Use of Numerical Simulation to Predict Iliac Complications During Placement of An Aortic Stent Graft.

BACKGROUND: During endovascular aneurysm repair (EVAR), complex iliac anatomy is a source of complications such as unintentional coverage of the hypogastric artery. The aim of our study was to evaluate ability to predict coverage of the hypogastric artery using a biomechanical model simulating arterial deformations caused by the delivery system. METHODS: The biomechanical model of deformation has been validated by many publications. The simulations were performed on 38 patients included retrospectively, for a total of 75 iliac arteries used for the study. On the basis of objective measurements, two groups were formed: one with "complex" iliac anatomy (n = 38 iliac arteries) and the other with "simple" iliac anatomy (n = 37 iliac arteries). The simulation enabled measurement of the lengths of the aorta and the iliac arteries once deformed by the device. Coverage of the hypogastric artery was predicted if the deformed renal/iliac bifurcation length (Lpre) was less than the length of the implanted device (Lstent-measured on the postoperative computed tomography [CT]) and nondeformed Lpre was greater than Lstent. RESULTS: Nine (12%) internal iliac arteries were covered unintentionally. Of the coverage attributed to perioperative deformations, 1 case (1.3%) occurred with simple anatomy and 6 (8.0%) with complex anatomy (P = 0.25). All cases of unintentional coverage were predicted by the simulation. The simulation predicted hypogastric coverage in 35 cases (46.7%). There were therefore 26 (34.6%) false positives. The simulation had a sensitivity of 100% and a specificity of 60.6%. On multivariate analysis, the factors significantly predictive of coverage were the iliac tortuosity index (P = 0.02) and the predicted margin between the termination of the graft limb and the origin of the hypogastric artery in nondeformed (P = 0.009) and deformed (P = 0.001) anatomy. CONCLUSIONS: Numerical simulation is a sensitive tool for predicting the risk of hypogastric coverage during EVAR and allows more precise preoperative sizing. Its specificity is liable to be improved by using a larger cohort.

Influencing factors of sac shrinkage after endovascular aneurysm repair.

OBJECTIVE: Sac shrinkage is considered a reliable surrogate marker of success after endovascular aneurysm repair (EVAR). Whereas sac shrinkage is the best expected outcome, predictive factors of sac shrinkage remain unclear. The aim of this study was to identify the role of preoperative and postoperative influencing factors of sac reduction after EVAR. METHODS: Online searches across MEDLINE, Embase, and Cochrane Library medical databases were simultaneously performed. Study effects were pooled using a random-effects model, and forest plots were generated for every potential influencing factor. RESULTS: A total of 24 studies with 14,754 patients were included (mean age, 73.4 years; 76% male). At a mean follow-up of 24 months, the pooled shrinkage proportion was 47%. Random-effects meta-analysis revealed that renal impairment (odds ratio [OR], 0.74; 95% confidence interval [CI], 0.57-0.96), type I endoleaks (OR, 0.17; 95% CI, 0.08-0.39), type II endoleaks (OR, 0.21; 95% CI, 0.14-0.33), and combined type I and type II endoleaks (OR, 0.32; 95% CI, 0.22-0.47) were found to prevent sac shrinkage, whereas hypercholesterolemia (OR, 1.24; 95% CI, 1.02-1.51) and smoking (OR, 1.32; 95% CI, 1.17-1.49) have a significant positive impact on sac shrinkage. In addition, there was a trend toward the association between shrinkage and statin therapy (OR, 4.07; 95% CI, 1.02-16.32) and nearly significant negative impacts of coronary artery disease (OR, 0.84; 95% CI, 0.70-1.01), diabetes (OR, 0.79; 95% CI, 0.60-1.04), and sac thrombus (OR, 0.88; 95% CI, 0.77-1.01) on sac shrinkage. CONCLUSIONS: In this large meta-analysis of patients undergoing EVAR, we found that several comorbidity and postoperative factors were associated with postoperative sac shrinkage. These findings may contribute to a better understanding of the shrinkage process of patients undergoing EVAR.

Interest of fusion imaging and modern navigation tools with hybrid rooms in endovascular aortic procedures.

Because of the emergence of hybrid operating rooms, cone-beam CT scans (CBCT) allow new intraoperative imaging to be produced. Image fusion (3D preoperative CT scan overlaid onto 2D live fluoroscopy image) is the most popular application and makes it possible to navigate throughout the aorta and its branches without having to make use of an additional injection, and allows a reduction to be achieved in the quantity of contrast medium and irradiation required during complex procedures. Planning-oriented software available in hybrid rooms makes it possible to adjust to the patient and the nature of the procedure, the information that is relevant during the operation. CBCT can also be used as a diagnostic tool at the end of a procedure for the detection of endoleaks and could replace routine CT scans made during the first month following the procedure, indirectly contributing again to a reduction of X-ray and contrast agent doses.

