# A Novel computational approach to optimize Fontan and improve surgical predictability

> **NIH NIH R01** · BOSTON CHILDREN'S HOSPITAL · 2022 · $627,547

## Abstract

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 an...

## Key facts

- **NIH application ID:** 10346020
- **Project number:** 1R01HL161507-01
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Vijay Govindarajan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $627,547
- **Award type:** 1
- **Project period:** 2022-02-01 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10346020

## Citation

> US National Institutes of Health, RePORTER application 10346020, A Novel computational approach to optimize Fontan and improve surgical predictability (1R01HL161507-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10346020. Licensed CC0.

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