| Literature DB >> 23343002 |
Chiara Corsini1, Catriona Baker, Ethan Kung, Silvia Schievano, Gregory Arbia, Alessia Baretta, Giovanni Biglino, Francesco Migliavacca, Gabriele Dubini, Giancarlo Pennati, Alison Marsden, Irene Vignon-Clementel, Andrew Taylor, Tain-Yen Hsia, Adam Dorfman.
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.Entities:
Keywords: computational fluid dynamics; congenital heart disease; lumped parameter network; multi-scale model; virtual surgery
Mesh:
Year: 2013 PMID: 23343002 PMCID: PMC4242799 DOI: 10.1080/10255842.2012.758254
Source DB: PubMed Journal: Comput Methods Biomech Biomed Engin ISSN: 1025-5842 Impact factor: 1.763