Construction and validation of a Nomogram-based prediction model for the risk of gout in men
10.3760/cma.j.cn311282-20221020-00586
- VernacularTitle:基于Nomogram对男性痛风患病风险预测模型的构建及验证
- Author:
Yuming CHEN
1
;
Pingfei JIANG
;
Lu LIU
;
Shuang HE
;
·Tuersun XIAYIDAI
;
Zhenzhen LI
;
Lei MIAO
Author Information
1. 新疆医科大学公共卫生学院，乌鲁木齐 830000
- Keywords:
Gout;
LASSO regression;
Nomograms;
Risk model
- From:
Chinese Journal of Endocrinology and Metabolism
2023;39(4):310-314
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the risk factors of gout and establish a columnar graph model to predict the risk of gout development.Methods:A total of 1 032 Han Chinese men attending the Affiliated Hospital of Traditional Chinese Medicine of Xinjiang Medical University, People′s Hospital of Xinjiang Uygur Autonomous Region, and the First Affiliated Hospital of Xinjiang Medical University from 2018 to 2020 were selected as study subjects and divided into training set(722 cases)and validation set(310 cases)by simple random sampling method in the ratio of 7∶3. General information and biochemical indices of the subjects were collected. The collected information was used to assess the risk of gout prevalence. LASSO regression analysis of R Studio software was used to screen the best predictors, and was introduced to construct a column line graph model for predicting gout risk using receiver operating characteristic(ROC)curves, and the Hosmer-Lemeshow test was used to assess the discrimination and calibration of the column line graph model. Finally, decision curve analysis(DCA)was performed using the rmda program package to assess the clinical utility of the model in validation data.Results:Age, uric acid, body mass index, total cholesterol, and waist-to-hip ratio were risk factors for gout( P<0.05). The column line graph prediction model based on the above five independent risk factors had good discrimination(AUC value: 0.923 for training set validation and 0.922 for validation set validation)and accuracy(Hosmer-Lemeshow test: P>0.05 for validation set validation); decision curve analysis showed that the prediction model curve had clinical practical value. Conclusion:The nomogram model established by combining age, uric acid, body mass index, total cholesterol, and waist-to-hip ratio indicators can predict the risk of gout more accurately.
