Parameter selection and optimization of a computational network model of blood flow in single-ventricle patients
Hypoplastic left heart syndrome (HLHS) is a congenital heart disease responsible for 23% of infant cardiac deaths each year. HLHS patients are born with an underdeveloped left heart, requiring several surgeries to reconstruct the aorta and create a single ventricle circuit known as the Fontan circul...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
26.06.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Hypoplastic left heart syndrome (HLHS) is a congenital heart disease
responsible for 23% of infant cardiac deaths each year. HLHS patients are born
with an underdeveloped left heart, requiring several surgeries to reconstruct
the aorta and create a single ventricle circuit known as the Fontan
circulation. While survival into early adulthood is becoming more common,
Fontan patients suffer from reduced cardiac output, putting them at risk for a
multitude of complications. These patients are monitored using chest and neck
MRI imaging, but these scans do not capture energy loss, pressure, wave
intensity, or hemodynamics beyond the imaged region. This study develops a
framework for predicting these missing features by combining imaging data and
computational fluid dynamics (CFD) models. Predicted features from models of
HLHS patients are compared to those from control patients with a double outlet
right ventricle (DORV). We use parameter inference to render the model
patient-specific. In the calibrated model, we predict pressure, flow,
wave-intensity (WI), and wall shear stress (WSS). Results reveal that HLHS
patients have higher vascular stiffness and lower compliance than DORV
patients, resulting in lower WSS and higher WI in the ascending aorta and
increased WSS and decreased WI in the descending aorta. |
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DOI: | 10.48550/arxiv.2406.18490 |