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

Doyle, Barry; Bappoo, Nikhilesh; Syed, Maaz; Forsythe, Rachael; Powell, Janet T.; Conlisk, Noel; Hoskins, Peter; McBride, Olivia; Shah, Anoop; Norman, Paul; Newby, David E.

doi:10.1016/j.ejvs.2020.02.023

Cited by 35 publications

(36 citation statements)

References 34 publications

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“…Our work builds upon previous efforts by our group and collaborators regarding the biomechanical modeling and characterization of AAA in-vivo behavior [ 10 – 16 ], as well as several previous studies indicating that biomechanical indices are more accurate predictors for AAA rupture risk than the clinically established maximum diameter criterion [ 17 – 24 ]. In contrast to the approaches in [ 17 – 20 , 22 , 24 ], however, we advocate a probabilistic treatment to account for uncertain vessel wall properties. Our work thus goes along the lines of [ 21 ], but with the key difference that it includes the stiffness parameters of the AAA vessel wall as statistical quantities, uses patient-specific vessel wall properties and accounts for statistical correlations among these properties.…”

Section: Introductionmentioning

confidence: 75%

“…In addition with regression models [ 13 , 26 , 27 ] for the prediction of the individual wall strength, this enabled the definition of biomechanics-based indices [ 19 , 20 , 22 , 28 ], such as the rupture potential index (RPI) relating the von Mises stress to the wall strength. Furthermore, it could be shown [ 19 , 20 , 24 ] that these indices can be better rupture risk indicators than the maximum diameter criterion.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 75%

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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“…Examples of suggested alternative predictors include characteristics of the intraluminal thrombus (ILT) 5 , 19 – 22 , tracer imaging of AAA vessel wall metabolism, calcification processes and inflammation 23 – 25 as well as circulating markers of extracellular matrix degradation, inflammation, coagulation and microRNAs 26 . Further, finite element analysis (FEA) has in several retrospective case-control studies been reported to predict rupture 27 – 33 but also to predict growth or future surgery 34 , 35 . As of yet, none of these alternative markers have been included into clinical management algorithms.…”

Section: Introductionmentioning

confidence: 99%

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

Liljeqvist

Bogdanovič

Siika

et al. 2021

Sci Rep

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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.

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“…was used by Polzer and Gasser [26]. A prospective study [6] collected 13 ruptured cases in about 2~3 years of follow up period. In this study, ATAA risks (PRR) were retrospectively reconstructed by FE simulation using pre-operative images and tissue testing data of 41 patients who underwent elective surgery.…”

Section: Discussionmentioning

confidence: 99%

“…Currently, the clinical surgery criterion is primarily based on the aortic size and classifies an ascending thoracic aortic aneurysm (ATAA) as high risk if the (maximum) diameter is larger than 5.5cm [2,3], which may not accurately reflect a patient's risk [4,5]: some aneurysms at smaller diameters (e.g., < 4cm) can and do rupture [4]. As ATAA rupture and dissection are essentially mechanical events, patient-specific biomechanical assessment, such as structural finite element analysis (FEA), can provide a more accurate assessment of ATAA rupture/dissection risk [6,7]. Using 3D patientspecific aorta geometry and physiological blood pressure, aortic wall stresses can be computed using FEA.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic and Anisotropic Failure Metric for Ascending Thoracic Aortic Aneurysm Risk Stratification

Liu

Liang

Zou

et al. 2020

Preprint

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Experimental studies have shown that aortic wall tensile strengths in circumferential and longitudinal directions are different (i.e., anisotropic), and vary significantly among patients with aortic aneurysm. To assess aneurysm rupture and dissection risk, material failure metric of the aortic wall needs to be accurately defined and determined. Previously such risk assessment methods have largely relied on deterministic or isotropic failure metric. In this study, we develop a novel probabilistic and anisotropic failure metric for risk stratification of ascending thoracic aortic aneurysm (ATAA). To this end, uniaxial tensile tests were performed using aortic tissue samples of 84 ATAA patients, from which a joint probability distribution of the anisotropic wall strengths was obtained. Next, the anisotropic failure probability (FP) based on the Tsai−Hill (TH) failure criterion was derived. The novel FP metric, which incorporates uncertainty in the anisotropic failure properties, can be evaluated after the aortic wall stresses are computed from patient-specific biomechanical analysis. For method validation, “ground-truth” risks of additional 41 ATAA patients were numerically-reconstructed using corresponding CT images and tissue testing data. Performance of different risk stratification methods (e.g., with and without patient-specific hyperelastic properties) was compared using p-value and receiver operating characteristic (ROC) curve. The results show that: (1) the probabilistic FP metric outperforms the deterministic TH metric; and (2) patient-specific hyperelastic properties can help to improve the performance of probabilistic FP metric in ATAA risk stratification.

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

Abstract: Apologies -this was a typo that is now corrected. The correct presentation of the study is 'MA 3 RS' and we do not believe this will cause problems with the reader interpreting the superscript as a reference.

Cited by 35 publications

References 34 publications

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

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

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

A Probabilistic and Anisotropic Failure Metric for Ascending Thoracic Aortic Aneurysm Risk Stratification

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