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Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model

There is a great need for a diagnostic tool using simple clinical information collected from patients to diagnose uric acid (UA) stones in nephrolithiasis. We built a predictive model making use of machine learning (ML) methodologies entering simple parameters easily obtained at the initial clinical...

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Autores principales: Chen, Hao-Wei, Chen, Yu-Chen, Lee, Jung-Ting, Yang, Frances M., Kao, Chung-Yao, Chou, Yii-Her, Chu, Ting-Yin, Juan, Yung-Shun, Wu, Wen-Jeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103478/
https://www.ncbi.nlm.nih.gov/pubmed/35565794
http://dx.doi.org/10.3390/nu14091829
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author Chen, Hao-Wei
Chen, Yu-Chen
Lee, Jung-Ting
Yang, Frances M.
Kao, Chung-Yao
Chou, Yii-Her
Chu, Ting-Yin
Juan, Yung-Shun
Wu, Wen-Jeng
author_facet Chen, Hao-Wei
Chen, Yu-Chen
Lee, Jung-Ting
Yang, Frances M.
Kao, Chung-Yao
Chou, Yii-Her
Chu, Ting-Yin
Juan, Yung-Shun
Wu, Wen-Jeng
author_sort Chen, Hao-Wei
collection PubMed
description There is a great need for a diagnostic tool using simple clinical information collected from patients to diagnose uric acid (UA) stones in nephrolithiasis. We built a predictive model making use of machine learning (ML) methodologies entering simple parameters easily obtained at the initial clinical visit. Socio-demographic, health, and clinical data from two cohorts (A and B), both diagnosed with nephrolithiasis, one between 2012 and 2016 and the other between June and December 2020, were collected before nephrolithiasis treatment. A ML-based model for predicting UA stones in nephrolithiasis was developed using eight simple parameters—sex, age, gout, diabetes mellitus, body mass index, estimated glomerular filtration rate, bacteriuria, and urine pH. Data from Cohort A were used for model training and validation (ratio 3:2), while data from Cohort B were used only for validation. One hundred and forty-six (13.3%) out of 1098 patients in Cohort A and 3 (4.23%) out of 71 patients in Cohort B had pure UA stones. For Cohort A, our model achieved a validation AUC (area under ROC curve) of 0.842, with 0.8475 sensitivity and 0.748 specificity. For Cohort B, our model achieved 0.936 AUC, with 1.0 sensitivity, and 0.912 specificity. This ML-based model provides a convenient and reliable method for diagnosing urolithiasis. Using only eight readily available clinical parameters, including information about metabolic disorder and obesity, it distinguished pure uric acid stones from other stones before treatment.
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spelling pubmed-91034782022-05-14 Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model Chen, Hao-Wei Chen, Yu-Chen Lee, Jung-Ting Yang, Frances M. Kao, Chung-Yao Chou, Yii-Her Chu, Ting-Yin Juan, Yung-Shun Wu, Wen-Jeng Nutrients Article There is a great need for a diagnostic tool using simple clinical information collected from patients to diagnose uric acid (UA) stones in nephrolithiasis. We built a predictive model making use of machine learning (ML) methodologies entering simple parameters easily obtained at the initial clinical visit. Socio-demographic, health, and clinical data from two cohorts (A and B), both diagnosed with nephrolithiasis, one between 2012 and 2016 and the other between June and December 2020, were collected before nephrolithiasis treatment. A ML-based model for predicting UA stones in nephrolithiasis was developed using eight simple parameters—sex, age, gout, diabetes mellitus, body mass index, estimated glomerular filtration rate, bacteriuria, and urine pH. Data from Cohort A were used for model training and validation (ratio 3:2), while data from Cohort B were used only for validation. One hundred and forty-six (13.3%) out of 1098 patients in Cohort A and 3 (4.23%) out of 71 patients in Cohort B had pure UA stones. For Cohort A, our model achieved a validation AUC (area under ROC curve) of 0.842, with 0.8475 sensitivity and 0.748 specificity. For Cohort B, our model achieved 0.936 AUC, with 1.0 sensitivity, and 0.912 specificity. This ML-based model provides a convenient and reliable method for diagnosing urolithiasis. Using only eight readily available clinical parameters, including information about metabolic disorder and obesity, it distinguished pure uric acid stones from other stones before treatment. MDPI 2022-04-27 /pmc/articles/PMC9103478/ /pubmed/35565794 http://dx.doi.org/10.3390/nu14091829 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
Chen, Hao-Wei
Chen, Yu-Chen
Lee, Jung-Ting
Yang, Frances M.
Kao, Chung-Yao
Chou, Yii-Her
Chu, Ting-Yin
Juan, Yung-Shun
Wu, Wen-Jeng
Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model
title Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model
title_full Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model
title_fullStr Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model
title_full_unstemmed Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model
title_short Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model
title_sort prediction of the uric acid component in nephrolithiasis using simple clinical information about metabolic disorder and obesity: a machine learning-based model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103478/
https://www.ncbi.nlm.nih.gov/pubmed/35565794
http://dx.doi.org/10.3390/nu14091829
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