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Purpose: To evaluate a finite element analysis (FEA) model as a predictive tool for abdominal aortic aneurysm (AAA) rupture risk assessment. Methods: FEA of asymptomatic infrarenal AAAs in 15 men (mean age 72 years) was performed preoperatively using semiautomatic finite element analysis software (A4clinics) to calculate peak wall stress (PWS) and regions of highest and lowest rupture risk index (RRI). The areas of high and low RRI identified on the preoperative FEA were sampled during open surgery; aortic wall specimens were prepared for histological analysis. A semiquantitative score compared the histological findings from high and low rupture risk samples. Results: Significant correlation was found between histological AAA wall integrity and RRI in individual patients. AAA wall regions with highest RRI showed advanced histological disintegrity compared to regions with lower RRI within the same AAA: mean smooth muscle cells: 0.43 vs. 1.21, respectively (p=0.031); elastic fibers: 0.57 vs. 1.29, respectively (p=0.008); cholesterol plaque: 2.60 vs. 2.20, respectively (p=0.034); and calcified plaque: 2.27 vs. 1.40, respectively (p=0.017). The amount of calcified plaque was significantly correlated with PWS (r=0.528, p=0.043) by univariate regression analysis. However, there was no correlation between PWS or RRI and the histological findings between patients. Conclusion: These preliminary results show that high rupture risk regions estimated by FEA contain increased histopathological degeneration compared to low rupture risk samples within the same AAA. Until now, the role of FEA in predicting individual AAA rupture risk has not been established as a validated diagnostic tool. However, these data provide promising results for FEA model verification.

This is the first preliminary study to develop prediction models for aneurysm rupture risk using radiomics analysis based on follow-up magnetic resonance angiography (MRA) images. We selected 103 follow-up images from 18 unruptured aneurysm (UA) cases and 10 follow-up images from 10 ruptured aneurysm (RA) cases to build the prediction models. A total of 486 image features were calculated, including 54 original features and 432 wavelet-based features, within each aneurysm region in the MRA images for the texture patterns. We randomly divided the 103 UA data into 50 training and 53 testing data and separated the 10 RA data into 1 test and 9 training data to be increased to 54 using a synthetic minority oversampling technique. We selected 11 image features associated with UAs and RAs from 486 image features using the least absolute shrinkage and the selection operator logistic regression and input them into a support vector machine to build the rupture prediction models. An imbalanced adjustment training and test strategy was developed. The area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.971, 0.948, 0.700, and 0.953, respectively. This prediction model with non-invasive MRA images could predict aneurysm rupture risk for SAH prevention.

Background: A ruptured abdominal aortic aneurysm is a severe condition associated with high mortality. Currently, the most important criterion used to estimate the risk of its rupture is the size of the aneurysm, but due to patients’ anatomical variability, many aneurysms have a high risk of rupture with a small aneurysm size. We asked ourselves whether individual differences in anatomy could be taken into account when assessing the risk of rupture. Methods: Based on the CT scan image, aneurysm and normal aorta diameters were collected from 186 individuals and compared in patients with ruptured and unruptured aneurysms. To take into account anatomical differences between patients, diameter ratios were calculated by dividing the aneurysm diameter by the diameter of the normal aorta at various heights, and then further comparisons were made. Results: It was found that the calculated ratios differ between patients with ruptured and unruptured aneurysms. This observation is also present in patients with small aneurysms, with its maximal size below the level that indicates the need for surgical treatment. For small aneurysms, the ratios help us to estimate the risk of rupture better than the maximum sac size (AUC: 0.783 vs. 0.650). Conclusions: The calculated ratios appear to be a valuable feature to indicate which of the small aneurysms have a high risk of rupture. The obtained results suggest the need for further confirmation of their usefulness in subsequent groups of patients.

Cardiovascular disorders are among the most important causes of sudden death and adult disability worldwide. Abdominal aortic aneurysm (AAA) is a critical clinical condition where the aorta dilates beyond 50% of its normal diameter and leads to a risk of rupture. In this study, we performed fluid-structure interaction (FSI) analysis on an eccentric computational AAA model in order to investigate the effects of wall thickness on AAA wall stresses, which are critically important to estimate the rupture risk. For this purpose, we modeled the problem domain using finite element analysis, and coupled the solutions of fluid and structure domains for improving the accuracy of results. ANSYS commercial finite element analysis software was used for modeling, solving, and post-processing the results. Expanded diameter in AAA sac resulted in altered hemodynamics. Wall shear stresses (WSS) caused by the flow are quite low on the AAA sac, which may deteriorate the endothelial cell regeneration and vascular remodeling in the long term. It is concluded that the most critical region for the rupture risk is the posterior distal end of AAA sac due to being exposed to peak mechanical stresses during the cardiac cycle. Obtained results shed light in understanding the rupture risk assessment of AAA.

