A simple bedside model to predict the risk of in-hospital mortality in Stanford type A acute aortic dissection / 中国胸心血管外科临床杂志

ABSTRACT

@#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.