CT analysis of aortic calcifications to predict abdominal aortic aneurysm rupture

Background Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved. The goal was to assess whether aortic calcification distribution could better predict AAA rupture through machine learning and LASSO regression. Methodology In this retrospective study, 80 patients treated for a ruptured AAA between January 2001 and August 2018 were matched with 80 non-ruptured patients based on maximal AAA diameter, age, and sex. Calcification volume and dispersion, morphologic, and clinical variables were compared between both groups using a univariable analysis with p = 0.05 and multivariable analysis through machine learning and LASSO regression. We used AUC for machine learning and odds ratios for regression to measure performance. Results Mean age of patients was 74.0 ± 8.4 years and 89% were men. AAA diameters were equivalent in both groups (80.9 ± 17.5 vs 79.0 ± 17.3 mm, p = 0.505). Ruptured aneurysms contained a smaller number of calcification aggregates (18.0 ± 17.9 vs 25.6 ± 18.9, p = 0.010) and were less likely to have a proximal neck (45.0% vs 76.3%, p