DOI: 10.5061/dryad.zpc866tj0
title: Data for: Parameter selection and optimization of a computational network model of blood flow in single-ventricle patients

description: 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 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 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.
description: # Data for: Parameter selection and optimization of a computational
 network model of blood flow in single-ventricle patients
 [https://doi.org/10.5061/dryad.zpc866tj0](https://doi.org/10.5061/dryad.zpc866tj0) ## Description of the data and file structure Data include four pairs of age- and sized-matched HLHS and DORV patients seen at a hospital in Houston, TX. Data collection was approved by the Baylor College of Medicine Institutional Review Board (H-46224: “Four-Dimensional Flow Cardiovascular Magnetic Resonance for the Assessment of Aortic Arch Properties in Single Ventricle Patients”). To isolate the differences in flow properties based on native versus reconstructed aortas, single ventricle morphologies were selected to have the same dominant single right ventricle. The DORV group includes patients with a single ventricle anatomy with right ventricular morphology and a native arch. The HLHS group includes patients with a single ventricle anatomy with right ventricular morphology and reconstructed aortas. Each cohort have similar atrioventricular valve regurgitation, achieved total cavopulmonary anastomosis (non-fenestrated), and preserved right ventricular systolic function. Exclusion criteria include patients with aortic surgery beyond initial Norwood, significant collateral burden &lt;40%, systemic hypertension, and those with heart failure. Patients were matched across cohorts (DORV to HLHS) based on patient body surface area and age. ### Files and variables The code operates using Matlab as the wrapper, C for the fluids simulation, and Fortran for the numerical solver. The majority of variables and how the code operates are commented into the code itself To run the fluids code:  1. Make sure that in the MakeFile the location of your gcc and gfortran compilers are defined for your machine. If using a Mac, make sure you also have Xcode downloaded. 2. run_1D.m runs the code. The connectivity can be defined manually as the connectivity matrix seen in the uploaded code, or read in from the file (see [https://github.com/msolufse/CDG_NCSU/tree/master/VascularTreeFromImages](https://github.com/msolufse/CDG_NCSU/tree/master/VascularTreeFromImages))  To run sensitivities: 1. Make sure you have run the fluids code and have not cleared any of your workspace 2. Global sensitivities - run_morris.m 3. Local sensitivities - run*_*local_sens.m To run the optimizer: 1. The run file is run_allparam_alg.m 2. The model is called in Run_allparam_Simulator.m 3. The cost function is constructed and the optimizer runs in Inflow_algorithm.m 4. Vessels where data is defined and extracting the same points model predictions are in ExtractData.m ## Code/software Plotting was done in Matlab. For image analysis, we use 3D Slicer, Paraview, and VMTK which are described in detail in the manuscript.