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A prediction model of Nephrolithiasis Risk: A population-based cohort study in Korea

PURPOSE: Well-validated risk prediction models help to stratify individuals on the basis of their disease risks and to guide health care professionals in decision-making. The incidence of nephrolithiasis has been increasing in Korea. Racial differences in the distribution of and risk for nephrolithi...

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Autores principales: Mukasa, David, Sung, Joohon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Urological Association 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052416/
https://www.ncbi.nlm.nih.gov/pubmed/32158970
http://dx.doi.org/10.4111/icu.2020.61.2.188
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author Mukasa, David
Sung, Joohon
author_facet Mukasa, David
Sung, Joohon
author_sort Mukasa, David
collection PubMed
description PURPOSE: Well-validated risk prediction models help to stratify individuals on the basis of their disease risks and to guide health care professionals in decision-making. The incidence of nephrolithiasis has been increasing in Korea. Racial differences in the distribution of and risk for nephrolithiasis have been reported in Asia but no population-specific nephrolithiasis models have been developed. We aimed to develop a simplified nephrolithiasis prediction model for the Korean population by using data from general medical practice. MATERIALS AND METHODS: This was a prospective, population-based cohort study in Korea. A total of 497,701 participants from the National Health Insurance Service–National Sample Cohort (NHIS-NSC) were enrolled from 2002 to 2010. A Cox proportional hazards model was used. RESULTS: During a median follow-up time of 8.5 years (range, 2.0–8.9 years) and among 497,701 participants, there were 15,783 cases (3.2%) of nephrolithiasis. The parsimonious model included age, sex, income grade, alcohol consumption, body mass index, total cholesterol, fasting blood glucose, and medical history of diseases. The Harrell's C-statistic was 0.806 (95% confidence interval [CI], 0.790–0.821) and 0.805 (95% CI, 0.782–0.827) in the derivation and validation cohorts, respectively. CONCLUSIONS: The results of the present study imply that nephrolithiasis risk can be predicted by use of data from general medical practice and based on predictors that clinicians and individuals from the general population are likely to know. This model comprises modifiable risk factors and can be used to identify those at higher risk who can modify their lifestyle to lower their risk for nephrolithiasis. This study also offers an opportunity for external validation or updating of the model through the incorporation of other risk predictors in other settings.
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spelling pubmed-70524162020-03-10 A prediction model of Nephrolithiasis Risk: A population-based cohort study in Korea Mukasa, David Sung, Joohon Investig Clin Urol Original Article PURPOSE: Well-validated risk prediction models help to stratify individuals on the basis of their disease risks and to guide health care professionals in decision-making. The incidence of nephrolithiasis has been increasing in Korea. Racial differences in the distribution of and risk for nephrolithiasis have been reported in Asia but no population-specific nephrolithiasis models have been developed. We aimed to develop a simplified nephrolithiasis prediction model for the Korean population by using data from general medical practice. MATERIALS AND METHODS: This was a prospective, population-based cohort study in Korea. A total of 497,701 participants from the National Health Insurance Service–National Sample Cohort (NHIS-NSC) were enrolled from 2002 to 2010. A Cox proportional hazards model was used. RESULTS: During a median follow-up time of 8.5 years (range, 2.0–8.9 years) and among 497,701 participants, there were 15,783 cases (3.2%) of nephrolithiasis. The parsimonious model included age, sex, income grade, alcohol consumption, body mass index, total cholesterol, fasting blood glucose, and medical history of diseases. The Harrell's C-statistic was 0.806 (95% confidence interval [CI], 0.790–0.821) and 0.805 (95% CI, 0.782–0.827) in the derivation and validation cohorts, respectively. CONCLUSIONS: The results of the present study imply that nephrolithiasis risk can be predicted by use of data from general medical practice and based on predictors that clinicians and individuals from the general population are likely to know. This model comprises modifiable risk factors and can be used to identify those at higher risk who can modify their lifestyle to lower their risk for nephrolithiasis. This study also offers an opportunity for external validation or updating of the model through the incorporation of other risk predictors in other settings. The Korean Urological Association 2020-03 2020-02-12 /pmc/articles/PMC7052416/ /pubmed/32158970 http://dx.doi.org/10.4111/icu.2020.61.2.188 Text en © The Korean Urological Association, 2020 http://creativecommons.org/licenses/by-nc/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Mukasa, David
Sung, Joohon
A prediction model of Nephrolithiasis Risk: A population-based cohort study in Korea
title A prediction model of Nephrolithiasis Risk: A population-based cohort study in Korea
title_full A prediction model of Nephrolithiasis Risk: A population-based cohort study in Korea
title_fullStr A prediction model of Nephrolithiasis Risk: A population-based cohort study in Korea
title_full_unstemmed A prediction model of Nephrolithiasis Risk: A population-based cohort study in Korea
title_short A prediction model of Nephrolithiasis Risk: A population-based cohort study in Korea
title_sort prediction model of nephrolithiasis risk: a population-based cohort study in korea
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052416/
https://www.ncbi.nlm.nih.gov/pubmed/32158970
http://dx.doi.org/10.4111/icu.2020.61.2.188
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