Objective:To develop an integrated health management system based on wearable devices for conducting health management to discharged patients,so as to improve the lifestyle and medication compliance of patients with cardiovascular and cerebrovascular diseases,and control risk factors of disease,and maintain patients'safety.Methods:The wearable devices,mobile terminals,and hospital's medical information platform were systematically integrated to develop an integrated health management system.A total of 75 patients with cardiovascular and cerebrovascular diseases(coronary heart disease and hypertension)who admitted to the Department of Cardiovascular Medicine of The 940th Hospital of People's Liberation Army Joint Service Support Force during March 1 and April 1,2024 were selected,and they were randomly divided into an intervention group and a control group using the random number table method,with 38 cases in the intervention group and 37 cases in the control group.During the 6 months of intervention observation period after discharge,patients in the intervention group used the integrated health management system for self-health management,while the patients of control group were managed with the conventional mode.The rate of medication compliance,changes of health behaviors,and changes of measurement data of body between the two groups were compared after the intervention.Results:The smoking rate of patients in the intervention group was 18.42%(7/38),which was lower than 43.24%(16/37)of the control group,and the difference was statistically significant(x2=3.94,P<0.05).The average rate of medication compliance of patients in the intervention group was(89.00±2.39)%,which was higher than(84.8±2.37)%of the control group,and the difference was statistically significant(t=2.15,P<0.05).The increase in diastolic blood pressure of patients in the intervention group was(1.76±2.06)mmHg,which was lower than(3.05±1.94)mmHg of the control group,and the difference was statistically significant(t=2.49,P<0.05).Conclusion:The integrated health management system based on wearable devices is effective for the self-management of patients with cardiovascular and cerebrovascular diseases,and it has a good effect in controlling the level of blood pressure,improving behavioral habits,and enhancing medication compliance of patients.

Objective:To develop an integrated health management system based on wearable devices for conducting health management to discharged patients,so as to improve the lifestyle and medication compliance of patients with cardiovascular and cerebrovascular diseases,and control risk factors of disease,and maintain patients'safety.Methods:The wearable devices,mobile terminals,and hospital's medical information platform were systematically integrated to develop an integrated health management system.A total of 75 patients with cardiovascular and cerebrovascular diseases(coronary heart disease and hypertension)who admitted to the Department of Cardiovascular Medicine of The 940th Hospital of People's Liberation Army Joint Service Support Force during March 1 and April 1,2024 were selected,and they were randomly divided into an intervention group and a control group using the random number table method,with 38 cases in the intervention group and 37 cases in the control group.During the 6 months of intervention observation period after discharge,patients in the intervention group used the integrated health management system for self-health management,while the patients of control group were managed with the conventional mode.The rate of medication compliance,changes of health behaviors,and changes of measurement data of body between the two groups were compared after the intervention.Results:The smoking rate of patients in the intervention group was 18.42%(7/38),which was lower than 43.24%(16/37)of the control group,and the difference was statistically significant(x2=3.94,P<0.05).The average rate of medication compliance of patients in the intervention group was(89.00±2.39)%,which was higher than(84.8±2.37)%of the control group,and the difference was statistically significant(t=2.15,P<0.05).The increase in diastolic blood pressure of patients in the intervention group was(1.76±2.06)mmHg,which was lower than(3.05±1.94)mmHg of the control group,and the difference was statistically significant(t=2.49,P<0.05).Conclusion:The integrated health management system based on wearable devices is effective for the self-management of patients with cardiovascular and cerebrovascular diseases,and it has a good effect in controlling the level of blood pressure,improving behavioral habits,and enhancing medication compliance of patients.

