Biomechanical Assessment Predicts Aneurysm Related Events in Patients with Abdominal Aortic Aneurysm

Peak wall rupture index is associated with risk of rupture of abdominal aortic aneurysms, independent of size and sex

BACKGROUND

Information on the predictive determinants of abdominal aortic aneurysm rupture from CT angiography are scarce. The aim of this study was to investigate biomechanical parameters in abdominal aortic aneurysms and their association with risk of subsequent rupture.

METHODS

In this retrospective study, the digital radiological archive was searched for 363 patients with ruptured abdominal aortic aneurysms. All patients who underwent at least one CT angiography examination before aneurysm rupture were included. CT angiography results were analysed to determine maximum aneurysm diameter, aneurysm volume, and biomechanical parameters (peak wall stress and peak wall rupture index). In the primary survival analysis, patients with abdominal aortic aneurysms less than 70 mm were considered. Sensitivity analyses including control patients and abdominal aortic aneurysms of all sizes were performed.

RESULTS

A total of 67 patients who underwent 109 CT angiography examinations before aneurysm rupture were identified. The majority were men (47, 70%) and the median age at the time of CTA examination was 77 (71-83) years. The median maximum aneurysm diameter was 56 (interquartile range 46-65) mm and the median time to rupture was 2.13 (interquartile range 0.64-4.72) years. In univariable analysis, maximum aneurysm diameter, aneurysm volume, peak wall stress, and peak wall rupture index were all associated with risk of rupture. Women had an increased HR for rupture when adjusted for maximum aneurysm diameter or aneurysm volume (HR 2.16, 95% c.i. 1.23 to 3.78 (P = 0.007) and HR 1.92, 95% c.i. 1.06 to 3.50 (P = 0.033) respectively). In multivariable analysis, the peak wall rupture index was associated with risk of rupture. The HR for peak wall rupture index was 1.05 (95% c.i. 1.03 to 1.08) per % (P < 0.001) when adjusted for maximum aneurysm diameter and 1.05 (95% c.i. 1.02 to 1.08) per % (P < 0.001) when adjusted for aneurysm volume.

CONCLUSION

Biomechanical factors appear to be important in the prediction of abdominal aortic aneurysm rupture. Women are at increased risk of rupture when adjustments are made for maximum aneurysm diameter alone.

A two-scale numerical study on the mechanobiology of abdominal aortic aneurysms

Abdominal aortic aneurysms (AAAs) are a serious condition whose pathophysiology is related to phenomena occurring at different length scales. To gain a better understanding of the disease, this work presents a multi-scale computational study that correlates AAA progression with microstructural and mechanical alterations in the tissue. Macro-scale geometries of a healthy aorta and idealized aneurysms with increasing diameter are developed on the basis of existing experimental data and subjected to physiological boundary conditions. Subsequently, microscopic representative volume elements of the abluminal side of each macro-model are employed to analyse the local kinematics at the cellular scale. The results suggest that the formation of the aneurysm disrupts the micromechanics of healthy tissue, which could trigger collagen growth and remodelling by mechanosensing cells. The resulting changes to the macro-mechanics and microstructure of the tissue seem to establish a new homeostatic state at the cellular scale, at least for the diameter range investigated.

Association between aortic peak wall stress and rupture index with abdominal aortic aneurysm-related events

OBJECTIVE

The aim of this study was to assess whether aortic peak wall stress (PWS) and peak wall rupture index (PWRI) were associated with the risk of abdominal aortic aneurysm (AAA) rupture or repair (defined as AAA events) among participants with small AAAs.

METHODS

PWS and PWRI were estimated from computed tomography angiography (CTA) scans of 210 participants with small AAAs (≥ 30 and  ≤ 50 mm) prospectively recruited between 2002 and 2016 from two existing databases. Participants were followed for a median of 2.0 (inter-quartile range 1.9, 2.8) years to record the incidence of AAA events. The associations between PWS and PWRI with AAA events were assessed using Cox proportional hazard analyses. The ability of PWS and PWRI to reclassify the risk of AAA events compared to the initial AAA diameter was examined using net reclassification index (NRI) and classification and regression tree (CART) analysis.

