Construction of a Risk Prediction Model for Hyperuricemia in Adults based on Nomogram
Objective To construct a nomogram for predicting the risk of hyperuricemia(HUA)and to provide evi-dence for early prevention and intervention of hyperuricemia(HUA).Methods The physical examination data and li-festyle data of 1 865 subjects were collected from the health management center of a tertiary hospital in Shiyan city,Hubei Province.The subjects were randomly divided into training set and validation set.Lasso regression method was used to select variables to construct a nomogram model for HUA risk stratification.The predictive performance of the nomogram was evaluated by discrimination,calibration,clinical decision curve and Bootstrap method.Results Nine variables(gender,age,soybean intake,central obesity,BMI,LDLC,HDLC,TG,ALT)were selected from Lasso regression to construct the web version of HUA nomogram.Then,the risk of HUA was divided into low risk(inci-dence＜8.29％),moderate risk(incidence＜17.56％),high risk(incidence＜30.81％),and extremely high risk(inci-dence≥30.81％).The AUC of the prediction model in the training set and validation set were 0.749 and 0.741,re-spectively.The Bootstrap method was used for internal validation of the model,and the AUC was 0.791.The calibra-tion curve showed good consistency.The clinical decision curve(DCA)showed that when the threshold probability of the training set and the validation set were in the range of 5％～70％and 5％～65％,respectively,the intervention of HUA patients could produce net benefits.Conclusion A more accurate risk prediction model for HUA was estab-lished in order to identify high-risk groups of HUA,providing a basis for early prevention and intervention of HUA.
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