Biomechanical Rupture Risk Assessment - A consistent and objective decision-making tool for Abdominal Aortic Aneurysm patients

Abstract

Abdominal aortic aneurysm (AAA) rupture is a local event in the aneurysm wall that naturally demands tools to assess the risk for local wall rupture. Consequently, global parameters like the maximum diameter and its expansion over time can only give very rough risk indications; therefore, they frequently fail to predict individual risk for AAA rupture. In contrast, the Biomechanical Rupture Risk Assessment (BRRA) method investigates the wall's risk for local rupture by quantitatively integrating many known AAA rupture risk factors like female sex, large relative expansion, intraluminal thrombus-related wall weakening, and high blood pressure. The BRRA method is almost 20 years old and has progressed considerably in recent years, it can now potentially enrich the diameter indication for AAA repair. The present paper reviews the current state of the BRRA method by summarizing its key underlying concepts (i.e., geometry modeling, biomechanical simulation, and result interpretation). Specifically, the validity of the underlying model assumptions is critically disused in relation to the intended simulation objective (i.e., a clinical AAA rupture risk assessment). Next, reported clinical BRRA validation studies are summarized, and their clinical relevance is reviewed. The BRRA method is a generic, biomecha nics-based approach that provides several interfaces to incorporate information from different research disciplines. As an example, the final section of this review suggests integrating growth aspects to (potentially) further improve BRRA sensitivity and specificity. Despite the fact that no prospective validation studies are reported, a significant and still growing body of validation evidence suggests integrating the BRRA method into the clinical decision-making process (i.e., enriching diameterbased decision-making in AAA patient treatment). Problem DefinitionAbdominal aortic aneurysm (AAA) disease is a serious condition and causes many deaths, especially in males over 65 years. Progressive treatment (i.e., surgical or endovascular AAA repair) cannot be offered to all patients, and according to the best clinical practice, AAA repair is indicated if rupture risk exceeds the interventional risks. While center-specific treatment risks are reasonably predictable, assessing AAA rupture risk for individual patients remains the bottleneck in clinical decision making. However, an accurate rupture risk assessment is critical to reduce aneurysm-related mortality without substantially increasing the rate of AAA repair.According to the current clinical practice, AAA rupture risk is assessed by the aneurysm's largest

A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero‐pressure geometry of human thoracic aorta

“…Structural nonlinear finite element analysis (FEA) has been increasingly used to investigate the biomechanics of human organs, such as heart valves, ventricles, and blood vessels, for which in vivo organ geometries can be obtained from 3D imaging systems (eg, computed tomography). Since nonlinear FEA usually starts from the stress‐free state and the organs in in vivo images are often at a loaded state, organ geometries at the zero‐pressure level are needed, serving as a good approximation of the stress‐free state, for accurate stress analysis at loaded states . Methods to obtain zero‐pressure geometries may have potential applications well beyond FEA.…”

“…Since nonlinear FEA usually starts from the stress-free state and the organs in in vivo images are often at a loaded state, organ geometries at the zero-pressure level are needed, serving as a good approximation of the stress-free state, for accurate stress analysis at loaded states. 5,6 Methods to obtain zeropressure geometries may have potential applications well beyond FEA. Currently, by using 3D printing technology, a phantom model of a human organ can be built to have a patient-specific geometry reconstructed from 3D images 7 and similar mechanical properties of biological tissue.…”

Computational Methods in Cardiovascular Mechanics

“…The use of such an equivalent wall thickness seems to yield a more accurate prediction of the aneurysm rupture site. Anyway, in the case of mechanical simulations of aortic aneurysms, the assessment of wall thickness is still an open issue [Gasser, 2016], and in particular the assumption of uniform wall strength and thickness in FE models predicting aneurysm rupture seems to provide better results than using a variable wall thickness [Martufi et al, 2015]. As a matter of fact, most of the structural simulations dealing with the analysis of the deployment of endovascular devices and the consequent interaction with the arterial wall assume either an elastic wall with uniform thickness [Altnji et al, 2015, Perrin et al, 2015 or a wall represented as a rigid surface [Auricchio et al, 2013a, Cosentino et al, 2015.…”

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

“…To perform patient-specific biomechanical assessment, parameters of a failure metric (e.g., wall strength in the RPI) need to be quantified. Some studies suggested to use deterministic approaches [13,[22][23][24]. For instance, Geest et al [25] proposed a linear regression model to estimate wall strength of abdominal aortic aneurysm (AAA) from patient parameters (age, gender, maximum dimeter, family history and smoking status) and local parameters (local intraluminal thrombus (ILT) thickness and local diameter).…”