An Accelerated Thrombosis Model for Computational Fluid Dynamics Simulations in Rotary Blood Pumps

Abstract

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.

A New Mathematical Numerical Model to Evaluate the Risk of Thrombosis in Three Clinical Ventricular Assist Devices

“…The thrombus model associated with the VADs requires the incorporation of complex flow characteristics within the VADs, including high shear stress and long residence times caused by the turbulence structure, complex geometries, and high rotating speeds. In addition, thrombus models should be implementable in computational CFD solvers, and ideally, such models should be compatible with commercial CFD software for broader adoption in the scientific and engineering communities [ 15 ]. Several researchers have assessed thrombotic risk by Lagrangian methods [ 16 , 17 , 18 , 19 , 20 , 21 ].…”

“…However, those models had not been applied in more complex flow geometries such as VADs. Furthermore, this models have been developed for laminar flow and resolves the formation time of a macroscopic thrombus which was around 30 min [ 15 ]. This proves to be impractical for the simulation of VAD where a high time resolution is required for adequate simulation accuracy.…”

“…More specifically, thrombus formation was predicted only in one region (front guide vanes) and not in other known/common regions such as the impeller or further downstream locations such as diffuser. Christopher et al [ 15 ] developed a simple thrombotic model applicable to VAD based on the study of Talyor et al [ 28 ]. Their model describes the interaction between resting platelets, activated platelets and ADP through three transport equations and applied the model to HMII.…”

A fibrin enhanced thrombosis model for medical devices operating at low shear regimes or large surface areas

“…In most device-related thrombosis models, platelet activity plays a central and oftentimes exclusive role. For example Blum et al [22] and Wu et al [26] developed models to primarily predict platelet activation and its effects in rotary blood pumps.…”

“…In most device-related thrombosis models, platelet activity plays a central and oftentimes exclusive role. For example Blum et al [22] and Wu et al [26] developed models to primarily predict platelet activation and its effects in rotary blood pumps. One of the disadvantages of an overemphasis on platelet activity is that these models cannot capture the clot formation initiated by factor XII adsorption which then activates the intrinsic pathway of the coagulation cascade [12, 27, 28].…”