RESULTS

After adjusting for other risk factors, one standard deviation increase in PWS (hazard ratio, HR, 1.56, 95% confidence intervals, CI 1.19, 2.06; p = 0.001) and PWRI (HR 1.74, 95% CI 1.29, 2.34; p < 0.001) were associated with significantly higher risks of AAA events. In the CART analysis, PWRI was identified as the best single predictor of AAA events at a cut-off value of  > 0.562. PWRI, but not PWS, significantly improved the classification of risk of AAA events compared to the initial AAA diameter alone.

CONCLUSION

PWS and PWRI predicted the risk of AAA events but only PWRI significantly improved the risk stratification compared to aortic diameter alone.

KEY POINTS

• Aortic diameter is an imperfect measure of abdominal aortic aneurysm (AAA) rupture risk. • This observational study of 210 participants found that peak wall stress (PWS) and peak wall rupture index (PWRI) predicted the risk of aortic rupture or AAA repair. • PWRI, but not PWS, significantly improved the risk stratification for AAA events compared to aortic diameter alone.

A review on the biomechanical behaviour of the aorta

Large aortic aneurysm and acute and chronic aortic dissection are pathologies of the aorta requiring surgery. Recent advances in medical intervention have improved patient outcomes; however, a clear understanding of the mechanisms leading to aortic failure and, hence, a better understanding of failure risk, is still missing. Biomechanical analysis of the aorta could provide insights into the development and progression of aortic abnormalities, giving clinicians a powerful tool in risk stratification. The complexity of the aortic system presents significant challenges for a biomechanical study and requires various approaches to analyse the aorta. To address this, here we present a holistic review of the biomechanical studies of the aorta by categorising articles into four broad approaches, namely theoretical, in vivo, experimental and combined investigations. Experimental studies that focus on identifying mechanical properties of the aortic tissue are also included. By reviewing the literature and discussing drawbacks, limitations and future challenges in each area, we hope to present a more complete picture of the state-of-the-art of aortic biomechanics to stimulate research on critical topics. Combining experimental modalities and computational approaches could lead to more comprehensive results in risk prediction for the aortic system.

A quarter of a century biomechanical rupture risk assessment of abdominal aortic aneurysms. Achievements, clinical relevance, and ongoing developments

Abdominal aortic aneurysm (AAA) disease, the local enlargement of the infrarenal aorta, is a serious condition that causes many deaths, especially in men exceeding 65 years of age. Over the past quarter of a century, computational biomechanical models have been developed towards the assessment of AAA risk of rupture, technology that is now on the verge of being integrated within the clinical decision-making process. The modeling of AAA requires a holistic understanding of the clinical problem, in order to set appropriate modeling assumptions and to draw sound conclusions from the simulation results. In this article we summarize and critically discuss the proposed modeling approaches and report the outcome of clinical validation studies for a number of biomechanics-based rupture risk indices. Whilst most of the aspects concerning computational mechanics have already been settled, it is the exploration of the failure properties of the AAA wall and the acquisition of robust input data for simulations that has the greatest potential for the further improvement of this technology.

Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period

OBJECTIVE

We investigated the utility of geometric features for future AAA growth prediction.

BACKGROUND

Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth.

METHODS

Computerized tomography (CT) scans from patients with infra-renal AAAs were analyzed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI), and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients.

RESULTS

There was no correlation between the geometric measurements and patients' demographic features. APD (Spearman r = 0.25, P < 0.05), UI (Spearman r = 0.38, P < 0.001) and RC (Spearman r =-0.53, P < 0.001) significantly correlated with annual AAA growth. Using APD, UI, and RC as 3 input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/yr) or fast growth (>5 mm/yr) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases.

CONCLUSIONS

Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway.

Abdominal Aortic Aneurysms (Etiology, Epidemiology, and Natural History)

Abdominal aortic aneurysm is a potentially lethal condition that is decreasing in frequency as tobacco use declines. The exact etiology remains unknown, but smoking and other perturbations seem to trigger an inflammatory state in the tunica media. Male sex and advanced age are clear demographic risk factors for the development of abdominal aortic aneurysms. The natural history of this disease varies, but screening remains vital as it is rarely diagnosed on physical examination, and elective repair (most commonly done endovascularly) offers significant morbidity and mortality advantages over emergent intervention for aortic rupture.