Finite element simulation of the insertion of guidewires during an EVAR procedure: example of a complex patient case, a first step toward patient-specific parameterized models.

Deformations of the vascular structure due to the insertion of tools during endovascular treatment of aneurysms of the abdominal aorta, unless properly anticipated during the preoperative planning phase, may be the source of intraoperative or postoperative complications. We propose here an explicit finite element simulation method which enables one to predict such deformations. This method is based on a mechanical model of the vascular structure which takes into account the nonlinear behavior of the arterial wall, the prestressing effect induced by the blood pressure and the mechanical support of the surrounding organs and structures. An analysis of the model sensitivity to the parameters used to represent this environment is done. This allows determining the parameters that have the largest influence on the quality of the prediction and also provides realistic values for each of them as no experimental data are available in the literature. Moreover, for the first time, the results are compared with 3D intraoperative data. This is done for a patient-specific case with a complex anatomy in order to assess the feasibility of the method. Finally, the predictive capability of the simulation is evaluated on a group of nine patients. The error between the final simulated and intraoperatively measured tool positions was 2.1 mm after the calibration phase on one patient. It results in a 4.6 ± 2.5 mm in average error for the blind evaluation on nine patients.

A structural model of passive skeletal muscle shows two reinforcement processes in resisting deformation.

Passive skeletal muscle derives its structural response from the combination of the titin filaments in the muscle fibres, the collagen fibres in the connective tissue and incompressibility due to the high fluid content. Experiments have shown that skeletal muscle tissue presents a highly asymmetrical three-dimensional behaviour when passively loaded in tension or compression, but structural models predicting this are not available. The objective of this paper is to develop a mathematical model to study the internal mechanisms which resist externally applied deformation in skeletal muscle bulk. One cylindrical muscle fibre surrounded by connective tissue was considered. The collagenous fibres of the endomysium and perimysium were grouped and modelled as tension-only oriented wavy helices wrapped around the muscle fibre. The titin filaments are represented as non-linear tension-only springs. The model calculates the force developed by the titin molecules and the collagen network when the muscle fibre undergoes an isochoric along-fibre stretch. The model was evaluated using a range of literature based input parameters and compared to the experimental fibre-direction stress-stretch data available. Results show the fibre direction non-linearity and tension/compression asymmetry are partially captured by this structural model. The titin filament load dominates at low tensile stretches, but for higher stretches the collagen network was responsible for most of the stiffness. The oblique and initially wavy collagen fibres account for the non-linear tensile response since, as the collagen fibres are being recruited, they straighten and re-orient. The main contribution of the model is that it shows that the overall compression/tension response is strongly influenced by a pressure term induced by the radial component of collagen fibre stretch acting on the incompressible muscle fibre. Thus for along-fibre tension or compression the model predicts that the collagen network contributes to overall muscle stiffness through two different mechanisms: (1) a longitudinal force directly opposing tension and (2) a pressure force on the muscle fibres resulting in an indirect longitudinal load. Although the model presented considers only a single muscle fibre and evaluation is limited to along-fibre loading, this is the first model to propose these two internal mechanisms for resisting externally applied deformation of skeletal muscle tissue.

The anisotropic mechanical behaviour of passive skeletal muscle tissue subjected to large tensile strain.

The passive mechanical properties of muscle tissue are important for many biomechanics applications. However, significant gaps remain in our understanding of the three-dimensional tensile response of passive skeletal muscle tissue to applied loading. In particular, the nature of the anisotropy remains unclear and the response to loading at intermediate fibre directions and the Poisson's ratios in tension have not been reported. Accordingly, tensile tests were performed along and perpendicular to the muscle fibre direction as well as at 30°, 45° and 60° to the muscle fibre direction in samples of Longissimus dorsi muscle taken from freshly slaughtered pigs. Strain was measured using an optical non-contact method. The results show the transverse or cross fibre (TT') direction is broadly linear and is the stiffest (77 kPa stress at a stretch of 1.1), but that failure occurs at low stretches (approximately λ=1.15). In contrast the longitudinal or fibre direction (L) is nonlinear and much less stiff (10 kPa stress at a stretch of 1.1) but failure occurs at higher stretches (approximatelyλ=1.65). An almost sinusoidal variation in stress response was observed at intermediate angles. The following Poisson's ratios were measured: VLT=VLT'=0.47, VTT'=0.28 and VTL=0.74. These observations have not been previously reported and they contribute significantly to our understanding of the three dimensional deformation response of skeletal muscle tissue.