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 systematically evaluate the clinical efficacy of compound α-ketoacid tablets in the treatment of diabetic kidney disease (DKD).Methods:CNKI, Wanfang database, EMBASE, PubMed and Cochrane Library database were searched for eligible records published from the establishment of individual database to November 13 th, 2022. The quality of the included studies were assessed, data were extracted, and meta-analysis was conducted using RevMan5.3. Results:A total of 26 randomized controlled trials were included, with a total of 2 790 DKD patients (1 465 in the experimental group and 1 325 in the control group). Multiple parameters were significantly improved in the experimental group compared with the control group, including 24-hour urinary protein, blood creatinine, urea nitrogen, nutritional index, oxidative stress level, fasting blood glucose, glycated hemoglobin, homocysteine, HGF, VEGF, TGF-β1, and systolic blood pressure.Conclusions:Limited low-quality evidence showed that compound α-ketoacid tablets combined with low-protein diet may be related to the improved 24-hour urinary protein, renal function, and glucose metabolism in patients with DKD. Due to the lack of randomized controlled trials designed for respective stages of DKD, the inclusion criteria of our study were relatively general, possibly leading to the lack of pertinence of the results. Some indicators showed apparent heterogeneity among different groups, and more high-quality multi-center studies with large sample sizes are still needed to verify our findings.