A preoperative mortality risk assessment model for Stanford type A acute aortic dissection

Abstract

Abstract Background Acute aortic dissection type A is a life-threatening disease required emergency surgery during acute phase. Different clinical manifestations, laboratory tests, and imaging features of patients with acute aortic dissection type A are the risk factors of preoperative mortality. This study aims to establish a simple and effective preoperative mortality risk assessment model for patients with acute aortic dissection type A. Methods A total of 673 Chinese patients with acute aortic dissection type A who were admitted to our hospital were retrospectively included. All patients were unable to receive surgically treatment within 3 days from the onset of disease. The patients included were divided into the survivor and deceased groups, and the endpoint event was preoperative death. Multivariable analysis was used to investigate predictors of preoperative mortality and to develop a prediction model. Results Among the 673 patients, 527 patients survived (78.31%) and 146 patients died (21.69%). The developmental dataset had 505 patients, calibration by Hosmer Lemeshow was significant (χ2 = 3.260, df = 8, P = 0.917) and discrimination by area under ROC curve was 0.8448 (95% CI 0.8007–0.8888). The validation dataset had 168 patients, calibration was significant (χ2 = 5.500, df = 8, P = 0.703) and the area under the ROC curve was 0.8086 (95% CI 0.7291–0.8881). The following independent variables increased preoperative mortality: age (OR = 1.008, P = 0.510), abrupt chest pain (OR = 3.534, P < 0.001), lactic in arterial blood gas ≥ 3 mmol/L (OR = 3.636, P < 0.001), inotropic support (OR = 8.615, P < 0.001), electrocardiographic myocardial ischemia (OR = 3.300, P = 0.001), innominate artery involvement (OR = 1.625, P = 0.104), right common carotid artery involvement (OR = 3.487, P = 0.001), superior mesenteric artery involvement (OR = 2.651, P = 0.001), false lumen / true lumen of ascending aorta ≥ 0.75 (OR = 2.221, P = 0.007). Our data suggest that a simple and effective preoperative death risk assessment model has been established. Conclusions Using a simple and effective risk assessment model can help clinicians quickly identify high-risk patients and make appropriate medical decisions.