A Model to Predict in-Hospital Death in Patients with Type B Acute Aortic Dissection: A Single-Center Retrospective Observational Study.

Abstract

Acute type B aortic dissection (BAAD), as a catastrophic disease, is linked to high morbidity and mortality. The current research is to create a simple risk model to predict in-hospital mortality in BAAD patients based on laboratory results. Patients with BAAD were included from April 1, 2017, to November 30, 2019, in the hospital. Clinical features and laboratory results were collected. Logistic regression analyses and ROC were applied to the evaluation. Hemoglobin (HB) (114.88 ± 28.42 (nonsurvivor) vs. 134.95 ± 17.88 (survivor), P < 0.001) and UREA (10.93 ± 7.02 (nonsurvivor) vs. 7.17 ± 3.77 (survivor), P = 0.001) were significantly different. In multivariate analysis, HB (hazard ratio (HR): 0.124; 95% confidence interval (CI) 0.025 - 0.627; P = 0.012) and UREA (HR: 8.765; 95% CI 2.022 - 37.993; P = 0.004) were independent predictors of in-hospital death. Then, a model with good performance (AUC 0.761 (0.677 - 0.832) was developed. A simple model with good prediction value was developed. With this model, physicians quickly can identify high-risk patients, determine the best treatment strategies, and improve prognosis.