Objective To analyze the risk factors of pulmonary infection in elderly patients with heart fail-ure with reduced left ventricular ejection fraction heart failure,and establish a risk predicting model of pulmonary infection in those patients by decision tree CART algorithm.Methods 320 elderly patients with heart failure with reduced left ventricular ejection fraction admitted from January 2020 to December 2022 were retrospectively selected as study objects,and were divided into an infection group and a non-infection group according to whether the patients were complicated with pulmonary infection.Logistic regression model and decision tree CART model were used to construct a prediction model of heart failure with reduced left ventricular ejection fraction complicated with pulmonary infection,and 5-fold cross-validation method was used for internal verification.The prediction effi-ciency of the models was compared.Results In the 320 patients,the incidence of pulmonary infection was 30.94％.The data on age,smoking history,diabetes mellitus,cardiac function grades,COPD,invasive procedures,length of hospital stay were compared between the infection and non-infection groups(P＜0.05).logistic regression analysis showed that age of ≥ 75 years smoking history,complications with diabetes or/and COPD,cardiac function gradeⅢ/Ⅳ,invasive procedures,and hospital stay of ≥ 14 days were independent risk factors for pulmonary infection in the patients(P＜0.05).Probability forecasting model P=1/[1+e(-3368+0.763*X1+0.814*X2+0.652*X3+1.05*X4+0.865*X5+1.027*X6+0.652*X7)],with an overall accurate rate of prediction of 80.9％.The Omnibus test showed P＜0.001.The accuracy of predic-tion was 73.6％after the cross-validation of 5 fold.The decision tree model showed that invasive procedures were the most important influencing factors for pulmonary infection in elderly patients with heart failure with reduced left ventricular ejection fraction,with an information gain of 0.280.The ROC showed that the AUC value of logistic regression model was slightly higher than that of the decision tree(Z=2.850,P=0.004),and the prediction efficiency of both models was medium.Conclusions Age,smoking history,complications with diabetes mellitus or/and COPD,cardiac function grades,invasive procedures,and length of hospital stay are all influencing factors for pulmonary infection in elderly patients with heart failure with reduced left ventricular ejection fraction.The deci-sion tree model constructed in this study has a better efficiency for risk prediction,and it can provide reference for early clinical screening and intervention of heart failure with reduced left ventricular ejection fraction.

Objective To analyze the risk factors of pulmonary infection in elderly patients with heart fail-ure with reduced left ventricular ejection fraction heart failure,and establish a risk predicting model of pulmonary infection in those patients by decision tree CART algorithm.Methods 320 elderly patients with heart failure with reduced left ventricular ejection fraction admitted from January 2020 to December 2022 were retrospectively selected as study objects,and were divided into an infection group and a non-infection group according to whether the patients were complicated with pulmonary infection.Logistic regression model and decision tree CART model were used to construct a prediction model of heart failure with reduced left ventricular ejection fraction complicated with pulmonary infection,and 5-fold cross-validation method was used for internal verification.The prediction effi-ciency of the models was compared.Results In the 320 patients,the incidence of pulmonary infection was 30.94％.The data on age,smoking history,diabetes mellitus,cardiac function grades,COPD,invasive procedures,length of hospital stay were compared between the infection and non-infection groups(P＜0.05).logistic regression analysis showed that age of ≥ 75 years smoking history,complications with diabetes or/and COPD,cardiac function gradeⅢ/Ⅳ,invasive procedures,and hospital stay of ≥ 14 days were independent risk factors for pulmonary infection in the patients(P＜0.05).Probability forecasting model P=1/[1+e(-3368+0.763*X1+0.814*X2+0.652*X3+1.05*X4+0.865*X5+1.027*X6+0.652*X7)],with an overall accurate rate of prediction of 80.9％.The Omnibus test showed P＜0.001.The accuracy of predic-tion was 73.6％after the cross-validation of 5 fold.The decision tree model showed that invasive procedures were the most important influencing factors for pulmonary infection in elderly patients with heart failure with reduced left ventricular ejection fraction,with an information gain of 0.280.The ROC showed that the AUC value of logistic regression model was slightly higher than that of the decision tree(Z=2.850,P=0.004),and the prediction efficiency of both models was medium.Conclusions Age,smoking history,complications with diabetes mellitus or/and COPD,cardiac function grades,invasive procedures,and length of hospital stay are all influencing factors for pulmonary infection in elderly patients with heart failure with reduced left ventricular ejection fraction.The deci-sion tree model constructed in this study has a better efficiency for risk prediction,and it can provide reference for early clinical screening and intervention of heart failure with reduced left ventricular ejection fraction.