Prediction Tool to Estimate Potassium Diet in Chronic Kidney Disease Patients Developed Using a Machine Learning Tool: The UniverSel Study

There is a need for a reliable and validated method to estimate dietary potassium intake in chronic kidney disease (CKD) patients to improve prevention of cardiovascular complications. This study aimed to develop a clinical tool to estimate potassium intake using 24-h urinary potassium excretion as...

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Autores principales: Granal, Maelys, Slimani, Lydia, Florens, Nans, Sens, Florence, Pelletier, Caroline, Pszczolinski, Romain, Casiez, Catherine, Kalbacher, Emilie, Jolivot, Anne, Dubourg, Laurence, Lemoine, Sandrine, Pasian, Celine, Ducher, Michel, Fauvel, Jean Pierre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228360/
https://www.ncbi.nlm.nih.gov/pubmed/35745151
http://dx.doi.org/10.3390/nu14122419
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author Granal, Maelys
Slimani, Lydia
Florens, Nans
Sens, Florence
Pelletier, Caroline
Pszczolinski, Romain
Casiez, Catherine
Kalbacher, Emilie
Jolivot, Anne
Dubourg, Laurence
Lemoine, Sandrine
Pasian, Celine
Ducher, Michel
Fauvel, Jean Pierre
author_facet Granal, Maelys
Slimani, Lydia
Florens, Nans
Sens, Florence
Pelletier, Caroline
Pszczolinski, Romain
Casiez, Catherine
Kalbacher, Emilie
Jolivot, Anne
Dubourg, Laurence
Lemoine, Sandrine
Pasian, Celine
Ducher, Michel
Fauvel, Jean Pierre
author_sort Granal, Maelys
collection PubMed
description There is a need for a reliable and validated method to estimate dietary potassium intake in chronic kidney disease (CKD) patients to improve prevention of cardiovascular complications. This study aimed to develop a clinical tool to estimate potassium intake using 24-h urinary potassium excretion as a surrogate of dietary potassium intake in this high-risk population. Data of 375 adult CKD-patients routinely collecting their 24-h urine were included to develop a prediction tool to estimate potassium diet. The prediction tool was built from a random sample of 80% of patients and validated on the remaining 20%. The accuracy of the prediction tool to classify potassium diet in the three classes of potassium excretion was 74%. Surprisingly, the variables related to potassium consumption were more related to clinical characteristics and renal pathology than to the potassium content of the ingested food. Artificial intelligence allowed to develop an easy-to-use tool for estimating patients’ diets in clinical practice. After external validation, this tool could be extended to all CKD-patients for a better clinical and therapeutic management for the prevention of cardiovascular complications.
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spelling pubmed-92283602022-06-25 Prediction Tool to Estimate Potassium Diet in Chronic Kidney Disease Patients Developed Using a Machine Learning Tool: The UniverSel Study Granal, Maelys Slimani, Lydia Florens, Nans Sens, Florence Pelletier, Caroline Pszczolinski, Romain Casiez, Catherine Kalbacher, Emilie Jolivot, Anne Dubourg, Laurence Lemoine, Sandrine Pasian, Celine Ducher, Michel Fauvel, Jean Pierre Nutrients Article There is a need for a reliable and validated method to estimate dietary potassium intake in chronic kidney disease (CKD) patients to improve prevention of cardiovascular complications. This study aimed to develop a clinical tool to estimate potassium intake using 24-h urinary potassium excretion as a surrogate of dietary potassium intake in this high-risk population. Data of 375 adult CKD-patients routinely collecting their 24-h urine were included to develop a prediction tool to estimate potassium diet. The prediction tool was built from a random sample of 80% of patients and validated on the remaining 20%. The accuracy of the prediction tool to classify potassium diet in the three classes of potassium excretion was 74%. Surprisingly, the variables related to potassium consumption were more related to clinical characteristics and renal pathology than to the potassium content of the ingested food. Artificial intelligence allowed to develop an easy-to-use tool for estimating patients’ diets in clinical practice. After external validation, this tool could be extended to all CKD-patients for a better clinical and therapeutic management for the prevention of cardiovascular complications. MDPI 2022-06-10 /pmc/articles/PMC9228360/ /pubmed/35745151 http://dx.doi.org/10.3390/nu14122419 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Granal, Maelys
Slimani, Lydia
Florens, Nans
Sens, Florence
Pelletier, Caroline
Pszczolinski, Romain
Casiez, Catherine
Kalbacher, Emilie
Jolivot, Anne
Dubourg, Laurence
Lemoine, Sandrine
Pasian, Celine
Ducher, Michel
Fauvel, Jean Pierre
Prediction Tool to Estimate Potassium Diet in Chronic Kidney Disease Patients Developed Using a Machine Learning Tool: The UniverSel Study
title Prediction Tool to Estimate Potassium Diet in Chronic Kidney Disease Patients Developed Using a Machine Learning Tool: The UniverSel Study
title_full Prediction Tool to Estimate Potassium Diet in Chronic Kidney Disease Patients Developed Using a Machine Learning Tool: The UniverSel Study
title_fullStr Prediction Tool to Estimate Potassium Diet in Chronic Kidney Disease Patients Developed Using a Machine Learning Tool: The UniverSel Study
title_full_unstemmed Prediction Tool to Estimate Potassium Diet in Chronic Kidney Disease Patients Developed Using a Machine Learning Tool: The UniverSel Study
title_short Prediction Tool to Estimate Potassium Diet in Chronic Kidney Disease Patients Developed Using a Machine Learning Tool: The UniverSel Study
title_sort prediction tool to estimate potassium diet in chronic kidney disease patients developed using a machine learning tool: the universel study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228360/
https://www.ncbi.nlm.nih.gov/pubmed/35745151
http://dx.doi.org/10.3390/nu14122419
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