International journal of cardiology · 2020
Prediction of adverse events in patients with initially medically treated type A intramural hematoma.
СтатьяНазвание и аннотация: Перевод в очереди
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Li Zhennan, Chen Yuan, Guo Junxia, Zhang Yan, Hou Zhihui, An Yunqiang, Gao Yang, Lu Bin
Аннотация
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.
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.