A simple model predicting in-hospital death in patients with type A acute aortic dissection

Abstract

Background:

Type A acute aortic dissection (TAAAD) is a destructive cardiovascular disease, with high morbidity and mortality rates. Identifying the high-risk TAAAD patients at an early stage is urgently necessary.

Methods:

A retrospective study of 160 patients was carried out. The admission data were retrospectively gathered. Logistic regression analysis and receiver operator characteristic curve (AUC) was utilized.

Results:

Compared with the survivor group, the nonsurvivor group was older, had higher D-dimer levels, red blood cell distribution width (RDW) levels and platelet distribution width (PDW) levels, and lower fibrinogen levels, platelet levels and plateletcrit levels. Multivariate analysis displayed that four independent factors, age (hazard ratio (HR): 7.877, 95% confidence interval (CI) 2.740–22.641, p < 0.001), D-dimer (HR: 3.791, 95% CI 1.520–9.452, p = 0.004), RDW (HR: 3.300, 95% CI 1.109–9.825, p = 0.032), PDW (HR: 3.755, 95% CI 1.436–9.815, p = 0.007) were incorporated into the model. The predict accuracy of the model (AUC 0.861, 95% CI 0.798–0.911, p < 0.001) was best.

Conclusions:

Age, D-dimer, RDW and PDW are independent markers of in-hospital death in TAAAD patients and the newly established model has better performance in predicting high-risk patients. This model can be used as a quick screening tool to assess the prognosis of patients in individualizing.