Analyzing data from patients with pulmonary hypertension

Faculty Mentors:

Mette Olufsen (Math, NCSU)
Naomi Chesler (Biomed Eng, UC Irvine)

Prerequisites: Differential equations, interest in biology, basic programming.

Outline: Pulmonary hypertension is a rare but deadly disease, which requires both imaging and invasive measurements to diagnose [1]. The disease is often detected late as it shares symptoms with several other diseases, and it is not easy to determine how successful the given treatments are. To do so requires integrating imaging data with dynamic measurements [2] and computational fluid dynamics [3].

Objectives: A validated fluid mechanics model integrating images and dynamic blood pressure data from right heart catheterization that can predict the load on the heart at rest and exercise.

Outcomes: Mathematical model integrating imaging data with dynamic measurements; local and global sensitivity analysis determining what model parameters impact predictions of blood pressure and flow at rest and during exercise (5 min walk test); a study of what parameters can be identified given the model and data; and simulations predicting effects of vasodilatory treatment at rest and during exercise.

References:

[1] Noordegraaf, A., J. Groeneveldt, and H. Bogaard, Pulmonary hypertension. Eurp Resp Rev, 2016. 25: p. 4-11.

[2] Chambers, M.J., et al., Structural and hemodynamic properties of murine pulmonary arterial networks under hypoxia-induced pulmonary hypertension. Proc Inst Mech Eng H, 2020. 234: p. 1312-1329.

[3] Colebank, M., et al., A multiscale model of vascular function in chronic thromboembolic pulmonary hypertension. Am J Physiol, 2021. 321: p. H318-H338.