Using modeling and data analysis to study congenital vascular disease

Using modeling and data analysis to study congenital vascular disease

Lead: Mette Olufsen (Mathematics, NCSU)
Collaborator: Colleen Witzenberg (Bioengineering, University of Wisconsin, Madison)

Intellectual merit and significance:
One of the most common congenital defects is aortic coarctation (COA), a severe narrowing of the aorta, the main vessel transporting blood from the heart to the body. Every year 1 in 1,800 babies are born with COA [1]. The disease is challenging to diagnose and therefore often goes untreated [2]. One sign of COA is a significant pressure change between the hands and legs. Both treated and untreated COA patients have an increasing risk of coronary disease and hypertension [3]. Imaging data from this patient group typically is limited to the chest region, making it difficult to understand how COA impacts flow and pressure in the whole body.

This study will use fluid dynamics modeling [4] to combine imaging (MRI) and hemodynamic (blood flow and pressure) data measured before and after COA surgery. Using the model, we will perform insilico patient diagnoses and treatments to improve understanding of this disease.

Computational model integrating the cardiovascular data. Local and global sensitivity analysis and parameter inference methodology will be used to calibrate the model to data. The calibrated model will be used for insilico studies identifying markers essential for diagnosis and post-treatment. Statistical methodology will be used to compare outcomes between healthy and stenosed babies.

1. Mai, Isenburg, Canfield, Meyer, Correa, Alverson, Lupo, Riehle-Colarusso, Cho, Aggarwal, Kirby, National Birth Defects Prevention Network (2019). National population – based estimates for major birth defects, 2010-2014. Birth Defects Res 111:1420-1435.
2. Dijkema, Leiner, Grotenhuis (2017). Diagnosis, imaging, and clinical management of aortic coarctation. Heart 103:1148-1155.
3. O’Sullivan, Derrick, Darnell (2002). Prevalence of hypertension in children after early repair of coarctation of the aorta: a cohort study using casual and 24-hour blood pressure measurement data. Heart 88:163-166.
4. Witzenburgh, Dhume, Shah, Korenczuk, Wagner, Alford, Barocas (2017). Failure of the porcine ascending aorta: multidirectional experiments and a unifying microstructural model. J Biomech Eng. 139:031005.
5. Colebank, Qureshi, Rajagopal, Rasuski, Olufsen (2021). A multiscale model of vascular function in chronic thromboembolic pulmonary hypertension. Am J Physiol, 321:H318-H338.
6. Bartolo, Taylor-LaPole, Gandhi, Johnson, Li, Slack, Stevens, Turner, Puelz, Husmeier, Olufsen. (2024). Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images. arXiv:2309.08779v2.