A simple prediction model of hyperuricemia for use in a rural setting

Abstract Currently, the most widely used screening methods for hyperuricemia (HUA) involves invasive laboratory tests, which are lacking in many rural hospitals in China. This study explored the use of non-invasive physical examinations to construct a simple prediction model for HUA, in order to red...

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Autores principales:	Jia-Cheng Shi, Xiao-Huan Chen, Qiong Yang, Cai-Mei Wang, Qian Huang, Yan-Ming Shen, Jian Yu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f1010455338443cfb0e8de56cb0a645e
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spelling	oai:doaj.org-article:f1010455338443cfb0e8de56cb0a645e2021-12-05T12:15:21ZA simple prediction model of hyperuricemia for use in a rural setting10.1038/s41598-021-02716-y2045-2322https://doaj.org/article/f1010455338443cfb0e8de56cb0a645e2021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02716-yhttps://doaj.org/toc/2045-2322Abstract Currently, the most widely used screening methods for hyperuricemia (HUA) involves invasive laboratory tests, which are lacking in many rural hospitals in China. This study explored the use of non-invasive physical examinations to construct a simple prediction model for HUA, in order to reduce the economic burden and invasive operations such as blood sampling, and provide some help for the health management of people in poor areas with backward medical resources. Data of 9252 adults from April to June 2017 in the Affiliated Hospital of Guilin Medical College were collected and divided randomly into a training set (n = 6364) and a validation set (n = 2888) at a ratio of 7:3. In the training set, non-invasive physical examination indicators of age, gender, body mass index (BMI) and prevalence of hypertension were included for logistic regression analysis, and a nomogram model was established. The classification and regression tree (CART) algorithm of the decision tree model was used to build a classification tree model. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analyses (DCA) were used to test the distinction, accuracy and clinical applicability of the two models. The results showed age, gender, BMI and prevalence of hypertension were all related to the occurrence of HUA. The area under the ROC curve (AUC) of the nomogram model was 0.806 and 0.791 in training set and validation set, respectively. The AUC of the classification tree model was 0.802 and 0.794 in the two sets, respectively, but were not statistically different. The calibration curves and DCAs of the two models performed well on accuracy and clinical practicality, which suggested these models may be suitable to predict HUA for rural setting.Jia-Cheng ShiXiao-Huan ChenQiong YangCai-Mei WangQian HuangYan-Ming ShenJian YuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jia-Cheng Shi Xiao-Huan Chen Qiong Yang Cai-Mei Wang Qian Huang Yan-Ming Shen Jian Yu A simple prediction model of hyperuricemia for use in a rural setting
description	Abstract Currently, the most widely used screening methods for hyperuricemia (HUA) involves invasive laboratory tests, which are lacking in many rural hospitals in China. This study explored the use of non-invasive physical examinations to construct a simple prediction model for HUA, in order to reduce the economic burden and invasive operations such as blood sampling, and provide some help for the health management of people in poor areas with backward medical resources. Data of 9252 adults from April to June 2017 in the Affiliated Hospital of Guilin Medical College were collected and divided randomly into a training set (n = 6364) and a validation set (n = 2888) at a ratio of 7:3. In the training set, non-invasive physical examination indicators of age, gender, body mass index (BMI) and prevalence of hypertension were included for logistic regression analysis, and a nomogram model was established. The classification and regression tree (CART) algorithm of the decision tree model was used to build a classification tree model. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analyses (DCA) were used to test the distinction, accuracy and clinical applicability of the two models. The results showed age, gender, BMI and prevalence of hypertension were all related to the occurrence of HUA. The area under the ROC curve (AUC) of the nomogram model was 0.806 and 0.791 in training set and validation set, respectively. The AUC of the classification tree model was 0.802 and 0.794 in the two sets, respectively, but were not statistically different. The calibration curves and DCAs of the two models performed well on accuracy and clinical practicality, which suggested these models may be suitable to predict HUA for rural setting.
format	article
author	Jia-Cheng Shi Xiao-Huan Chen Qiong Yang Cai-Mei Wang Qian Huang Yan-Ming Shen Jian Yu
author_facet	Jia-Cheng Shi Xiao-Huan Chen Qiong Yang Cai-Mei Wang Qian Huang Yan-Ming Shen Jian Yu
author_sort	Jia-Cheng Shi
title	A simple prediction model of hyperuricemia for use in a rural setting
title_short	A simple prediction model of hyperuricemia for use in a rural setting
title_full	A simple prediction model of hyperuricemia for use in a rural setting
title_fullStr	A simple prediction model of hyperuricemia for use in a rural setting
title_full_unstemmed	A simple prediction model of hyperuricemia for use in a rural setting
title_sort	simple prediction model of hyperuricemia for use in a rural setting
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f1010455338443cfb0e8de56cb0a645e
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A simple prediction model of hyperuricemia for use in a rural setting

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