An integrated approach to patient-specific predictive modeling for single ventricle heart palliation

Abstract : In patients with congenital heart disease and a single ventricle (SV), ventricular support of the circulation is inadequate, and staged palliative surgery (usually 3 stages) is needed for treatment. In the various palliative surgical stages individual differences in the circulation are important and patient-specific surgical planning is ideal. In this study, an integrated approach between clinicians and engineers has been developed, based on patient-specific multi-scale models, and is here applied to predict stage 2 surgical outcomes. This approach involves four distinct steps: (1) collection of pre-operative clinical data from a patient presenting for SV palliation, (2) construction of the pre-operative model, (3) creation of feasible virtual surgical options which couple a three-dimensional model of the surgical anatomy with a lumped parameter model (LPM) of the remainder of the circulation and (4) performance of post-operative simulations to aid clinical decision making. The pre-operative model is described, agreeing well with clinical flow tracings and mean pressures. Two surgical options (bi-directional Glenn and hemi-Fontan operations) are virtually performed and coupled to the pre-operative LPM, with the hemodynamics of both options reported. Results are validated against postoperative clinical data. Ultimately, this work represents the first patient-specific predictive modeling of stage 2 palliation using virtual surgery and closed-loop multi- scale modeling.
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Journal articles
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https://hal.inria.fr/hal-00918643
Contributor : Gregory Arbia <>
Submitted on : Friday, December 13, 2013 - 8:14:25 PM
Last modification on : Monday, July 15, 2019 - 5:58:03 PM

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Chiara Corsini, Catriona Baker, Ethan Kung, Silvia Schievano, Grégory Arbia, et al.. An integrated approach to patient-specific predictive modeling for single ventricle heart palliation. Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis, 2013, ⟨10.1080/10255842.2012.758254⟩. ⟨hal-00918643⟩

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