Abstract 11871: Morphologic Features Predict Late Adverse Events in Uncomplicated Stanford Type-B Aortic Dissection

Abstract

Introduction: Medical treatment of uncomplicated Stanford type-B aortic dissection (AD) is associated with a high rate of late adverse events (AE). Endografting may prevent late complications, however, initial trials failed to demonstrate a survival benefit in the first two years. We hypothesize that morphologic features at the initial hospitalization are associated with AE and could aid in the risk stratification of patients with AD. Methods: Eighty-three patients with acute uncomplicated type-B AD were retrospectively identified at two aortic centers in the US and EU. Multiple clinical and morphologic features - including true vs. false lumen blood supply - were identified on high-quality CT scans and followed. Multivariable Cox proportional hazards regression was used to evaluate the associations between candidate predictors and AE - defined as rapid growth, aneurysm formation (≥6cm), organ ischemia, and fatal or non-fatal aortic rupture, and develop an internally validated risk score. Results: AE were observed in 33 patients over a median 850 (IQR 247-1824) days follow up. Five significant predictors were identified in the multivariable model: Connective tissue disease (HR 2.94, 95% CI: 1.29 - 6.72, p= 0.01), circumferential extent of false lumen in angular degrees (HR 1.03, 95% CI: 1.01 - 1.04, p= 0.003), maximum aortic diameter in mm (HR 1.10, 95% CI: 1.02 - 1.18, p= 0.015), false lumen outflow in mL/min (HR 0.999, 95% CI: 0.998 - 1.000, p= 0.055), and number of intercostal arteries (HR 0.89, 95% CI: 0.80 - 0.98), p= 0.024). A model combining these five factors was constructed to calculate patient specific risk scores allowing stratification into high, intermediate, and low risk terciles. The model showed good discriminatory ability with an optimism-corrected c-statistic of 70.1%. Conclusion: Morphologic features predict late AE in uncomplicated type-B AD. If externally validated this may identify high-risk patients who benefit from early endografting.

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