Study on risk prediction model of hyperuricemia in adult health physical examination population in Beijing
Objective To establish a risk prediction model of hyperuricemia(HUA)in adult health physical examination population in Beijing.Methods A total of 7 836 health check-up adults from the national key research and development plan"construction of big data cloud platform for health examination"in Haidian district,Fengtai district and Chaoyang district of Beijing from 2017 to 2022 were selected,and the basic information,uric acid and related health check-up data of the subjects were collected.Taking the onset time and occurrence of HUA as dependent variables,and Cox proportional hazard regression was used to construct the risk prediction model of HUA,and Bootstrap 500-time repeated sampling method was used to verify the model internally,the ROC curve was used to evaluate the distinguishing ability,and the ten-fold cross was used to verify the calibration ability of the model.Results Among the 7 836 subjects,4 013 were males and 3 823 were females,aged from 18 to 89 years,with an average age of(43.0±12.8)years.The follow-up time was 4.89(2.00±5.02)years,during which 3 694 subjects(47.14%)developed HUA,and the incidence density was 126.32/1000 person-years.Cox regression analysis showed that gender,age,BMI,SBP,LDL-C,TC,TG,BUN,UA and FPG were the predictors of the risk of HUA,and the AUC of ROC curve was 0.842(95%CI:0.834-0.851,P<0.05),and the average AUC was 0.841(95%CI:0.832-0.849,P<0.05),and the predicted probability of HUA occurrence was consistent with the actual observation probability.Conclusions Based on the data of health physical examination,the risk predication model of HUA in adult health physical examination population in Beijing is effective.Men aged from 20 to 40 years,women aged more than 50 years,population with BMI exceeding the standard,abnormal BP,higher blood glucose and dyslipidemia are high-risk group of HUA.It is suggested that health education should be carried out for high-risk groups,routine monitoring of HUA and targeted intervention should be carried out to reduce and prevent the health damage caused by HUA.
万方数据
维普