Activating transcription factor 4 aggravates angiotensin II-induced cell dysfunction in human vascular aortic smooth muscle cells via transcriptionally activating fibroblast growth factor 21

Abdominal aortic aneurysm (AAA) is a life-threatening disorder worldwide. Fibroblast growth factor 21 (FGF21) was shown to display a high level in the plasma of patients with AAA; however, its detailed functions underlying AAA pathogenesis are unclear. An in vitro AAA model was established in human aortic vascular smooth muscle cells (HASMCs) by angiotensin II (Ang-II) stimulation. Cell counting kit-8, wound healing, and Transwell assays were utilized for measuring cell proliferation and migration. RT-qPCR was used for detecting mRNA expression of FGF21 and activating transcription factor 4 (ATF4). Western blotting was utilized for assessing protein levels of FGF21, ATF4, and markers for the contractile phenotype of HASMCs. ChIP and luciferase reporter assays were implemented for identifying the binding relation between AFT4 and FGF21 promoters. FGF21 and ATF4 were both upregulated in Ang-II-treated HASMCs. Knocking down FGF21 attenuated Ang-II-induced proliferation, migration, and phenotype switch of HASMCs. ATF4 activated FGF21 transcription by binding to its promoter. FGF21 overexpression reversed AFT4 silencing-mediated inhibition of cell proliferation, migration, and phenotype switch. ATF4 transcriptionally upregulates FGF21 to promote the proliferation, migration, and phenotype switch of Ang-II-treated HASMCs.

Association between blood flow pattern and rupture risk of abdominal aortic aneurysm based on computational fluid dynamics

OBJECTIVES

This study aimed to derive a novel classification of blood flow pattern in AAA based on computational fluid dynamics, and determine the predicting value of flow pattern in abdominal aortic aneurysm (AAA) rupture.

DESIGN

Age, gender matched case-control study MATERIALS: Case patients were identified as those who underwent emergent endovascular or open repair due to ruptured or impending rupture AAA. Control patients were those age and gender matched AAA patients who were asymptomatic and confirmed unruptured from CTA images from the same period.

METHODS

Classification of blood flow pattern (Type I: non-helical main flow channel with multiple vortices; Type II: non-helical main flow channel with single vortices; Type III, helical main flow channel with helical vortices) and hemodynamic parameters [areas of low wall shear stress (A _{low WSS}), aneurysm pressure drop (Δ pressure), etc.] were derived from computational fluid dynamic (CFD) analyses. Multivariate regression was used to determine independent risk factors of AAA rupture. The incremental discriminant and reclassification abilities for AAA rupture were compared among different models.

RESULTS

This study included 53 ruptured and 53 intact AAA patients. Ruptured AAA showed higher prevalence of type III flow pattern (60.38% vs. 15.09%, P<.001) compared to intact AAA. Type III flow pattern was associated with a significantly increased risk of aneurysm rupture (OR 10.22, 95%CI 3.43-30.49). Among all predicting models, combination of AAA diameter, hemodynamic parameters (A _{low WSS} or Δ pressure) and flow pattern showed highest discriminant abilities in both overall population (concordance statistic [c-index] .862) and subgroup patients with AAAs <55mm (c-index .972). Compared to AAA diameter, adding flow pattern could significantly improve the reclassification abilities in both overall population (net reclassification index [NRI] .321; p<.001) and subgroup of AAAs < 55mm (NRI .732, P<.001).

CONCLUSION

Type III flow pattern was associated with a significantly increased risk of AAA rupture. Integration of blood flow pattern may improve the identification of high-risk aneurysms in both overall population and AAAs smaller than 55mm.

The Evaluation of Longitudinal Strain of Large and Small Abdominal Aortic Aneurysm by Two-Dimensional Speckle-Tracking Ultrasound

OBJECTIVES

Abdominal aortic aneurysm (AAA) is a dangerous and lethal vascular disease. Non-invasive two-dimensional speckle-tracking imaging (2D STI) plays an important role in assessing aortic biomechanical properties. Our study aimed to evaluate the alterations of biomechanical characteristics using 2D STI in 91 AAA patients with different size.

METHODS

Aneurysm strain, elastic modulus, stiffness index β, and aortic distensibility determined by M-Mode ultrasound (US), and longitudinal strain (LS) derived from 2D STI were compared in 40 large AAA patients (diameter ≥ 55 mm) and 51 small AAA patients (diameter < 55 mm).

