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Published on: June 18, 2021
Prediction of adverse events in patients with initially medically treated type A intramural hematoma
Zhennan Li1, Yuan Chen2, Junxia Guo3
1Heart Center of Henan Provincial People's Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, Henan 450003, People's Republic of China; Department of Radiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, People's Republic of China.
Insights
Predictive models using clinical and CT data can estimate adverse aorta-related events in patients with type A intramural hematoma (IMH). These models aid in risk assessment and clinical decision-making for IMH management.
Area of Science:
- Cardiology
- Radiology
- Vascular Surgery
Background:
- Limited data exists on the natural history of medically treated type A intramural hematoma (IMH).
- Understanding long-term outcomes is crucial for effective patient management.
Purpose of the Study:
- To develop predictive models for adverse aorta-related events in patients with type A IMH.
- To identify key clinical and CT characteristics associated with adverse outcomes.
Main Methods:
- Retrospective pooled analysis of individual patient data.
- Inclusion of baseline clinical and computed tomography (CT) characteristics.
- Follow-up for adverse aorta-related events (aortic death or complications requiring invasive treatment).
Main Results:
- 60 out of 172 patients (34.9%) experienced adverse aorta-related events during follow-up.
- Hypertension, maximum aortic diameter (MAD), ulcer-like projections (ULP), and pericardial effusion were associated with adverse events.
- MAD ≥ 50.7 mm and ULP were independent predictors of 90-day aortic events.
Conclusions:
- Predictive models incorporating clinical and CT data accurately estimate the risk of adverse aorta-related events in type A IMH.
- These models can assist in risk stratification and inform clinical decision-making for patients with type A IMH.
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.
