Biomechanical indices are more sensitive than diameter in predicting rupture of asymptomatic abdominal aortic aneurysms.

OBJECTIVE: Several studies of biomechanical rupture risk assessment (BRRA) showed its advantage over the diameter criterion in rupture risk assessment of abdominal aortic aneurysm (AAA). However, BRRA studies have not investigated the predictability of biomechanical risk indices at different time points ahead of rupture, nor have they been performed blinded for biomechanical analysts. The objective of this study was to test the predictability of the BRRA method against diameter-based risk indices in a quasi-prospective patient cohort study. METHODS: In total, 12 women and 31 men with intact AAAs at baseline have been selected retrospectively at two medical centers. Within 56 months, 19 cases ruptured, whereas 24 cases remained intact within 2 to 56 months. This outcome was kept confidential until all biomechanical activities in this study were finished. The biomechanical AAA rupture risk was calculated at baseline using high-fidelity and low-fidelity finite element method models. The capability of biomechanics-based and diameter-based risk indices to predict the known outcomes at 1 month, 3 months, 6 months, 9 months, and 12 months after baseline was validated. Besides common cohort statistics, the area under the curve (AUC) of receiver operating characteristic curves has been used to grade the different rupture risk indices. RESULTS: Up to 9 months ahead of rupture, the receiver operating characteristic analysis of biomechanics-based risk indices showed a higher AUC than diameter-based indices. Six months ahead of rupture, the largest difference was observed with an AUC of 0.878 for the high-fidelity biomechanical risk index, 0.859 for the low-fidelity biomechanical risk index, 0.789 for the diameter, and 0.821 for the sex-adjusted diameter. In predictions beyond 9 months, none of the risk indices proved to be superior. CONCLUSIONS: High-fidelity biomechanical modeling improves the predictability of AAA rupture. Asymptomatic AAA patients with high biomechanical AAA rupture risk indices have an increased risk of rupture. Integrating biomechanics-based diagnostic indices may significantly decrease the false-positive rate in AAA treatment. CLINICAL RELEVANCE: Rupture of abdominal aortic aneurysm (AAA) is the tenth leading cause of death in men older than 60 years; however, the currently used maximal diameter criterion has a high false-positive rate. In this study, we have compared this criterion with biomechanical rupture risk assessment on the unique data set of 43 asymptomatic AAAs, of which 19 ruptured later. Moreover, the AAA outcome was blinded to the operator for the first time. Our data demonstrated that the biomechanical rupture risk assessment is superior to maximal diameter in predicting AAA rupture up to 9 months ahead and significantly decreases the false-positive rate.

Predictive capabilities of various constitutive models for arterial tissue.

INTRODUCTION: Aim of this study is to validate some constitutive models by assessing their capabilities in describing and predicting uniaxial and biaxial behavior of porcine aortic tissue. METHODS: 14 samples from porcine aortas were used to perform 2 uniaxial and 5 biaxial tensile tests. Transversal strains were furthermore stored for uniaxial data. The experimental data were fitted by four constitutive models: Holzapfel-Gasser-Ogden model (HGO), model based on generalized structure tensor (GST), Four-Fiber-Family model (FFF) and Microfiber model. Fitting was performed to uniaxial and biaxial data sets separately and descriptive capabilities of the models were compared. Their predictive capabilities were assessed in two ways. Firstly each model was fitted to biaxial data and its accuracy (in term of R2 and NRMSE) in prediction of both uniaxial responses was evaluated. Then this procedure was performed conversely: each model was fitted to both uniaxial tests and its accuracy in prediction of 5 biaxial responses was observed. RESULTS: Descriptive capabilities of all models were excellent. In predicting uniaxial response from biaxial data, microfiber model was the most accurate while the other models showed also reasonable accuracy. Microfiber and FFF models were capable to reasonably predict biaxial responses from uniaxial data while HGO and GST models failed completely in this task. CONCLUSIONS: HGO and GST models are not capable to predict biaxial arterial wall behavior while FFF model is the most robust of the investigated constitutive models. Knowledge of transversal strains in uniaxial tests improves robustness of constitutive models.