RESULTS

Compared with small AAA group, anterior wall longitudinal strain (ALS) and posterior wall longitudinal strain (PLS) were significantly decreased in large AAA group (all P < .05) and not affected by age, symptom, hypertension, and thrombus. Meanwhile, ALS and PLS correlated negatively with maximal aneurysm diameters (r = -0.628 and -0.469, respectively, all P < .001). And only ALS was associated with M-Mode US parameters (all P < .05). Based on receiver operating characteristic (ROC) analysis, ALS and PLS had strong diagnostic values for large AAA with the area under the curve (AUC) of 0.82 and 0.72, and cut-off points of 1.71 and 1.64% with a sensitivity of 78 and 72%, and a specificity of 75 and 70%, respectively.

CONCLUSIONS

LS measured by 2D STI could evaluate the biomechanical properties of aneurysm wall with different size, and add additional diagnostic value in distinguishing between small and large AAA.

miR-124a Involves in the Regulation of Wnt/β-Catenin and P53 Pathways to Inhibit Abdominal Aortic Aneurysm via Targeting BRD4

BACKGROUND

Abdominal aortic aneurysm (AAA) belongs to a progressive, gradual aortic rupture, which can lead to death without surgical intervention. The key factors regulating the occurrence and progress of AAA are not clear. Increasing studies have indicated that microRNA (miRNA) plays an important role in cancer development. miR-124a serves as a tumor suppressor in several neoplasms, and its upregulation can greatly inhibit the life activities such as malignant growth and migration of tumor cells.

AIM

The objective of this study is to explore the association of miR-124a with AAA and to uncover the regulated mechanism of miR-124a on AAA progression.

METHODS

The specimens from the AAA patients were used for observing the miR-124a expression, and human aortic endothelial cells (hAoECs) were treated with AngII to establish the AAA cell models. The quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR), CCK-8, transwell assay, flow cytometry assay, and western blot were conducted to unearth the regulation mechanism of miR-124a on AAA, and the dual-luciferase reporter assay was employed to investigate the downstream target of miR-124a.

RESULTS

miR-124a was significantly downregulated in the whole blood of the patients, and the decreased miR-124a was also observed in AAA cell models. Overexpressing miR-124a could effectively inhibit the proliferation and migration and promote the apoptosis of the AAA cells. The dual-luciferase reporter assay confirmed that BRD4 was a downstream target of miR-124a, and BRD4 upregulation could obviously reverse the effects of miR-124a on the phenotype of AAA cells. Moreover, it was found that miR-124a could regulate the activities of Wnt/β-catenin and P53 pathways via targeting the BRD4.

CONCLUSION

Our data suggested that miR-124a could regulate the activities of Wnt/β-catenin and P53 to suppress the AAA progression via targeting the BRD4.

Predictive value of elliptical neck parameters and oversizing ratio for type Ia endoleaks after endovascular aneurysm repair

OBJECTIVE

To assess the predictive value of elliptical geometric parameters regarding type IA endoleak (T1AEL) after endovascular aneurysm repair (EVAR) and investigate optimal range of oversizing ratio (OSR) in patients with elliptical aneurysmal neck.

METHODS

In this propensity-score matched case-control study, case patients were those diagnosed with T1AEL, control patients were those who did not have T1AEL after EVAR in the same period from 2012 to 2018. Geometric and oversizing parameters were compared based on both 2D and 3D measurement. Net reclassification improvement (NRI) was used to measure prediction increment of elliptical model (major axis OSR, neck length and severe neck angulation (SNA)) compared to conventional model (OSR 2D, neck length and SNA).

RESULTS

Nineteen case patients and 111 control patients were included. The median OSR 2D of T1AEL patients were 17% (15%-22%), but the median major axis OSR were only 7% (5%-12%). Among geometric parameters, axis difference had the highest area under the curve (AUC) of 0.74 (95%CI 0.63-0.84) in predicting T1AEL. As for elliptical oversizing parameters, major axis OSR had an AUC of 0.89 (95%CI 0.78-0.97) with a cut-off value of 13%. Elliptical model had higher discriminating ability in T1AEL compared to conventional model (AUC 0.91 vs 0.86, P=0.045), with improved reclassification ability (NRI 27.93%, 95% CI 19.22%-36.64%, P<0.0001).

