Prediction of adverse events in patients with initially medically treated type A intramural hematoma. (15th August 2020) - Li, Zhennan; Chen, Yuan; Guo, Junxia; Zhang, Yan; Hou, Zhihui; An, Yunqiang; Gao, Yang; Lu, Bin
Abstract: Background: Prior studies provided limited data regarding natural history of initially medically treated type A intramural hematoma (IMH). Objectives: To develop predictive models for adverse aorta-related events in patients with type A IMH. Methods: We performed a retrospective pooled analysis of individual patient data, including baseline clinical and CT characteristics. All patients enrolled were followed up for adverse aorta-related events, defined as a composite of aortic disease-related death and the presence of aortic complications that required aortic invasive treatment. Results: A total of 172 patients (52.9% men) were included, with a mean age of 61.1 ± 11.2 years. During a median follow-up time of 770.5 (45.3–1695.8) days, 60 patients (34.9%) experienced adverse aorta-related events. In Cox regression model for predicting adverse aorta-related events, hypertension (HR = 3.78, p = .067), MAD (HR = 1.05, p = .018), presence of ULP (HR = 2.43, p = .002) and pericardial effusion (HR = 1.65, p = .061) were independently associated with adverse aorta-related events. A majority of the adverse aorta-related events ( n = 46, 76.7%) occurred within acute and subacute phase (90 days) of IMH. In predictive model for 90 days aortic events, MAD≥50.7 mm (OR = 2.79, p = .006) and presence of ULP (OR = 3.20, p = .002) were independent predictors. C statistic of the predictive model were 0.71 ( p < .001). Conclusions: Predictive models including baseline clinical and CT characteristics as predictors allow for accurate estimation of risk of adverse aorta-related events in patients with type A IMH. The proposed predictive models are helpful for risk estimates and decision making. Highlights: Patients with ULP and larger aortic diameter are more likely to suffer adverse events. Clinical and CT predictors allow for accurate estimation of risk of adverse events. The proposed predictive models are helpful for risk estimates and decision making.