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Prediction Tool to Estimate Potassium Diet in Chronic Kidney Disease Patients Developed Using a Machine Learning
Maelys Granal1, Lydia Slimani1, Nans Florens1
1Hospices Civils de Lyon, Service de Néphrologie, Hôpital Edouard Herriot, Université Claude Bernard Lyon 1, CEDEX, F-69437 Lyon, France.
Nutrients
|June 24, 2022
Summary
Estimating dietary potassium in chronic kidney disease (CKD) patients is crucial for preventing cardiovascular issues. This study developed an AI tool using 24-hour urine potassium to estimate dietary intake, aiding patient management.
Area of Science:
- Nephrology
- Cardiovascular Medicine
- Nutritional Science
Background:
- Dietary potassium intake estimation is vital for chronic kidney disease (CKD) patients to mitigate cardiovascular risks.
- Current methods for assessing potassium intake in CKD are often unreliable, necessitating improved clinical tools.
Purpose of the Study:
- To develop and validate a clinical tool for estimating dietary potassium intake in CKD patients.
- To utilize 24-hour urinary potassium excretion as a surrogate marker for dietary potassium consumption.
- To leverage artificial intelligence for creating an accessible tool for clinical practice.
Main Methods:
- A prediction tool was developed using data from 375 adult CKD patients.
- The tool was created from an 80% random sample and validated on the remaining 20%.
- Artificial intelligence was employed to build an easy-to-use estimation tool.
Main Results:
- The developed prediction tool achieved 74% accuracy in classifying potassium intake levels.
- Factors influencing potassium consumption were found to be more linked to clinical characteristics and renal pathology than food potassium content.
- An AI-driven tool was successfully developed for practical clinical use.
Conclusions:
- The AI-powered tool offers a reliable method for estimating dietary potassium in CKD patients.
- This tool can enhance clinical and therapeutic management, aiding in the prevention of cardiovascular complications.
- Further external validation is recommended before widespread clinical adoption.
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