CONCLUSION

Elliptical aneurysmal neck, assessed by axis difference, is associated with an increased risk of T1AEL. Calculation of oversizing based on major axis for at least 13% can significantly reduce the risk of T1AEL in patients with elliptical aneurysmal neck.

Low Shear Stress at Baseline Predicts Expansion and Aneurysm-Related Events in Patients With Abdominal Aortic Aneurysm

BACKGROUND

Low shear stress has been implicated in abdominal aortic aneurysm (AAA) expansion and clinical events. We tested the hypothesis that low shear stress in AAA at baseline is a marker of expansion rate and future aneurysm-related events.

METHODS

Patients were imaged with computed tomography angiography at baseline and followed up every 6 months >24 months with ultrasound measurements of maximum diameter. From baseline computed tomography angiography, we reconstructed 3-dimensional models for automated computational fluid dynamics simulations and computed luminal shear stress. The primary composite end point was aneurysm repair and/or rupture, and the secondary end point was aneurysm expansion rate.

RESULTS

We included 295 patients with median AAA diameter of 49 mm (interquartile range, 43-54 mm) and median follow-up of 914 (interquartile range, 670-1112) days. There were 114 (39%) aneurysm-related events, with 13 AAA ruptures and 98 repairs (one rupture was repaired). Patients with low shear stress (<0.4 Pa) experienced a higher number of aneurysm-related events (44%) compared with medium (0.4-0.6 Pa; 27%) and high (>0.6 Pa; 29%) shear stress groups (P=0.010). This association was independent of known risk factors (adjusted hazard ratio, 1.72 [95% CI, 1.08-2.73]; P=0.023). Low shear stress was also independently associated with AAA expansion rate (β=+0.28 mm/y [95% CI, 0.02-0.53]; P=0.037).

CONCLUSIONS

We show for the first time that low shear stress (<0.4 Pa) at baseline is associated with both AAA expansion and future aneurysm-related events. Aneurysms within the lowest tertile of shear stress, versus those with higher shear stress, were more likely to rupture or reach thresholds for elective repair. Larger prospective validation trials are needed to confirm these findings and translate them into clinical management.

Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms

It remains difficult to predict when which patients with abdominal aortic aneurysm (AAA) will require surgery. The aim was to study the accuracy of geometric and biomechanical analysis of small AAAs to predict reaching the threshold for surgery, diameter growth rate and rupture or symptomatic aneurysm. 189 patients with AAAs of diameters 40–50 mm were included, 161 had undergone two CTAs. Geometric and biomechanical variables were used in prediction modelling. Classifications were evaluated with area under receiver operating characteristic curve (AUC) and regressions with correlation between observed and predicted growth rates. Compared with the baseline clinical diameter, geometric-biomechanical analysis improved prediction of reaching surgical threshold within four years (AUC 0.80 vs 0.85, p = 0.031) and prediction of diameter growth rate (r = 0.17 vs r = 0.38, p = 0.0031), mainly due to the addition of semiautomatic diameter measurements. There was a trend towards increased precision of volume growth rate prediction (r = 0.37 vs r = 0.45, p = 0.081). Lumen diameter and biomechanical indices were the only variables that could predict future rupture or symptomatic AAA (AUCs 0.65–0.67). Enhanced precision of diameter measurements improves the prediction of reaching the surgical threshold and diameter growth rate, while lumen diameter and biomechanical analysis predicts rupture or symptomatic AAA.

Computation of a probabilistic and anisotropic failure metric on the aortic wall using a machine learning-based surrogate model

Scalar-valued failure metrics are commonly used to assess the risk of aortic aneurysm rupture and dissection, which occurs under hypertensive blood pressures brought on by extreme emotional or physical stress. To compute failure metrics under an elevated blood pressure, a classical patient-specific computer model consists of multiple computation steps involving inverse and forward analyses. These classical procedures may be impractical for time-sensitive clinical applications that require prompt feedback to clinicians. In this study, we developed a machine learning-based surrogate model to directly predict a probabilistic and anisotropic failure metric, namely failure probability (FP), on the aortic wall using aorta geometries at the systolic and diastolic phases. Ascending thoracic aortic aneurysm (ATAA) geometries of 60 patients were obtained from their CT scans, and biaxial mechanical testing data of ATAA tissues from 79 patients were collected. Finite element simulations were used to generate datasets for training, validation, and testing of the ML-surrogate model. The testing results demonstrated that the ML-surrogate can compute the maximum FP failure metric, with 0.42% normalized mean absolute error, in 1 s. To compare the performance of the ML-predicted probabilistic FP metric with other isotropic or deterministic metrics, a numerical case study was performed using synthetic "baseline" data. Our results showed that the probabilistic FP metric had more discriminative power than the deterministic Tsai-Hill metric, isotropic maximum principal stress, and aortic diameter criterion.

