Purpose Thrombosis ranks among the major complications in blood-carrying medical devices and a better understanding to influence the design related contribution to thrombosis is desirable. Over the past years many computational models of thrombosis have been developed. However, numerically cheap models able to predict localized thrombus risk in complex geometries are still lacking. The aim of the study was to develop and test a computationally efficient model for thrombus risk prediction in rotary blood pumps. Methods We used a two-stage approach to calculate thrombus risk. The first stage involves the computation of velocity and pressure fields by computational fluid dynamic simulations. At the second stage, platelet activation by mechanical and chemical stimuli was determined through species transport with an Eulerian approach. The model was compared with existing clinical data on thrombus deposition within the HeartMate II. Furthermore, an operating point and model parameter sensitivity analysis was performed. Results Our model shows good correlation (R2 > 0.93) with clinical data and identifies the bearing and outlet stator region of the HeartMate II as the location most prone to thrombus formation. The calculation of thrombus risk requires an additional 10–20 core hours of computation time. Conclusion The concentration of activated platelets can be used as a surrogate and computationally low-cost marker to determine potential risk regions of thrombus deposition in a blood pump. Relative comparisons of thrombus risk are possible even considering the intrinsic uncertainty in model parameters and operating conditions.

Thrombosis is one of the major complications in blood-carrying medical devices and a better understanding to influence design of such devices is desirable. Over the past years many computational models of thrombosis have been developed. However, open questions remain about the applicability and implementation within a pump development process. The aim of the study was to develop and test a computationally efficient model for thrombus risk prediction in rotary blood pumps. We used a two-stage approach to calculate thrombus risk. At the first stage, the velocity and pressure fields were computed by computational fluid dynamic (CFD) simulations. At the second stage, platelet activation by mechanical and chemical stimuli was determined through species transport with an Eulerian approach. The model was implemented in ANSYS CFX and compared with existing clinical data on thrombus deposition within the HeartMate II. Our model shows good correlation (R2>0.94) with clinical data and identifies the bearing and outlet stator region of the HeartMate II as the location most prone to thrombus formation. The calculation of platelet activation requires an additional 10-20 core hours of computation time. The concentration of activated platelets can be used as a surrogate marker to determine risk regions of thrombus deposition in a blood pump. Model expansion, e.g. by including more chemical species can easily be performed. We make our model openly available by implementing it for the FDA benchmark blood pump.