Comprehensive physiology · 2026
Patient-Specific Lumped-Parameter Model for Quantifying Vessel-Specific Remodeling and Predicting Right Ventricular Function in Pulmonary Hypertension.
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Lechuga Christopher G, Kachabi Amirreza, Colebank Mitchel J, Korcarz Claudia E, Raza Farhan, Chesler Naomi C
Аннотация
PURPOSE: Pulmonary hypertension (PH) is a heterogeneous disease with patient-specific variability and vessel-specific remodeling, which eventually lead to right ventricular (RV) failure. The gold standard for RV assessment-pressure-volume (PV) loop acquisition-is invasive and limited to specialized settings. This study aims to develop a patient-specific lumped-parameter model that quantifies vessel-specific remodeling and simulates RV PV loops across PH phenotypes using routine clinical data.
METHODS: A lumped-parameter model was calibrated using right heart catheterization and echocardiography data. Model agreement was assessed by R2 values for pressure and flow goodness-of-fit, and model-derived hemodynamic metrics were compared with clinical values. A dimensionality reduction approach was applied to investigate how well different PH phenotypes could be separated.
RESULTS: Across the cohort, the lumped-parameter model showed good agreement with clinical data. Model-derived vessel-specific (pulmonary arterial, capillary, venular) parameters highlighted physiological distinctions among phenotypes. Predicted RV PV loops revealed phenotype-specific differences in right ventricular volumes, pressures, and stroke work. The linear discriminant analysis (LDA) demonstrated qualitative separability, indicating that model-derived, nonmeasurable features offer additional discriminatory information.
CONCLUSION: Our results demonstrate that lumped-parameter models can be calibrated to clinical data to quantify vessel-specific remodeling and simulate RV pressure-volume dynamics to provide useful information for distinguishing among different PH phenotypes. This underscores the potential of computational models as noninvasive, clinically feasible tools for assessing in-depth pulmonary vascular and RV function in PH.
METHODS: A lumped-parameter model was calibrated using right heart catheterization and echocardiography data. Model agreement was assessed by R2 values for pressure and flow goodness-of-fit, and model-derived hemodynamic metrics were compared with clinical values. A dimensionality reduction approach was applied to investigate how well different PH phenotypes could be separated.
RESULTS: Across the cohort, the lumped-parameter model showed good agreement with clinical data. Model-derived vessel-specific (pulmonary arterial, capillary, venular) parameters highlighted physiological distinctions among phenotypes. Predicted RV PV loops revealed phenotype-specific differences in right ventricular volumes, pressures, and stroke work. The linear discriminant analysis (LDA) demonstrated qualitative separability, indicating that model-derived, nonmeasurable features offer additional discriminatory information.
CONCLUSION: Our results demonstrate that lumped-parameter models can be calibrated to clinical data to quantify vessel-specific remodeling and simulate RV pressure-volume dynamics to provide useful information for distinguishing among different PH phenotypes. This underscores the potential of computational models as noninvasive, clinically feasible tools for assessing in-depth pulmonary vascular and RV function in PH.