Analysis of Flow Dynamics and Outcomes of Cerebral Aneurysms Treated with Intrasaccular Flow-Diverting Devices

Data

A total of 42 aneurysms in 39 patients (35 women and 4 men; mean age, 64 years) treated with intrasaccular devices were studied. The aneurysms were imaged with 3D rotational angiography immediately before treatment. 2D-DSA images were acquired immediately before and after implantation of the intrasaccular devices. All aneurysms were treated with the Woven EndoBridge (WEB; Sequent Medical, Aliso Viejo, California) device,⁴ 33 with Dual-Layer (DL), 8 with Single-Layer (SL), and 1 with a Single-Layer Spherical (SLS) devices. Typically, the device size is chosen to be 1 mm larger in diameter compared with the aneurysm width and 1 mm smaller in length than the aneurysm depth (size from the neck to the dome), to allow the device to expand into the aneurysm dome and conform to the aneurysm walls. Of the 42 aneurysms, 6 were discarded because there were no follow-up angiographic studies to evaluate the treatment outcome. Patient, aneurysm, and device characteristics of the remaining 36 aneurysms in 33 patients are summarized in On-line Table 1.

Models

Patient-specific vascular models were constructed from the 3D rotational angiography images using previously developed methods.¹⁰ Unstructured grids composed of tetrahedral elements that fill the lumen of the vascular models were constructed using an advancing front grid generator.¹⁰ Models of the intrasaccular devices were created and interactively placed within the vascular models using DSA image-guidance tools and techniques recently developed.¹¹ Briefly, the vascular model is rendered semitransparent and is superimposed on the DSA image; it is then interactively rotated, zoomed, and translated to make sure it coincides with the vessels visible in the DSA image (ie, both have the same projection). Next, the device is interactively translated and rotated to make the virtual device markers coincide with the actual device markers visible in the DSA image. The process is repeated with different DSA images acquired in different projections (ie, different views) to guide the placement of the 3D virtual device within the vascular model. The process is illustrated with a representative case in On-line Fig 1.

Computational fluid dynamics simulations were performed by numerically solving the Navier-Stokes equations using an in-house finite-element solver.¹² Pulsatile flow conditions were imposed at the inlet boundary by scaling flow waveforms measured in healthy subjects.¹³ To simulate the blood flow after intrasaccular device implantation, we used an immersed boundary method operating on adaptive unstructured grids.¹⁴ In this approach, the device wires were discretized as a series of overlapping spheres and the mesh elements intersected by the spheres were adaptively refined to resolve the wires. Single-layer devices were modeled as a structure composed of 144 wires of 25 μm in diameter braided at an angle of 80°. Double-layer devices of >8 mm were modeled as an outer layer of 144 wires of 19 μm in diameter and an inner layer of 144 wires of 38 μm in diameter, both braided at an angle of 80°. Double-layer devices <8 mm had 108 wires in each layer instead of 144. Because these devices contain numerous thin wires, the resulting meshes after adaptive refinement were quite large, ranging between 100 and 200 million tetrahedra.

Flow simulations were performed for 2 cardiac cycles, and the results from the second cycle were used to compute a number of flow parameters to quantitatively characterize the aneurysm hemodynamic environment.¹⁵ In particular, these variables characterize the mean aneurysm inflow (Q) and concentration (ICI) of the inflow jet, and the mean aneurysm velocity (VE) and mean vorticity (VO, corelen) of the intra-aneurysmal flow pattern. Similarly, a number of geometric parameters were calculated to characterize the size (aneurysm size [Asize], neck size [Nsize]) and shape (aspect ratio [AR], bottleneck factor [BF], volume-to-ostium ratio [VOR], undulation index [UI], nonsphericity index [NSI]) of the aneurysms.¹⁶ A list of the geometric and hemodynamic variables used and their basic meaning are provided in On-line Table 2. Hemodynamic analysis was performed blinded to aneurysm occlusion outcomes.

Analysis

The outcomes of the treatments were evaluated at follow-up with angiography imaging. Aneurysms were classified into 4 categories: A) no remnant or filling of the aneurysm or device, B) small remnant associated with filling of the proximal recession of the device, C) filling of the proximal compartment of the device (dual-layer only), and D) filling of the distal compartment of the device. Categories A and B were grouped into a “complete” occlusion group, and categories C and D, into an “incomplete” occlusion group. Outcome assessment was performed blinded to the computational fluid dynamics results.

Hemodynamic variables computed for the pretreatment and posttreatment configurations, as well as their percentage change (reduction) from pre- to posttreatment, were compared between the complete and incomplete occlusion groups using the nonparametric Mann-Whitney test. Geometric characteristics of the aneurysms of these 2 groups were similarly compared. Differences were considered significant at P < .05 (95% confidence). P values were adjusted for multiple comparisons using the Bonferroni method. For each of the variables with statistically significant differences, univariate logistic regressions were performed, receiver operating characteristic curves were created, and the area under the curve (AUC) was calculated to assess the discriminatory power of each of these variables. Optimal thresholds were computed as the point on the receiver operating characteristic curve closest to the upper-left corner. Multivariate logistic regression analysis was performed with statistically significant variables. All statistical analyses were performed using codes written in R statistical and computing software (http://www.r-project.org/).