Objective To analyze the usual death causes of cardiovascular disease and the differences in gender and age .Methods By adopting the retrospective study method ,the clinical data of death cases in cardiovascular disease were collected and analyze on the situation suffering from cardiovascular disease ,direct death causes ,gender and age difference .Results (1) among 181 cases of cardiovascular disease death ,coronary heart disease(115/181 ,64% ) and hypertension(96/181 ,53% ) were the most common dis-ease ,lung infection(104/181 ,57% ) was the most common complication ;(2)There was no significant difference in the situation suf-fering from basic diseases between male and female(P>0 .05);(3)The basic diseases in cardiovascular death cases aged over 60 years old were dominated by coronary heart disease and hypertension ;the proportion of complicating pulmonary infection was grad-ually increased with age increase ;(4) in the direct death causes ,the top 3 places were sudden cardiac death (44/181 ,24 .3% ) ,multi-ple organ dysfunction syndrome(24/181 ,13 .3% ) and cardiogenic shock(24/181 ,13 .3% ) .Conclusion Strengthening the manage-ment of diagnosis and treatment on elderly patients with coronary heart disease ,hypertension ,especially those complicating diabe-tes ,strengthening the treatment intervention of lung infection in cardiovascular disease population and conducting the emphasis pro-tection on the target organ function may reduce the mortality of cardiovascular inpatients .

Objective:To investigate the value of a preoperatively MRI-based deep learning (DL) radiomics machine learning model to distinguish low-grade and high-grade soft tissue sarcomas (STS).Methods:From November 2007 to May 2019, 151 patients with STS confirmed by pathology in the Affiliated Hospital of Qingdao University were enrolled as training sets, and 131 patients in the Affiliated Hospital of Shandong First Medical University and the Third Hospital of Hebei Medical University were enrolled as external validation sets. According to the French Federation Nationale des Centres de Lutte Contre le Cancer classification (FNCLCC) system, 161 patients with FNCLCC grades Ⅰ and Ⅱ were defined as low-grade and 121 patients with grade Ⅲ were defined as high-grade. The hand-crafted radiomic (HCR) and DL radiomic features of the lesions were extracted respectively. Based on HCR features, DL features, and HCR-DL combined features, respectively, three machine-learning models were established by decision tree, logistic regression, and support vector machine (SVM) classifiers. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each machine learning model and choose the best one. The univariate and multivariate logistic regression were used to establish a clinical-imaging factors model based on demographics and MRI findings. The nomogram was established by combining the optimal radiomics model and the clinical-imaging model. The AUC was used to evaluate the performance of each model and the DeLong test was used for comparison of AUC between every two models. The Kaplan-Meier survival curve and log-rank test were used to evaluate the performance of the optimal machine learning model in the risk stratification of progression free survival (PFS) in STS patients.Results:The SVM radiomics model based on HCR-DL combined features had the optimal predicting power with AUC values of 0.931(95%CI 0.889-0.973) in the training set and 0.951 (95%CI 0.904-0.997) in the validation set. The AUC values of the clinical-imaging model were 0.795 (95%CI 0.724-0.867) and 0.615 (95%CI 0.510-0.720), and of the nomogram was 0.875 (95%CI 0.818-0.932) and 0.786 (95%CI 0.701-0.872) in the training and validation sets, respectively. In validation set, the performance of SVM radiomics model was better than those of the nomogram and clinical-imaging models ( Z=3.16, 6.07; P=0.002,<0.001). Using the optimal radiomics model, there was statistically significant in PFS between the high and low risk groups of STS patients (training sets: χ2=43.50, P<0.001; validation sets: χ2=70.50, P<0.001). Conclusion:Preoperative MRI-based DL radiomics machine learning model has accurate prediction performance in differentiating the histopathological grading of STS. The SVM radiomics model based on HCR-DL combined features has the optimal predicting power and was expected to undergo risk stratification of prognosis in STS patients.

Objective To investigate the risk factors for biliary stricture within two years after orthotopic liver transplantation,and analyze the survival.Methods A retrospective analysis was performed for the data of 495 patients who underwent liver transplantation at Liver Transplantation Center of The First Hospital of Jilin University from January 2014 to January 2022,and according to the presence or absence of biliary stricture within two years after liver transplantation,the 495 patients were divided into stricture group with 89 patients and non-stricture group with 406 patients.The risk factors for biliary stricture and prognosis were analyzed.The independent-samples t-test was used for comparison of normally distributed continuous data between two groups,and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups;the chi-square test was used for comparison of categorical data between two groups.Univariate and multivariate Cox regression analyses were used for the analysis of risk factors,and the Kaplan-Meier method was used for survival analysis.Results Recipient sex(hazard ratio[HR]=1.808,95%confidence interval[CI]:1.055-3.098,P=0.031),preoperative total bilirubin of the recipient(HR=1.002,95%CI:1.001-1.003,P=0.001),cold ischemia time(HR=1.003,95%CI:1.001-1.005,P=0.007),history of abdominal surgery for the recipient(HR=3.851,95%CI:2.273-6.524,P<0.001),and mismatch of donor-recipient bile ducts(HR=1.962,95%CI:1.041-3.698,P=0.037)were identified as independent risk factors for biliary stricture within two years after transplantation.The median follow-up time was 4.09 years,and the 1-,3-,and 5-year survival rates were 92.7%,80.5%,and 75.4%,respectively,after liver transplantation.The onset of biliary stricture within two years after liver transplantation had no significant impact on the survival of patients undergoing orthotopic liver transplantation.Conclusion Recipient sex,preoperative total bilirubin of the recipient,cold ischemia time,history of abdominal surgery for the recipient,and mismatch of donor-recipient bile ducts are independent risk factors for biliary stricture within two years after transplantation.The onset of biliary stricture within two years after transplantation does not affect the survival time of patients undergoing orthotopic liver transplantation.