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

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Published on: February 2, 2021

Predicting chronic kidney disease progression with artificial intelligence.

Mario A Isaza-Ruget¹, Nancy Yomayusa², Camilo A González³

¹Pathology and clinical laboratory. INPAC research group. Clinica Colsanitas. Keralty group, Fundación Universitaria Sanitas, Bogotá, Colombia.

|April 26, 2024

Summary

Machine learning models predict chronic kidney disease (CKD) progression and the need for renal replacement therapy (RRT). A time-to-event model shows promise for clinical use in managing CKD patients.

Keywords:

Artificial intelligence Machine learning Prediction Renal insufficiency Renal replacement therapy

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Published on: October 10, 2018

Area of Science:

Nephrology
Medical Informatics
Data Science

Background:

Chronic kidney disease (CKD) progression prediction tools lack clinical utility.
Developing accurate predictive models for advanced CKD is crucial.

Purpose of the Study:

To develop and validate machine learning models for predicting CKD progression.
To predict the need for renal replacement therapy (RRT) in patients with stage 3-5 CKD.

Main Methods:

Retrospective, observational cohort study.
Utilized demographic, clinical, and laboratory data from CKD patients.
Developed and compared three machine learning prediction models.

Main Results:

Model 1 (4.5-year risk) achieved F1 scores of 0.82 (RRT), 0.53 (stage progression), and 0.55 (eGFR reduction).
Model 2 (time-to-event) showed C-indices of 0.89 (RRT), 0.67 (stage progression), and 0.67 (eGFR reduction).
Model 3 (reduced Model 2) demonstrated C-indices of 0.68 (RRT), 0.68 (stage progression), and 0.88 (eGFR reduction).

Conclusions:

The time-to-event model effectively predicted CKD progression outcomes over five years.
This model can aid in forecasting the onset and timing of adverse events in CKD patients.