Parameter selection and optimization of a computational network model of blood flow in single-ventricle patients.

Abstract

Hypoplastic left heart syndrome (HLHS) is a congenital heart disease responsible for 23% of infant cardiac deaths each year in the United States. 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 often have a reduced cardiac output, putting them at risk for a multitude of complications. These patients are monitored using chest and neck magnetic resonance imaging (MRI), but their scans do not capture energy loss, pressure, wave intensity or haemodynamics 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 with those from control patients with a double outlet right ventricle (DORV). We infer patient-specific parameters through the proposed framework. In the calibrated model, we predict pressure, flow, wave intensity (WI) and wall shear stress (WSS). Results reveal that HLHS patients have 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.