(B-44) Computational Analysis of Disease Progression in Pediatric Pulmonary Arterial Hypertension

Pulmonary Arterial Hypertension (PAH) is a severe, progressive cardiopulmonary disease characterized by elevated blood pressure in the pulmonary arteries. PAH patients experience structural and functional changes in the pulmonary vasculature, resulting in hemodynamic alterations that can eventually lead to right ventricular (RV) failure [1]. Changes in hemodynamics, such as elevated pressure, can trigger adaptations in the pulmonary arterial wall, such as stiffening and thickening. Such structural adaptations can lead to further increases in pressure, creating a positive feedback loop that plays a critical role in the progression of PAH [2]. Therefore, it is essential to characterize disease progression by using metrics that can aid in the prognosis of PAH. 

This study aims to investigate PAH progression in children by leveraging longitudinal patient data to build patient-specific computational hemodynamic models. To do so, we are expanding upon a previous study [3], where our team described methods for calibrating the computational models, and correlated data-derived and model-derived metrics to disease severity, using data from a single timepoint. Specifically, we aim to leverage a prospective longitudinal patient dataset and computational models to yield patient-specific metrics at two timepoints separated by ~2 years. This will enable us to evaluate correlations between metrics and PAH progression.

Clinical Data: Clinical data on anatomy, flow, and pressure were prospectively acquired using MRI and pressure catheterizations in six pediatric PAH patients (ClinicalTrials.gov ID No. NCT03564522). Two separate datasets (baseline and follow-up) were captured for each patient, separated approximately 2 years, to study disease progression. In this abstract, we report results for 2 subjects, Patient #1 and Patient #2.

Computational Modeling: Using the CRIMSON software [4], patient-specific closed-loop 3D fluid structure interaction (FSI) models were constructed based on anatomical and functional data for both timepoints for each patient. Clinical hemodynamic data were used to inform arterial wall properties (linearized stiffness and thickness), inflow and outflow boundary conditions. Computational models were used to yield quantitative metrics such as pressure, flow, pulse wave velocity, pulmonary resistance and capacitance, and arterial stiffness. Lumped-parameter Windkessel model were used to capture resistance and compliance of the distal vasculature, while lumped-parameter heart models were used to model the atria, ventricles, and valves [Fig 1]. Data-derived and model-derived metrics were compared between the timepoints.

Model calibration: Simulated mean flow rates, mean pressures, systolic, diastolic, and pulse pressures were all matched within 10% of clinical data in the main pulmonary artery (MPA) and ascending aorta through an iterative process. 

Results:

Significant changes in PAH metrics were observed between baseline and follow-up [Table 1].

Patient #1 (age 14, Female):  

Data-derived metrics: Pulmonary arterial systolic and pulse pressures, RV Stroke Work index (RVSWi), Pulmonary Arterial Compliance index (PACi), and Main Pulmonary Artery stiffness increased between time points.

Model-derived metrics: MPA-left and MPA-right pulmonary artery pulse wave velocities (MPA-LPA-PWV and MPA-RPA-PWV) and total pulmonary vasculature resistance (R_total) increased, while total compliance (C_total) decreased. 

Patient #2 (age 8, Female):

Data-derived metrics: MPA systolic and Pulse pressures, RVSWi, PACi, MPA-LPA and MPA-RPA PWVs decreased between timepoints.

Model-derived metrics: A slight increase in the MPA stiffness was observed. R_total decreased while the C_total increased.  

Discussion:

In Patient #1, an increase in both MPA systolic and pulse pressures increased RV workload, as demonstrated by the significant increase in RVSWi, which has detrimental effects on long-term RV function. The MPA stiffness [Fig 3], a strong indicator of PAH prognosis, increased by approximately 25%. The greater the stiffness, the larger the pulse pressures in the pulmonary arteries [Table 1].

Despite most hemodynamic metrics improving in Patient #2, a slight 0.5% increase in MPA stiffness was observed [Fig 3]. Changes in pulmonary arterial PWVs, a surrogate for arterial stiffness, were observed in both patients. Cardiac indexes remained unchanged regardless of changes in disease state, reflecting the system’s ability to adapt and maintain a homeostatic cardiac output at the expense of increasing pressure and cardiac work.

During this study, both patients were prescribed Ambrisentan, a vasoconstriction inhibitor [5]. Patient #1's presented a more complex disease, including both PAH and RV dysfunction, in addition to anemia, mild obstructive sleep apnea, and thrombocytopenia. The large age gap between patients could explain the differential outcomes in disease progression.

Conclusion:

The findings from this study provide valuable insights into the hemodynamic changes associated with progression of pulmonary arterial hypertension. By matching clinical hemodynamic data, model-derived metrics were used to assess disease progression in two PAH patients. Future work will focus on studying RV remodeling using high-fidelity finite element models, following the same methodology in our foundational work [3].

References:

[1] Simonneau G. et al., Eur Respir J, 53: 1801913, 2019.

[2] Humphrey J.D. et al., Circ Res, 118:379–381, 2016.

[3] Tossas-Betancout C. et al., Front. Physiol, 13, 2022.

[4] Arthurs C.D. et al., PLoS Comput Biol, 17, 2021.

[5] Casserly B. et al., DMP, 2: 265-280, 2008