A multi-constituent model of thrombosis for blood-contacting medical devices

Ventricular assist devices (VADs) are currently effective clinical interventions for treating diseases related to end-stage heart failure (HF), yet hemocompatibility-related complications persist as prevalent challenges. Among them, device-induced thrombosis may lead to severe consequences such as stroke, neurological events, pump replacement, and even mortality, making it critically important to predict thrombosis accurately. The primary objective of this study is to develop a thrombosis model capable of simulating thrombus formation within VADs.

The proposed model integrates hemodynamics, platelet activity, and the coagulation cascade, wherein the cascade products regulate platelet activation, aggregation, and stabilization. To enable simulations at the device scale while preserving essential physiological mechanisms, a reduced-order coagulation cascade model was adopted. Furthermore, the model incorporates hemodynamic-thrombus interaction while thrombus breakdown due to the shear stress clearance is under consideration.

The model was first validated in a backward-facing step (BFS) geometry to assess its applicability under separated flow conditions. In terms of volumetric evolution, the simulation followed a trend consistent with experimental data, while the thrombus length and height matched the experimental measurements closely. The model was then applied to a left ventricular assist device (VAD) to explore thrombus formation mechanisms. Simulations at different flow rates revealed consistent thrombosis on the straightener blades, while initiation sites and growth dynamics were governed by local hemodynamics, platelet activation, and stabilization.

The thrombosis model developed in this study enables the investigation of thrombus formation mechanisms and the identification of potential high-risk regions within VADs under varying flow conditions. It provides a basis for future experimental validation and has potential utility for optimizing VAD design and informing patient-specific risk assessment.