Multidimensional Analysis of Descending Aortic Growth After Acute Type A Aortic Dissection

Background. After repair of acute type A aortic dissection, typical geometric variables of conventional aortic surveillance focus on maximum diameter and its rate of growth, potentially missing important geometric changes elsewhere. We determined additional information provided by a semiautomated, 3-dimensional (3D), nonlinear growth model of the descending thoracic aorta after repair of type A aortic dissection. Methods. Computed tomographic angiography data were retrospectively collected after hemiarch repair of type A aortic dissection. The descending aorta was systematically reconstructed to generate a 3D model made up of individual segments. The baseline and follow-up diameters were measured semiautomatically for each segment, and the nonlinear interval growth was determined. Results. The fastest growing segment expanded at a rate of 3.8 mm/y (interquartile range, 2.2 to 5.4 mm/y) vs 0.6 mm/y (interquartile range, -0.3 to 1.7 mm/y) when measured at the original site of maximum diameter (P < .01). The maximum baseline diameter was a poor predictor of location with fastest growth (r = 0.10, P >.1). Using the society recommended growth limits, a greater proportion of patients would be considered at risk when assessed by our method vs conventional surveillance measures. Conclusions. Our model identifies areas of rapid aortic growth after repair of type A dissection that would likely be missed using current surveillance techniques. The increased precision, resolution, and reproducibility provided by our technique may improve on limitations of current surveillance techniques, provide novel geometric data on aortic remodeling, and contribute to the pursuit of a comprehensive patient-specific approach to aortic risk stratification. (C) 2021 by The Society of Thoracic Surgeons.

Automated summary

The study utilized a semi-automated, 3D, nonlinear growth model to monitor the descending aorta after repair of type A aortic dissection, identifying areas of rapid aortic growth that may be missed by conventional surveillance techniques. The technique provided enhanced precision, resolution, and reproducibility, potentially improving upon the limitations of current surveillance methods.