Objective To optimize the processing parameters for the extraction of chlorogenic acid in Fule Granula by Box-Behnken experimental design.Methods The content of chlorogenic acid in Fule Granula was determination.The separation was performed on a Agilent ZORBAX Eclipse XDB C18 column(4.6 mm ×250 mm, 5 μm), and column temperature 30 ℃.Aetonitrile-0.2% H3PO4 was used as the mobile phase.The detection wavelength was at 327 nm.A three-factor and three-level Box-Behnken experimental design was employed to investigate effects of extraction time, solid-liquid ratio and extraction times on composite score of extracting amount of chlorogenic acid.Results Optimum process conditions were as follows:12 times the amount of water extracted three times, each time 60 min; chlorogenic acid extraction rate of 3.379%.Conclusion This optimized extraction technology has good predictability by Box-Behnken experimental design, and overall desirability, it provides a reference for application of Fule Granula.

Objective To discuss the application of day surgery management mode in patients with cardiovascular interventional treatment, evaluate the safety of day surgery and its effects on the average in-hospital days and expenses. Methods A total of 1 578 patients who received cardiovascular interventional therapy in the Department of Cardiology in Ningbo First Hospital from January 2015 to December 2016 were included in the study. They were randomly divided into the observation group and the control group, 789 cases respectively. Patients in the control group received normal in-hospital operation mode, while patients in the observation group used day surgery mode. The safety, complications, average in-hospital days and expenses were compared between two groups. Results The average in-hospital days and expenses were 1 d and 3 083 Yuan in the observation group; in the control group, average in-hospital days and expenses were (5.34±0.73) d and (3 713.70±21.06) Yuan, which were significantly higher than those in the observation group (P<0.05). There were 5 cases of bleeding in the puncture site of the radial artery, 9 cases of forearm hematoma, and 1 case of Iodine allergic reaction with a complication incidence of 1.9% in the control group. In the observation group, there were 3 cases of bleeding in the puncture site of the radial artery, 6 cases of forearm hematoma, and 2 cases of Iodine allergic reaction with a complication incidence of 1.4%. No significant difference in the complications of patients was observed between two groups (P>0.05). Conclusions The day surgery mode of patients with cardiovascular interventional treatment, which can ensure the therapeutic effect and the safety of patients, is feasible and can reduce the average in-hospital days and expenses.

Objectives:To evaluate and predict the risk for in-hospital mortality in elderly patients receiving cardiac valvular surgery preferably,we developed a new prediction model using machine learning. Methods:Clinical data including baseline characteristics,peri-operative data and primary endpoint of 7 163 elderly patients aged 65 years or older undergoing cardiac valvular surgery from January 2016 to December 2018 from 87 hospitals were collected from the Chinese Cardiac Surgery Registry(CCSR).Patients from January 2016 to June 2018 were assigened to the training cohort(n=5 774)and patients from July to December 2018 were assigened to the validation cohort(n=1 389).The primary endpoint was in-hospital mortality.Machine learning algorithms were used to analyze risk factors and develop prediction model. Results:Overall in-hospital mortality was 4.1%.Linear discriminant analysis(LDA),support vector classification(SVC)and logistic regression(LR)models in the training cohort all have high AUCs and low Brier scores,with good discrimination and calibration.In validation cohort,the AUC of LDA,SVC and LR were 0.744,0.744 and 0.746 respectively,which were significantly better than that of 0.642 using the European System for Cardiac Operative Risk Evaluation II(EuroSCORE II)model(P<0.05). Conclusions:The mortality rate for elderly patients undergoing cardiac valvular surgery is relatively high.LDA,SVC and LR can predict the risk for in-hospital mortality in elderly patients receiving cardiac valvular surgery with high accuracy.

Objective     To evaluate the risk factors for postoperative in-hospital mortality in elderly patients receiving cardiac valvular surgery, and develop a new prediction models using the least absolute shrinkage and selection operator (LASSO)-logistic regression. Methods     The patients≥65 years who underwent cardiac valvular surgery from 2016 to 2018 were collected from the Chinese Cardiac Surgery Registry (CCSR). The patients who received the surgery from January 2016 to June 2018 were allocated to a training set, and the patients who received the surgery from July to December 2018 were allocated to a testing set. The risk factors for postoperative mortality were analyzed and a LASSO-logistic regression prediction model was developed and compared with the EuroSCOREⅡ. Results     A total of 7 163 patients were collected in this study, including 3 939 males and 3 224 females, with a mean age of 69.8±4.5 years. There were 5 774 patients in the training set and 1 389 patients in the testing set. Overall, the in-hospital mortality was 4.0% (290/7 163). The final LASSO-logistic regression model included 7 risk factors: age, preoperative left ventricular ejection fraction, combined coronary artery bypass grafting, creatinine clearance rate, cardiopulmonary bypass time, New York Heart Association cardiac classification. LASSO-logistic regression had a satisfying discrimination and calibration in both training [area under the curve (AUC)=0.785, 0.627] and testing cohorts (AUC=0.739, 0.642), which was superior to EuroSCOREⅡ. Conclusion     The mortality rate for elderly patients undergoing cardiac valvular surgery is relatively high. LASSO-logistic regression model can predict the risk of in-hospital mortality in elderly patients receiving cardiac valvular surgery.