Data-driven computational models of ventricular-arterial hemodynamics in pediatric pulmonary arterial hypertension

Christopher Tossas-Betancourt, Nathan Y. Li, Sheikh M. Shavik, Katherine Afton, Brian Beckman, Wendy Whiteside, Mary K. Olive, Heang M. Lim, Jimmy C. Lu, Christina M. Phelps, Robert J. Gajarski, Simon Lee, David A. Nordsletten, Ronald G. Grifka, Adam L. Dorfman, Seungik Baek, Lik Chuan Lee, C. Alberto Figueroa*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Pulmonary arterial hypertension (PAH) is a complex disease involving increased resistance in the pulmonary arteries and subsequent right ventricular (RV) remodeling. Ventricular-arterial interactions are fundamental to PAH pathophysiology but are rarely captured in computational models. It is important to identify metrics that capture and quantify these interactions to inform our understanding of this disease as well as potentially facilitate patient stratification. Towards this end, we developed and calibrated two multi-scale high-resolution closed-loop computational models using open-source software: a high-resolution arterial model implemented using CRIMSON, and a high-resolution ventricular model implemented using FEniCS. Models were constructed with clinical data including non-invasive imaging and invasive hemodynamic measurements from a cohort of pediatric PAH patients. A contribution of this work is the discussion of inconsistencies in anatomical and hemodynamic data routinely acquired in PAH patients. We proposed and implemented strategies to mitigate these inconsistencies, and subsequently use this data to inform and calibrate computational models of the ventricles and large arteries. Computational models based on adjusted clinical data were calibrated until the simulated results for the high-resolution arterial models matched within 10% of adjusted data consisting of pressure and flow, whereas the high-resolution ventricular models were calibrated until simulation results matched adjusted data of volume and pressure waveforms within 10%. A statistical analysis was performed to correlate numerous data-derived and model-derived metrics with clinically assessed disease severity. Several model-derived metrics were strongly correlated with clinically assessed disease severity, suggesting that computational models may aid in assessing PAH severity.

Original languageEnglish
Article number958734
JournalFrontiers in Physiology
Publication statusPublished - 7 Sept 2022


  • arterial hemodynamics
  • biomechanics
  • computational modeling
  • patient stratification
  • pulmonary arterial hypertension
  • ventricular mechanics
  • ventricular-arterial coupling

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