Computational Analysis of Disease Progression in Pediatric Pulmonary Arterial Hypertension

Area of Science:

• Cardiovascular Medicine
• Biomedical Engineering
• Computational Biology

Background:

• Pulmonary arterial hypertension (PAH) is a severe condition leading to right ventricular failure.
• Pediatric PAH is understudied, lacking targeted diagnostics and therapies.
• Longitudinal data for pediatric PAH patients is scarce.

Purpose of the Study:

• To develop and calibrate patient-specific cardiovascular models for pediatric PAH.
• To utilize multi-scale computational modeling for simulating hemodynamics.
• To assess the potential of computational modeling for monitoring disease progression.

Main Methods:

• Developed patient-specific, multi-scale cardiovascular models using MRI and catheterization data.
• Coupled 3D fluid-structure interaction (FSI) models with 0D lumped-parameter models.
• Employed an automated Python optimizer for efficient model calibration.
• Simulated patient hemodynamics and derived key cardiovascular metrics.

Main Results:

• Model calibration time reduced from weeks to days using automated optimization.
• Model-derived metrics (arterial stiffness, pulse wave velocity, resistance, compliance) correlated with clinical disease severity.
• Computational models successfully captured patient-specific hemodynamic adaptations over two years.
• Non-invasive hemodynamic assessment aligned with clinical indicators.

Conclusions:

• Patient-specific computational modeling is a viable tool for monitoring pediatric PAH.
• This approach can non-invasively assess hemodynamic changes and disease progression.
• Findings support the use of computational modeling to inform pediatric PAH treatment strategies.