Understanding the formation of spontaneous thrombosis inside aneurysms

The spontaneous formation of clots, called thrombosis, in brain aneurysms is a life-threatening event that can lead to strokes and remains poorly understood. Current medical diagnostics can only provide a limited view of the altered blood flow inside aneurysms that lead to such events, and their unpredictable nature make them difficult to monitor for and investigate.

Liu et al. developed a fully automated in silico model to simulate blood flow dynamics in intracranial aneurysms. They calibrated it using clinical data from a systematic literature review and performed numerical simulations on a virtual cohort of 109 patients.

The study revealed insights into the prevalence of spontaneous thrombosis in aneurysms and investigated its relation with various morphological and physiological characteristics, including the effects of hypertension.

“Our work showcases how in silico models can generate insights into conditions where real-life data is scarce or difficult to collect,” said author Alejandro Frangi. “Through the calibration of our predictive model using evidence from a meta-analysis, we expanded our knowledge of spontaneous thrombosis.”

A significant challenge faced by the researchers was the limited understanding of the prevalence of spontaneous thrombosis. Their calibrated model identified critical parameters for thrombosis trigger thresholds. They discovered that larger aneurysms with higher aspect ratios are more likely to develop thrombosis, and surprisingly, the prevalence of thrombosis might be slightly lower in hypertensive patients.

The researchers plan to focus future projects on using their calibrated model to study and develop medical devices for treating brain aneurysms.

Source: “Hemodynamics of thrombus formation in intracranial aneurysms: An in-silico observational study,” by Qiongyao Liu, Ali Sarrami-Foroushani, Yongxing Wang, Michael MacRaild, Christopher Kelly, Fengming Lin, Yan Xia, Shuang Song, Nishant Ravikumar, Tufail Patankar, Zeike A. Taylor, Toni Lassila, and Alejandro F. Frangi, APL Bioengineering (2023). The article can be accessed at https://doi.org/10.1063/5.0144848 .