4.5 Article

Personalized Perioperative Multi-scale, Multi-physics Heart Simulation of Double Outlet Right Ventricle

Journal

ANNALS OF BIOMEDICAL ENGINEERING
Volume 48, Issue 6, Pages 1740-1750

Publisher

SPRINGER
DOI: 10.1007/s10439-020-02488-y

Keywords

Congenital heart disease; Computer simulation; Double outlet right ventricle

Funding

  1. MEXT as 'Priority Issue on Post-K-computer' (Integrated Computational Life Science to Support Personalized and Preventive Medicine) [hp160209, hp150260]
  2. Japan Society for the Promotion of Science (JSPS) through its 'Funding Program for World-Leading Innovative R&D on Science and Technology (FIRST Program)'

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For treatment of complex congenital heart disease, computer simulation using a three-dimensional heart model may help to improve outcomes by enabling detailed preoperative evaluations. However, no highly integrated model that accurately reproduces a patient's pathophysiology, which is required for this simulation has been reported. We modelled a case of complex congenital heart disease, double outlet right ventricle with ventricular septal defect and atrial septal defect. From preoperative computed tomography images, finite element meshes of the heart and torso were created, and cell model of cardiac electrophysiology and sarcomere dynamics was implemented. The parameter values of the heart model were adjusted to reproduce the patient's electrocardiogram and haemodynamics recorded preoperatively. Two options of in silico surgery were performed using this heart model, and the resulting changes in performance were examined. Preoperative and postoperative simulations showed good agreement with clinical records including haemodynamics and measured oxyhaemoglobin saturations. The use of a detailed sarcomere model also enabled comparison of energetic efficiency between the two surgical options. A novel in silico model of congenital heart disease that integrates molecular models of cardiac function successfully reproduces the observed pathophysiology. The simulation of postoperative state by in silico surgeries can help guide clinical decision-making.

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