Key result
Computational models of ventricular-arterial hemodynamics strongly correlate with clinical disease severity in pediatric PAH.
Why the study?
Ventricular-arterial interactions are fundamental to PAH pathophysiology but are rarely captured in computational models, and metrics quantifying these interactions are needed to inform disease understanding and facilitate patient stratification.
Observational (n=8)
No
Patient-specific computational models of ventricular-arterial hemodynamics can be successfully calibrated using routine clinical data and may aid in assessing disease severity in pediatric pulmonary arterial hypertension.
May aid non-invasive severity assessment in pediatric PAH; leaves open prospective validation before clinical use.
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.
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Tossas-Betancourt et al. (2022) conducted an observational in Pediatric pulmonary arterial hypertension (n=8). Patient-specific computational modeling was evaluated. Data-driven computational models of ventricular-arterial hemodynamics generated metrics that strongly correlated with clinically assessed disease severity in pediatric pulmonary arterial hypertension.
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