the best chance of
decreasing the risk for developing severe AKI in high-risk patients. A better understanding of which interventions
are of greatest benefit to specific patients is critical for improving the outcomes of patients at risk of AKI.
 The objective of this project is to develop novel tools to improve the identification and treatment of patients
at high risk of AKI using a large, multicenter cohort. In Aim 1, we will use NLP and deep learning algorithms to
develop a model to predict severe AKI across four health systems. In Aim 2, we will silently run the best-
performing model developed in Aim 1 in real-time to identify high-risk patients. Manual retrospective chart review
will be performed on a cohort of the highest risk patients to determine both the proportion of patients who receive
guideline-based care as well as the association between receipt of guideline-based care and outcomes. We will
also identify novel phenotypes of patients who are particularly helped or harmed by specific guideline-based
interventions. Finally, in Aim 3, we will collect kidney injury biomarkers in the highest-risk patients to determine
the added value of biomarkers to EHR-based models alone. Our proposal will provide clinicians with new tools
to identify patients at risk of AKI earlier and more accurately. It will also provi...

## Key facts

- **NIH application ID:** 10294824
- **Project number:** 1R01DK126933-01A1
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Matthew Michael Churpek
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $621,756
- **Award type:** 1
- **Project period:** 2021-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10294824

## Citation

> US National Institutes of Health, RePORTER application 10294824, Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury (1R01DK126933-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10294824. Licensed CC0.

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