A Novel computational approach to optimize Fontan and improve surgical predictability

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An efficient hemodynamics with minimal thrombosis risk post-surgery is essential for short- and long-term success of a cardiac surgery. Achieving this is a challenge in cardiac surgery that involves the design of a complex flow pathway. Aortic arch reconstruction, aneurysm repair, Fontan surgeries are a few examples. The Fontan surgical procedure is the most effective palliative treatment for patients with single ventricle defects (SVD). SVD refers to a collection of congenital heart diseases where one of the lower ventricular chambers of the heart remains underdeveloped. Fontan procedure involves re-routing of deoxygenated blood from upper and lower body to flow directly to lungs allowing the single functioning ventricle to pump blood for systemic circulation. Though lifesaving, the Fontan physiology creates a non-natural pathway for venous return of the blood to the lungs thus producing a non-physiological blood flow. A successful Fontan procedure should involve 1) well-balanced overall and hepatic venous flow return to lungs to prevent pulmonary arteriovenous malformations (PAVMs) that can lead to poor gas exchange, 2) minimal energy loss, and 3) minimal thrombosis (blood clot) risk. Complications such as PAVMs and thrombosis post-surgery can result in a Fontan failure. To improve Fontan surgical planning, its efficacy and predictability, we propose to develop an automated image-based computational fluid dynamics (CFD) workflow capable of optimizing and predicting all the above determinants for a successful Fontan physiology. CFD models have been developed in the past to assess energy loss and hepatic venous flow distribution, but an automated computational tool for rapidly optimizing the patients' Fontan physiology in terms of factors affecting success does not exist. To fill this gap, we will integrate our existing patient-specific Fontan surgical planning protocol to predict energy loss and hepatic venous flow distribution with 1) a shape optimization algorithm and 2) our validated model of blood coagulation to provide a computational tool to virtually improve the planned Fontan physiology for optimal hepatic and overall venous return to lungs, minimal energy loss and thrombotic potential and quantitatively predict thrombosis risk. We will completely automate our workflow with custom scripts to minimize errors and user intervention. Our biochemical model of blood coagulation has all the components representing platelet and fibrin deposition and is 2-way coupled with blood flow. The continuum-based approach of this model allows it to be used in large geometries. After rigorous validation of our surgical optimization workflow using MRI-based patient specific in-vitro models, we will perform virtual surgeries using our tool and retrospective patient data to establish clinical applicability. Our tool could potentially be 1) included in the current surgical planning workflow to perform virtual surgeries using patient pre-op data to improve and predict surgical outcomes, and 2) used to evaluate risk of clotting post- Fontan so that patients can be selectively monitored. Our long-term objective is to provide a prospective surgical planning tool for Fontan and then extend it other surgeries where such optimization can improve surgical efficacy.