Objective To investigate predictors for mortality among patients with Stanford type A acute aortic dissection (AAD) and to establish a predictive model to estimate risk of in-hospital mortality. Methods A total of 999 patients with Stanford type A AAD enrolled between 2010 and 2015 in our hospital were included for analysis. There were 745 males and 254 females with a mean age of 49.8±12.0 years. There were 837 patients with acute dissection and 182 patients (18.22%) were preoperatively treated or waiting for surgery in the emergency department and 817 (81.78%) were surgically treated. Multivariable logistic regression analysis was used to investigate predictors of in-hospital mortality. Significant risk factors for in-hospital death were used to develop a prediction model. Results The overall in-hospital mortality was 25.93%. In the multivariable analysis, the following variables were associated with increased in-hospital mortality: increased age (OR=1.04, 95% CI 1.02 to 1.05, P<0.000 1), acute aortic dissection (OR=2.49, 95% CI 1.30 to 4.77, P=0.006 1), syncope (OR=2.76, 95% CI 1.15 to 6.60, P=0.022 8), lower limbs numbness/pain (OR=7.99, 95% CI 2.71 to 23.52, P=0.000 2), type Ⅰ DeBakey dissection (OR=1.72, 95% CI 1.05 to 2.80, P=0.030 5), brachiocephalic vessels involvement (OR=2.25, 95% CI 1.20 to 4.24, P=0.011 7), acute liver insufficiency (OR=2.60, 95% CI 1.46 to 4.64, P=0.001 2), white blood cell count (WBC)>15×109 cells/L (OR=1.87, 95% CI 1.21 to 2.89, P=0.004 9) and massive pericardial effusion (OR=4.34, 95% CI 2.45 to 7.69, P<0.000 1). Based on these multivariable results, a reliable and simple bedside risk prediction tool was developed. Conclusion Different clinical manifestations and imaging features of patients with Stanford type A AAD predict the risk of in-hospital mortality. This model can be used to assist physicians to quickly identify high risk patients and to make reasonable treatment decisions.
ObjectiveTo explore a method for establishing a priority-scoring model for thyroid carcinoma patient admission. MethodsA questionnaire survey was conducted among specialists and outpatients in the thyroid surgery department of the hospital. The weight coefficient of the index factors was calculated to establish the priority-scoring mode by the analytic hierarchy process. The differences in results between specialists and patients were compared. The logical rationality of the model index was tested. ResultsA priority-scoring model for thyroid carcinoma surgery admission was established, including 10 first-level indicators, such as sex, age, cancer type and TNM stage. The weight coefficients of the indicators from high to low were cancer type (0.137), TNM stage (0.134), tumor size (0.127), tumor invasion degree (0.126), tumor invasion site (0.124), relationship between tumor and capsule (0.111), age (0.093), sex (0.061), place of residence (0.05) and medical insurance type (0.035). After the total ratio test, the model CR value was 0.0073, and the model index was highly rational. ConclusionThis study successfully establish a priority-scoring model for thyroid carcinoma surgery admission, which can provide references and a basis for tiered medical services and relevant researches in the future.