Discrimination of intracranial aneurysm rupture status: patient-specific inflow boundary may not be a must-have condition in hemodynamic simulations

Background Computational fluid dynamics (CFD) are important in evaluating the hemodynamics of intracranial aneurysm rupture, and the setting of inflow boundary conditions is critical. We evaluated intracranial aneurysm hemodynamics based on generalized versus patient-specific inflow boundary conditions to examine the effect of different hemodynamic results on the discrimination of intracranial aneurysm rupture status. Methods We enrolled 148 patients with 156 intracranial aneurysms. For each included aneurysm, we performed CFD simulation once based on patient-specific and once based on generalized inflow boundary conditions. First, we compared the hemodynamics of intracranial aneurysms based on different inflow boundary conditions. Then, we divided the included aneurysms into a ruptured and unruptured group and compared the hemodynamics between the two groups under patient-specific and generalized inflow boundary conditions. Results For the hemodynamic parameters using specific inflow boundary conditions, more complex flow (p = 0.002), larger minimum WSS (p = 0.024), lower maximum low WSS area (LSA) (p = 0.038), and oscillatory shear index (p = 0.002) were found. Furthermore, we compared the hemodynamics between ruptured and unruptured groups based on different inflow boundary conditions. We found that the significant hemodynamic parameters associated with rupture status were the same, including the proportion of aneurysms with flow complex and unstable flow and the minimum and maximum of LSA (p = 0.011,p = 0.003,p = 0.001 andp = 0.004, respectively). Conclusion Patient-specific and generalized inflow boundary conditions of aneurysmal hemodynamics resulted in significant differences. However, the significant parameters associated with rupture status were the same in both conditions, indicating that patient-specific inflow boundary conditions may not be necessary for predicting rupture risk.