APS Logo

Efficient, Multi-Fidelity Modelling of the Coagulation Cascade in Patient-Specific Left Atrial Flow

ORAL

Abstract

The left atrium (LA) is the most common site of cardiac thrombosis, associated with up to 30% of ischemic strokes. The coagulation cascade regulates thrombosis via a large biochemical network. Under flow, this cascade is governed by a system of dozens of 3D advection-reaction-diffusion partial differential equations (PDE). Solving these PDEs is computationally challenging due to their high dimensionality and multi-scale nature. Here, we leverage a recently developed Multi-Fidelity (MuFi) coagulation model that reduces the 3D PDE system into an equivalent ordinary differential equation (ODE) system. The MuFi model represents species concentration ui(x,t) as a function of the statistical moments of blood residence time, which are the only PDEs we need to solve. We apply a 9-species MuFi model to a database of LA flows (N=6 patients, 3 thrombus negative and 3 thrombus/TIA positive) to quantify patient-specific thrombin production. Residence time moments are obtained from the LA velocity fields calculated by LA with CFD analysis of 4D CT patient-specific images. Taking advantage of the MuFi model's low computational cost, we also present a sensitivity analysis of the effect of reaction kinetic constants.

Publication: "Efficient multi-fidelity computation of blood coagulation under flow"<br>Manuel Guerrero-Hurtado, Manuel Garcia-Villalba, Alejandro Gonzalo, Pablo Martinez-Legazpi, Andy M. Kahn, Elliot McVeigh, J. Bermejo, Juan C. del Alamo, Oscar Flores<br>bioRxiv 2023.05.29.542763; doi: https://doi.org/10.1101/2023.05.29.542763

Presenters

  • Manuel Guerrero-Hurtado

    University Carlos III of Madrid

Authors

  • Manuel Guerrero-Hurtado

    University Carlos III of Madrid

  • Eduardo Duran

    University of Malaga

  • Manuel García-Villalba

    TU Wien

  • Alejandro Gonzalo

    University of Washington

  • Pablo Martinez-Legazpi

    Universidad Nacional de Educación a Distancia, UNED

  • Andrew M Kahn

    University of California San Diego

  • Elliot McVeigh

    University of California San Diego

  • Javier Bermejo

    Hospital General Universitario Gregorio Marañón

  • Juan Carlos del Alamo

    University of Washington

  • Oscar Flores

    Univ Carlos III de Madrid