Impact of Intracranial Aneurysm Morphology and Rupture Status on the Particle Residence Time

Hemodynamics of thrombus formation in intracranial aneurysms: An in silico observational study

How prevalent is spontaneous thrombosis in a population containing all sizes of intracranial aneurysms? How can we calibrate computational models of thrombosis based on published data? How does spontaneous thrombosis differ in normo- and hypertensive subjects? We address the first question through a thorough analysis of published datasets that provide spontaneous thrombosis rates across different aneurysm characteristics. This analysis provides data for a subgroup of the general population of aneurysms, namely, those of large and giant size (>10 mm). Based on these observed spontaneous thrombosis rates, our computational modeling platform enables the first in silico observational study of spontaneous thrombosis prevalence across a broader set of aneurysm phenotypes. We generate 109 virtual patients and use a novel approach to calibrate two trigger thresholds: residence time and shear rate, thus addressing the second question. We then address the third question by utilizing this calibrated model to provide new insight into the effects of hypertension on spontaneous thrombosis. We demonstrate how a mechanistic thrombosis model calibrated on an intracranial aneurysm cohort can help estimate spontaneous thrombosis prevalence in a broader aneurysm population. This study is enabled through a fully automatic multi-scale modeling pipeline. We use the clinical spontaneous thrombosis data as an indirect population-level validation of a complex computational modeling framework. Furthermore, our framework allows exploration of the influence of hypertension in spontaneous thrombosis. This lays the foundation for in silico clinical trials of cerebrovascular devices in high-risk populations, e.g., assessing the performance of flow diverters in aneurysms for hypertensive patients.

The effect of the size of the new contour neurovascular device for altering intraaneurysmal flow

BACKGROUND

Recently, a novel intrasaccular device (contour neurovascular system, contour) was introduced to treat intracranial aneurysms. Contour is placed at thе aneurysm neck and reduces the intraaneurysmal blood inflow. Contour comes in a range of sizes to target different aneurysms. The efficiency of altering flow with contour and the effect of device size have not yet been investigated. Therefore, we studied the effect of the device size with patient-based aneurysm models using 2D digital subtraction angiography (DSA).

METHODS

Three patient-based aneurysm models with necks ranging from 2.7 to 9.7 mm were produced, providing standardized testing conditions. Contours with diameters of 5, 11, and 14 mm were implanted into the models, four of each size. 2D DSA images were acquired before and after implanting contour (15 frames/s, manual contrast injection). After injecting angiographic contrast agent, the DSA signal was recorded over time to calculate the contrast washout time (WOT), which is a measure of flow diversion efficiency.

RESULTS

All contour devices caused contrast agent stasis and increased WOT in aneurysm sac (p-value = 0.0005). The median relative WOT was largest for 5-mm contour (6.6 ± 3.2) and similar for 11-mm contour (3.4 ± 2.6) and 14-mm contour (3.2 ± 3.8). The implantation procedure might affect WOT values even for contours of the same size; the overall relative WOT ranged between 1.5 and 10.89.

CONCLUSION

The 5-mm contour showed the longest WOT value in our study, while no apparent difference between 11-mm contour and 14-mm contour was found.

Development and validation of a novel nomogram to predict aneurysm rupture in patients with multiple intracranial aneurysms: a multicentre retrospective study

Background and purpose

Approximately 15%–45% of patients with unruptured intracranial aneurysms have multiple intracranial aneurysms (MIAs). Determining which one is most likely to rupture is extremely important for treatment decision making for MIAs patients. This study aimed to develop and validate a nomogram to evaluate the per-aneurysm rupture risk of MIAs patients.

Methods

A total of 1671 IAs from 700 patients with MIAs were randomly dichotomised into derivation and validation sets. Multivariate logistic regression analysis was used to select predictors and construct a nomogram model for aneurysm rupture risk assessment in the derivation set. The discriminative accuracy, calibration performance and clinical usefulness of this nomogram were assessed. We also developed a multivariate model for a subgroup of 158 subarachnoid haemorrhage (SAH) patients and compared its performance with the nomogram model.

Results

Multivariate analyses identified seven variables that were significantly associated with IA rupture (history of SAH, alcohol consumption, female sex, aspect ratio >1.5, posterior circulation, irregular shape and bifurcation location). The clinical and morphological-based MIAs (CMB-MIAs) nomogram model showed good calibration and discrimination (derivation set: area under the curve (AUC)=0.740 validation set: AUC=0.772). Decision curve analysis demonstrated that the nomogram was clinically useful. Compared with the nomogram model, the AUC of multivariate model developed from SAH patients had lower value of 0.730.

Conclusions

This CMB-MIAs nomogram for MIAs rupture risk is the first to be developed and validated in a large multi-institutional cohort. This nomogram could be used in decision-making and risk stratification in MIAs patients.