The integration of biomechanical and morphological analyses holds tremendous potential for assessing the rupture risk of abdominal aortic aneurysms (AAA). We employed a one-way fluid-structure interaction (FSI) model to distinguish between ruptured AAA (RAAA) and asymptomatic intact AAA (IAAA), focusing on morphological and computational fluid dynamics (CFD) indices. Patient groups with ruptured RAAA and asymptomatic IAAA were matched by diameter, age, and sex. AAA morphology was analyzed via CT segmentation, and biomechanical indices—including wall shear stress (WSS), peak wall stress (PWS), maximum deformation (MD), and other indices—were determined using FSI analysis. Statistical comparisons were performed using paired t-tests or Wilcoxon rank sum tests. Multivariate and LASSO regression analyses identified predictive factors, and a nomogram was developed. Model accuracy was assessed using the area under the curve (AUC). In our study with 66 RAAA and 66 asymptomatic IAAA patients, the tortuosity of the RAAAs was 1.4 times that of the asymptomatic IAAAs ( P  = 0.0005). The PWS, MD and peak wall rupture index (PWRI) of the RAAAs was 1.18, 1.32 and 1.27 times that of the asymptomatic IAAAs ( P  = 0.0158, 0.0036, 0.0071). The MD position demonstrated high consistency with RAAA rupture locations (94.12%). Four variables were selected for a nomogram, predicting AAA rupture with an AUC of 0.7604 (95% CI 0.6653–0.8556) and an internal validation AUC of 0.8051 (95% CI 0.6400–0.9703). In this study, we demonstrated that the location of MD is valuable for predicting the rupture location of AAA. We constructed a nomogram incorporating four key predictors—aortic neck length (ANL), intraluminal thrombus volume relative to AAA volume (VILT/VAAA), tortuosity, and MD—that enhances the prediction of AAA rupture risk, offering a more personalized assessment beyond traditional diameter-based methods.

The biomechanical rupture risk assessment (BRRA) of abdominal aortic aneurysms (AAA) has higher sensitivity than maximal diameter criterion (DSEX) but its estimation is time-consuming and relies on an uncertain estimation of wall thickness. The aim of this study is to test tension-based criterion in the BRRA of AAA which removes the necessity of wall thickness measurement and should be faster. For that, we retrospectively analyzed 99 patients with intact AAA (25 females). Nineteen of them experienced a rupture later. BRRA was performed with wall tension PRRIT as a primary criterion. The ability of criterion to separate intact and ruptured AAAs at 1,3,6,9 and 12 months was estimated. Next, the receiver operating characteristics and the percentage of true negative cases for a different time to an outcome were estimated. Finally, the computational time was recorded. The results were compared to stress-based criterion PRRI and DSEX which served as a reference. All three criterions were able to discriminate between intact and ruptured AAAs up to 9 months (p < 0.05) while none of them could do for a 12 month prediction. PRRIT exhibited a significantly higher percentage of true negatives for 12 and 9 month predictions (45 % and 20 % respectively) and similar to other criteria for other prediction times. The mean computational time for estimating PRRIT was 19 h per patient compared to 67 h for PRRI. The tension- based BRRA of AAA leads to better outcomes for a 9 and 12 month prediction while the computational time drops by more than 70 % compared to PRRI.

Computational fluid dynamics (CFD) has been used for several years to identify mechanical risk factors associated with aneurysmal evolution and rupture as well as to understand flow characteristics before and after surgical treatments in order to help the clinical decision making process. We used the keywords, “CFD” and “aneurysms” to search recent publications since about 2000, and categorized them into (i) studies of rupture risk factors and (ii) investigations of pre- and post-evaluations of surgical treatment with devices like coils and flow diverters (FD). This search enables us to examine the current status of CFD as a clinical tool and to determine if CFD can potentially become an important part of the routine clinical practice for the evaluation and treatment of aneurysms in near future. According to previous reports, it has been argued that CFD has become a quite robust non-invasive tool for the evaluation of surgical devices, especially in the early stages of device design and it has also been applied successfully to the study of rupture risk assessment. However, we find that due to the large number of pre-processing inputs further efforts of validation and reproducibility of CFD with larger clinical datasets are still essential to identify standardized mechanical risk factors. As a result, we identify the following needs to have a robust CFD tool for clinical use: (i) more reliability tests through validation studies, (ii) analyses of larger generalized clinical datasets to find converging universal risk parameters, (iii) fluid structure interaction (FSI) analyses to better understand the detailed vascular remodeling processes associated with aneurysm growth, evolution and rupture, and (iv) better coordinated and organized communications and collaborations between engineers and clinicians.

