Computational Analysis of Disease Progression in Pediatric Pulmonary Arterial Hypertension

TL;DR

This study developed and calibrated multi-scale, patient-specific cardiovascular models for four pediatric PAH patients using longitudinal MRI and catheterization data collected approximately two years apart, demonstrating that computational modeling can non-invasively capture patient-specific hemodynamic adaptation over time.

Abstract

Pulmonary arterial hypertension (PAH) is a progressive cardiopulmonary disease that leads to increased pulmonary pressures, vascular remodeling, and eventual right ventricular (RV) failure. Pediatric PAH remains understudied due to limited data and the lack of targeted diagnostic and therapeutic strategies. In this study, we developed and calibrated multi-scale, patient-specific cardiovascular models for four pediatric PAH patients using longitudinal MRI and catheterization data collected approximately two years apart. Using the CRIMSON simulation framework, we coupled three-dimensional fluid-structure interaction (FSI) models of the pulmonary arteries with zero-dimensional (0D) lumped-parameter heart and Windkessel models to simulate patient hemodynamics. An automated Python-based optimizer was developed to calibrate boundary conditions by minimizing discrepancies between simulated and clinical metrics, reducing calibration time from weeks to days. Model-derived metrics such as arterial stiffness, pulse wave velocity, resistance, and compliance were found to align with clinical indicators of disease severity and progression. Our findings demonstrate that computational modeling can non-invasively capture patient-specific hemodynamic adaptation over time, offering a promising tool for monitoring pediatric PAH and informing future treatment strategies.

Figures (7)

• Figure 1: Schematic of the vascular stiffening feedback loop. Increased blood pressure and structural stiffness reinforce each other through local wall mechanics and global hemodynamics, influenced by aging, disease, and vascular remodeling. Equations relate wall stress and pulse wave velocity (PWV) to arterial geometry and material properties.
• Figure 2: Comparison between a normal heart and a heart affected by pulmonary hypertension. Note the constriction of the pulmonary arteries and right ventricular enlargement in the diseased state.
• Figure 3: Schematic of the multi-scale cardiovascular simulation framework. The 3D FSI model of the heart and vasculature is coupled with lumped-parameter Windkessel models (W) at outlets and a 0D heart model (H) at the inlet to capture global hemodynamics.
• Figure 4: Boundary condition calibration workflow across three stages. Stage 1 uses an open-loop arterial model with imposed aortic and MPA flows. Stage 2 integrates a 0D heart model (H) while maintaining open-loop architecture. Stage 3 forms a fully closed-loop system coupling the arterial model with the heart model and Windkessel outlets.
• Figure 5: Model calibration workflow for tuning lumped-parameter Windkessel components. Simulations are iteratively updated until simulated pressure and flow waveforms match clinical data within a 10% margin for both pulmonary and systemic circulations.