Decision-making algorithm for ascending aortic aneurysm: Effectiveness in clinical application?

Objective: The risk of rupture and dissection in ascending thoracic aortic aneurysms increases as the aortic diameter exceeds 5 cm. This study evaluates the clinical effectiveness of a specific algorithm based on size and symptoms for preemptive surgery to prevent complications. Methods: A total of 781 patients with nondissecting ascending thoracic aortic aneurysms who presented electively for evaluation to our institution from 2011 to 2017 were triaged to surgery (n = 607, 77%) or medical observation (n = 181, 24%) based on a specific algorithm: surgery for large (>5 cm) or symptomatic aneurysms. A total of 309 of 781 patients did not undergo surgery. Of these, 128 (16%) had been triaged to prompt repair but did not undergo surgery for a variety of reasons (“surgery noncompliant and overwhelming comorbidities” group). Another 181 patients (24%) were triaged to medical management (“medical” group). Results: In the “surgery noncompliant and overwhelming comorbidities” versus the “medical” group, mean aortic diameters were 5 ± 0.5 cm versus 4.45 ± 0.4 cm and aortic events (rupture/dissection) occurred in 17 patients (13.3%) versus 3 patients (1.7%), respectively (P <.001). Later elective surgeries (representing late compliance in the “surgery noncompliant and overwhelming comorbidities group” or onset of growth or symptoms in the “medical” group) were conducted in 21 patients (16.4%) versus 15 patients (8.3%) (P =.04), respectively. Death ensued in 20 patients (15.6%) versus 6 patients (3.3%) (P <.001), respectively. In the “surgery noncompliant and overwhelming comorbidities” group, 7 of 20 patients died of definite aortic causes compared with none in the “medical” group. Conclusions: Patients with ascending thoracic aortic aneurysms who did not follow surgical recommendations experienced substantially worse outcomes compared with medically triaged candidates. The specific algorithm based on size and symptoms functioned effectively in the clinical setting, correctly identifying both at-risk and safe patients.