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 clinical and follow-up results of the surgical treatment for hypertrophic obstructive cardiomyopathy associated with aortic stenosis. Methods     We retrospectively analyzed the clinical data of the patients with hypertrophic obstructive cardiomyopathy plus aortic stenosis in our hospital from February 2008 to October 2015. There were 4 males and 3 females aged 55.6 ± 7.5 years. All the patients were received concomitant aortic valvulopasty at the time of modified extended Morrow procedure. Echocardiographic data and major complications were recorded through the outpatient clinic and telephone. Results     The postoperative ventricular septal thickness, left ventricular outflow tract gradient and aortic gradient were significantly lower than those in preoperation with statistical differences (P<0.05). During the mean follow-up 25.6 ± 28.2 months period, 1 patient died of cerebral hemorrhage, 1 patient was implanted a permanent pacemaker, and 1 patient had a postoperative new-onset atrial fibrillation. All patients had a satisfied prosthetic valve function and the left ventricular outflow tract gradient. The patient's symptoms and heart function significantly improved postoperatively. Conclusion     For patients with hypertrophic obstructive cardiomyopathy associated with moderate to severe aortic stenosis, concomitant aortic valvulopasty at the time of modified extended Morrow procedure is an appropriate and effective treatment, which can significantly alleviate the clinical symptoms, and improve quality of life with a satisfied prognosis.

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.