Background Breast implants (BI) are widely used in plastic surgery, though they are not lifetime devices. Average life before rupture is reported to be around 10–15 years. No consensus exists regarding which factors are involved. Objectives Following FDA recommendations, this study aims at identifying potential risk factors by evaluating their effect on BI rupture cases. Methods In this observational study, 763 BI patients were operated between 2003 and 2019, with a mean implant indwelling of 12.2 years. Patients that returned for follow-up were administered a questionnaire regarding postoperative lifestyle and habits. Implant rupture rate was 15.1%, while BI lifespan was 10.1 years. We obtained complete data from 191 breast implant patients (288 implants). Twenty-three potential risk factors were evaluated and divided in four categories: patient-related, surgery-related, postoperative complications/symptoms, and postoperative care/lifestyle habits. Odds Ratio (OR) for each factor was calculated. Linear regression analysis was calculated for those with a significant OR. Results We report 120 patients (195 implants) with intact and 71 (93 implants) with ruptured devices. BIs were macrotextured in 95.1% of cases (86.8% Allergan BIOCELL). OR was significant for underwire bra use (OR: 2.708), car seat belts (OR: 3.066), mammographic imaging (OR: 2.196), weightlifting (OR: 0.407) and carry-on heavy purses and backpacks (OR: 0.347). Conclusion Wearing underwire bras, seat belts and undergoing mammography increases the risk of rupture. Weightlifting and carry heavy bags do not increase that risk. Implant rupture is directly linked with time of indwelling. Postoperative recommendations in BI patients should consider findings from our study, though larger multicenter studies should be encouraged. Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Accurate rupture risk assessment is essential for optimizing treatment decisions in patients with cerebral aneurysms. While computational fluid dynamics (CFD) has provided critical insights into aneurysmal hemodynamics, most analyses focus on blood flow patterns, neglecting the biomechanical properties of the aneurysm wall. To address this limitation, we applied Fluid-Structure Interaction (FSI) analysis, an integrative approach that simulates the dynamic interplay between hemodynamics and wall mechanics, offering a more comprehensive risk assessment. In this study, we used advanced FSI techniques to investigate the rupture risk of middle cerebral artery bifurcation (MCA) aneurysms, analyzing a cohort of 125 patients treated for a MCA aneurysm at Kepler University Hospital, Linz, Austria. Multivariate analysis identified two significant rupture predictors: High Equivalent Stress Area (HESA; p  = 0.049), which quantifies stress distribution relative to the aneurysm surface, and Gaussian curvature (GLN; p  = 0.031), which captures geometric complexity. We also introduce the HGD index, a novel composite metric combining H ESA, G LN, and Maximum Wall D isplacement, designed to enhance predictive accuracy. With a threshold of 0.075, the HGD index exhibited excellent diagnostic performance; in internal validation, 24 of 25 ruptured aneurysms surpassed this threshold, yielding a sensitivity of 0.96. In a 5-fold cross validation the reliability of results was confirmed. Our findings demonstrate that the HGD index provides superior rupture risk stratification compared to conventional single-parameter models, offering a more robust tool for the assessment of complex aneurysmal structures. Further multicenter studies are warranted to refine and validate the HGD index, advancing its potential for clinical application and improving patient outcomes.

An ascending thoracic aortic aneurysm (ATAA) is a serious medical condition which, more often than not, requires surgery. Aneurysm diameter is the primary clinical criterion for determining when surgical intervention is necessary but, biomechanical studies have suggested that the diameter criterion is insufficient. This manuscript presents a method for obtaining the patient specific wall stress distribution of the ATAA and the retrospective rupture risk for each patient. Five human ATAAs and the preoperative dynamic CT scans were obtained during elective surgeries to replace each patient's aneurysm with a synthetic graft. The material properties and rupture stress for each tissue sample were identified using bulge inflation tests. The dynamic CT scans were used to generate patient specific geometries for a finite element (FE) model of each patient's aneurysm. The material properties from the bulge inflation tests were implemented in the FE model and the wall stress distribution at four different pressures was estimated. Three different rupture risk assessments were compared: the maximum diameter, the rupture risk index, and the overpressure index. The peak wall stress values for the patients ranged from 28% to 94% of the ATAA's failure stress. The rupture risk and overpressure indices were both only weakly correlated with diameter (ρ=−0.29, both cases). In the future, we plan to conduct a large experimental and computational study that includes asymptomatic patients under surveillance, patients undergoing elective surgery, and patients who have experienced rupture or dissection to determine if the rupture risk index or maximum diameter can meaningfully differentiate between the groups.