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Renal failure occurs when the kidneys lose their ability to filter waste products from the blood effectively. It can be classified into two types: acute renal failure (ARF) and chronic renal failure (CRF).
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...

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Related Experiment Video

Updated: Jul 8, 2025

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

Published on: February 2, 2021

A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney

Inger Persson^1,2, Adam Grünwald², Ludivine Morvan²

¹Department of Statistics, Uppsala University, Uppsala, Sweden.

JMIR Formative Research

|December 14, 2023

Summary

This summary is machine-generated.

A new machine learning algorithm, NAVOY Acute Kidney Injury, can predict the onset of acute kidney injury (AKI) in intensive care unit (ICU) patients up to 12 hours in advance. This early prediction tool uses routinely collected electronic health record data to improve patient outcomes.

Keywords:

AKI ICU acute kidney injury algorithm early detection electronic health records intensive care unit machine learning nephrology prediction software as a medical device

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Area of Science:

Critical care medicine
Biomedical informatics
Machine learning in healthcare

Background:

Acute kidney injury (AKI) is a significant global health challenge with high mortality rates.
Current AKI detection relies on late indicators, delaying intervention.
Early AKI prediction is crucial for proactive management and improved patient outcomes.

Purpose of the Study:

To develop the NAVOY Acute Kidney Injury machine learning algorithm for predicting AKI in ICU patients.
To create a clinical decision support tool for proactive AKI management.
To enhance patient outcomes through early AKI detection.

Main Methods:

Developed a hybrid ensemble model combining Random Forest and XGBoost algorithms.
Utilized 22 clinical variables for hourly AKI prediction based on KDIGO guidelines.
Trained the algorithm on the MIMIC-IV clinical database from MIT Lab for Computational Physiology.

Main Results:

The NAVOY AKI algorithm accurately predicts high-risk patients 12 hours before AKI onset using 4 hours of input data.
Achieved an AUROC of 0.91 and AUPRC of 0.75, comparable to existing algorithms.
Externally validated on an independent dataset, confirming exceptional predictive accuracy.

Conclusions:

NAVOY Acute Kidney Injury accurately predicts AKI onset using readily available ICU electronic health record data.
Early detection capability enhances patient monitoring and management, potentially improving outcomes.
First CE-marked AKI prediction algorithm for commercial use in European ICUs.