Systematic Review and Meta-Analysis of Peak Wall Stress and Peak Wall Rupture Index in Ruptured and Asymptomatic Intact Abdominal Aortic Aneurysms

Background Prior studies have suggested aortic peak wall stress (PWS) and peak wall rupture index (PWRI) can estimate the rupture risk of an abdominal aortic aneurysm (AAA), but whether these measurements have independent predictive ability over assessing AAA diameter alone is unclear. The aim of this systematic review was to compare PWS and PWRI in participants with ruptured and asymptomatic intact AAAs of similar diameter. Methods and Results Web of Science, Scopus, Medline, and The Cochrane Library were systematically searched to identify studies assessing PWS and PWRI in ruptured and asymptomatic intact AAAs of similar diameter. Random-effects meta-analyses were performed using inverse variance-weighted methods. Leave-one-out sensitivity analyses were conducted to assess the robustness of findings. Risk of bias was assessed using a modification of the Newcastle-Ottawa scale and standard quality assessment criteria for evaluating primary research papers. Seven case-control studies involving 309 participants were included. Meta-analyses suggested that PWRI (standardized mean difference, 0.42; 95% CI, 0.14-0.70; P=0.004) but not PWS (standardized mean difference, 0.13; 95% CI, -0.18 to 0.44; P=0.418) was greater in ruptured than intact AAAs. Sensitivity analyses suggested that the findings were not dependent on the inclusion of any single study. The included studies were assessed to have a medium to high risk of bias. Conclusions Based on limited evidence, this study suggested that PWRI, but not PWS, is greater in ruptured than asymptomatic intact AAAs of similar maximum aortic diameter.

Estimating aortic thoracic aneurysm rupture risk using tension-strain data in physiological pressure range: an in vitro study

Previous studies have shown that the rupture properties of an ascending thoracic aortic aneurysm (ATAA) are strongly correlated with the pre-rupture response features. In this work, we present a two-step machine learning method to predict where the rupture is likely to occur in ATAA and what safety reserve the structure may have. The study was carried out using ATAA specimens from 15 patients who underwent surgical intervention. Through inflation test, full-field deformation data and post-rupture images were collected, from which the wall tension and surface strain distributions were computed. The tension-strain data in the pressure range of 9-18 kPa were fitted to a third-order polynomial to characterize the response properties. It is hypothesized that the region where rupture is prone to initiate is associated with a high level of tension buildup. A machine learning method is devised to predict the peak risk region. The predicted regions were found to match the actual rupture sites in 13 samples out of the total 15. In the second step, another machine learning model is utilized to predict the tissue's rupture strength in the peak risk region. Results suggest that the ATAA rupture risk can be reasonably predicted using tension-strain response in the physiological range. This may open a pathway for evaluating the ATAA rupture propensity using information of in vivo response.

Biomechanical rupture risk assessment of abdominal aortic aneurysms using clinical data: A patient-specific, probabilistic framework and comparative case-control study

We present a data-informed, highly personalized, probabilistic approach for the quantification of abdominal aortic aneurysm (AAA) rupture risk. Our novel framework builds upon a comprehensive database of tensile test results that were carried out on 305 AAA tissue samples from 139 patients, as well as corresponding non-invasively and clinically accessible patient-specific data. Based on this, a multivariate regression model is created to obtain a probabilistic description of personalized vessel wall properties associated with a prospective AAA patient. We formulate a probabilistic rupture risk index that consistently incorporates the available statistical information and generalizes existing approaches. For the efficient evaluation of this index, a flexible Kriging-based surrogate model with an active training process is proposed. In a case-control study, the methodology is applied on a total of 36 retrospective, diameter matched asymptomatic (group 1, n = 18) and known symptomatic/ruptured (group 2, n = 18) cohort of AAA patients. Finally, we show its efficacy to discriminate between the two groups and demonstrate competitive performance in comparison to existing deterministic and probabilistic biomechanical indices.