Literature DB >> 23343002

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

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


  29 in total

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