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 < 0.001). In the machine learning analysis, 5 variables were associated to AAA rupture: proximal neck, antiplatelet use, calcification number, Euclidian distance between calcifications, and standard deviation of the Euclidian distance. A follow-up LASSO regression was concomitant with the findings of the machine learning analysis regarding calcification dispersion but discordant on calcification number.

CONCLUSION

There might be more to AAA calcifications that what is known in the present literature. We need larger prospective studies to investigate if indeed, calcification dispersion affects rupture risk.

CLINICAL RELEVANCE STATEMENT

Ruptured aneurysms are possibly more likely to have their calcification volume concentrated in a smaller geographical area.

KEY POINTS

• Abdominal aortic aneurysm (AAA) rupture prediction based on sex and diameter could be improved. • For a given calcification volume, AAAs with well-distributed calcification clusters could be less likely to rupture. • A machine learning model including AAA calcifications better predicts rupture compared to a model based solely on maximal diameter and sex alone, although it might be prone to overfitting.