The Risk Prediction Model Of Early Renal Damage In Essential Hypertension

Background:Many risk factors for hypertensive renal damage have been widely reported,but a risk prediction model for renal impairment in hypertensive patients has not yet been established.The purpose of this study is to establish a comprehensive risk assessment model of renal damage in hypertensive patients by logistic regression analysis based on principal component analysis,which provides a theoretical basis for clinicians to take individualized treatment for patients with different risk factors.Methods:We analyzed the data of 582 Chinese hypertensive patients from January 2014 to December 2016.The average age was(58 ± 13)years,348 males(60%)and 234 females(40%).According to the albumin-to-creatinine ratio,the subjects were divided into a hypertension with renal damage group(systolic blood pressure>140 mmHg and/or diastolic blood pressure>90 mmHg with UACR>30 mg/g)and a hypertension without renal damage(group systolic BP>140 mmHg and/or diastolic BP>90 mmHg with UACR<30 mg/g).The basic information and clinical indexes were collected.Eleven risk factors were screened by one-way ANOVA.Logistic regression analysis based on principal component analysis of the 11 risk factors was used to establish the prediction model.The area under the ROC curve was calculated to evaluate the predictive efficacy.Result:After single-factor analysis of variance,a total of 11 indicators and hypertensive renal damage were significantly correlated(P<0.05),respectively,sex,age,smoking history,drinking history,history of coronary heart disease,history of diabetes,Cystatin-C,β2-microglobulin,C-reactive protein,type of blood pressure,renal artery resistance index.The 11 indicators were significantly collinear,which seriously affected the stability and predictive power of the predictive model.Therefore,the Logistic regression analysis based on principal component analysis was used to establish the predictive model.The total contribution rate of the six principal component factors extracted by principal component analysis is as high as 81.7%.The area under the ROC curve is 0.735 and the model has good predictive power.Conclusions and significance:Eleven indicators included gender,age,smoking history,drinking history,history of coronary heart disease,history of diabetes,Cystatin-C,β2-microglobulin,C-reactive protein,are the risk factors of renal damage.The predictive model established by Logistic regression analysis based on principal component analysis has a good predictive performance,and can be used to comprehensively assess the risk of renal damage in hypertensive patients.