Computational Analysis of Disease Progression in Pediatric Pulmonary Arterial Hypertension

TL;DR

This study presents a multi-scale computational modeling approach for pediatric PAH, integrating patient-specific MRI and catheter data to non-invasively monitor disease progression and potentially guide treatment.

Contribution

We developed a calibrated, patient-specific cardiovascular model for pediatric PAH that accurately reflects disease progression using longitudinal data and automated optimization.

Findings

Model metrics correlated with clinical severity indicators

Automated calibration reduced modeling time from weeks to days

Simulation captured hemodynamic changes over two years

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.