OBJECTIVE To construct a prediction model of mild cognitive impairment(MCI)in elderly patients with hyperten-sion based on the bi-dimensional features of Chinese and western medicine with the help of machine learning(ML).METHODS The clinical data of 502 patients over 60 years old with essential hypertension treated in hospital from January 2020 to March 2023 were collected and analyzed,randomly divided into training set and verification set according to a ratio of 7∶3,and divided into cognitive impairment group(n=104)and cognitive normal group(n=398).LASSO regression analysis was used to reduce the dimension of clin-ical indicator data and screen out the core predictors.Six ML algorithms,logistic regression,XGBoost,AdaBoost,SVM,GNB,and MLP were used to construct the models,and ROC curves were plotted to compare the AUC,accuracy,sensitivity,specificity,and F1 scores of the 6 models.SHAP models were adopted to reveal the characteristic importance of predictors.RESULTS Waist-hip ratio,qi depression,age,total cholesterol,phlegm-dampness,damp-heat,qi deficiency and fasting blood glucose were the core predictors of early MCI in elderly hypertensive patients.The AUC,accuracy,sensitivity,specificity,and F1 scores of the XGBoost model were 0.938,0.885,0.846,0.896,and 0.755 respectively,which were superior to those of other algorithmic models.CONCLUSION The XGBoost model constructed on the basis of waist-to-hip ratio,qi depression,age,total cholesterol,phlegm-dampness,damp-heat,qi deficiency and fasting blood glucose has the best prediction performance,which can provide a reference basis for early identifi-cation of MCI risk and diagnostic and therapeutic decision-making in the clinical elderly hypertensive population.

OBJECTIVE To construct a prediction model of mild cognitive impairment(MCI)in elderly patients with hyperten-sion based on the bi-dimensional features of Chinese and western medicine with the help of machine learning(ML).METHODS The clinical data of 502 patients over 60 years old with essential hypertension treated in hospital from January 2020 to March 2023 were collected and analyzed,randomly divided into training set and verification set according to a ratio of 7∶3,and divided into cognitive impairment group(n=104)and cognitive normal group(n=398).LASSO regression analysis was used to reduce the dimension of clin-ical indicator data and screen out the core predictors.Six ML algorithms,logistic regression,XGBoost,AdaBoost,SVM,GNB,and MLP were used to construct the models,and ROC curves were plotted to compare the AUC,accuracy,sensitivity,specificity,and F1 scores of the 6 models.SHAP models were adopted to reveal the characteristic importance of predictors.RESULTS Waist-hip ratio,qi depression,age,total cholesterol,phlegm-dampness,damp-heat,qi deficiency and fasting blood glucose were the core predictors of early MCI in elderly hypertensive patients.The AUC,accuracy,sensitivity,specificity,and F1 scores of the XGBoost model were 0.938,0.885,0.846,0.896,and 0.755 respectively,which were superior to those of other algorithmic models.CONCLUSION The XGBoost model constructed on the basis of waist-to-hip ratio,qi depression,age,total cholesterol,phlegm-dampness,damp-heat,qi deficiency and fasting blood glucose has the best prediction performance,which can provide a reference basis for early identifi-cation of MCI risk and diagnostic and therapeutic decision-making in the clinical elderly hypertensive population.

Objective To investigate the clinical significance of antinucleosome antibody (AnuA) in the patients with lupus erythematosus (LE) through detecting the serum level of AnuA in different types of LE patients before and after treatment. Methods Enzyme-linked immunosorbent assay(ELISA) was used to detect the serum level of AnuA in 31 SLE patients, 26 SCLE patients, 7 DLE patients, 6 scleroderma patients, 5 dermatomyositis patients and 30 healthy controls. Among them, the serum levels of AnuA before and after treatment were detected in 15 SLE patients and 10 SCLE patients. Clinical data, laboratory test results and the scores of SLE disease activity index (SLEDAI) were analyzed with SPSS11.0 software. Results The serum levels of AnuA in SLE patients were higher than those in SCLE, DLE, scleroderma, dermatomyositis patients and healthy controls(P