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Computed Tomography Imaging Features in Acute Uncomplicated Stanford Type-B Aortic Dissection Predict Late Adverse
Anna M Sailer1, Sander M J van Kuijk1, Patricia J Nelemans1
1From the Department of Radiology (A.M.S., A.S.C., A.K., K.B., D.F.), Department of Cardiothoracic Surgery (P.C., M.P.F., M.D.D., D.C.M.), and the Stanford Cardiovascular Institute (M.D.D., D.F.), Stanford University School of Medicine, CA; Department of Radiology (A.M.S.), Department of Clinical Epidemiology and Medical Technology Assessment (S.M.J.v.K.), Department of Epidemiology (P.J.N.), and Department of Vascular Surgery (M.H., G.W.H.S.), Maastricht University Medical Center, The Netherlands; Institute of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany (G.M.); and the Institute for Computer Graphics, Vienna University of Technology, Austria (J.S., G.M.).
Insights
Identifying patients at high risk for late adverse events after acute Stanford type-B aortic dissection is crucial. A new prediction model using computed tomography imaging features can help stratify patients for timely intervention.
Area of Science:
- Cardiovascular Surgery
- Radiology
- Medical Imaging
Background:
- Medical management of uncomplicated Stanford type-B aortic dissection has a high rate of late adverse events.
- Identifying patients who could benefit from preventive endografting is a clinical priority.
Purpose of the Study:
- To identify computed tomography (CT) imaging features associated with late adverse events in patients with acute uncomplicated Stanford type-B aortic dissection.
- To develop a predictive model for stratifying patient risk.
Main Methods:
- Retrospective assessment of CT imaging features in 83 patients with acute uncomplicated Stanford type-B aortic dissection.
- Multivariable Cox regression analysis to identify significant predictors of adverse events.
- Development and internal validation of a risk prediction model.
Main Results:
- Five significant predictors of late adverse events were identified: connective tissue disease, false lumen extent, maximum aortic diameter, false lumen outflow, and number of intercostal arteries.
- A prediction model was developed to calculate patient-specific risk at 1, 2, and 5 years.
- The model demonstrated good discriminatory ability with a C statistic of 70.1%.
Conclusions:
- CT imaging-based morphological features can be integrated into a prediction model.
- This model shows potential for identifying high-risk patients with uncomplicated type-B aortic dissection who may benefit from closer monitoring or intervention.
Background:
Medical treatment of initially uncomplicated acute Stanford type-B aortic dissection is associated with a high rate of late adverse events. Identification of individuals who potentially benefit from preventive endografting is highly desirable.
Methods And Results:
The association of computed tomography imaging features with late adverse events was retrospectively assessed in 83 patients with acute uncomplicated Stanford type-B aortic dissection, followed over a median of 850 (interquartile range 247-1824) days. Adverse events were defined as fatal or nonfatal aortic rupture, rapid aortic growth (>10 mm/y), aneurysm formation (≥6 cm), organ or limb ischemia, or new uncontrollable hypertension or pain. Five significant predictors were identified using multivariable Cox regression analysis: connective tissue disease (hazard ratio [HR] 2.94, 95% confidence interval [CI]: 1.29-6.72; P=0.01), circumferential extent of false lumen in angular degrees (HR 1.03 per degree, 95% CI: 1.01-1.04, P=0.003), maximum aortic diameter (HR 1.10 per mm, 95% CI: 1.02-1.18, P=0.015), false lumen outflow (HR 0.999 per mL/min, 95% CI: 0.998-1.000; P=0.055), and number of intercostal arteries (HR 0.89 per n, 95% CI: 0.80-0.98; P=0.024). A prediction model was constructed to calculate patient specific risk at 1, 2, and 5 years and to stratify patients into high-, intermediate-, and low-risk groups. The model was internally validated by bootstrapping and showed good discriminatory ability with an optimism-corrected C statistic of 70.1%.
Conclusions:
Computed tomography imaging-based morphological features combined into a prediction model may be able to identify patients at high risk for late adverse events after an initially uncomplicated type-B aortic dissection.