Cardiocirculatory computational models for the study of hypertension

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Authors: Simone Celora, Andrea Tonini, Francesco Regazzoni, Luca Dede’, Gianfranco Parati, Alfio Maria Quarteroni
Cited by: 0
References: 74

Abstract

In this work, we develop patient-specific cardiocirculatory models with the aim of building digital twins for hypertension. In particular, in our pathophysiology-based framework, we consider both 0D cardiocirculatory models and a 3D-0D electromechanical model. The 0D model, which consists of an RLC circuit, is studied in two variants, with and without capillaries. The 3D–0D model consists of a three-dimensional electromechanical model of the left ventricle, coupled with a 0D model for the external blood circulation: this representation enables the assessment of additional quantities related to ventricular deformation and stress, and offers a more detailed representation compared to a fully 0D model. Sensitivity analysis is performed on the 0D model, with both a mono- and a multi-parametric approach, in order to identify the parameters that most influence the model outputs and guide the calibration process. We studied three different scenarios, corresponding to systemic, pulmonary and renovascular hypertension, each in three nuances of severity. To maintain a fair comparison among the models, a parameter calibration strategy is developed; the outputs of the 0D model with capillaries are utilized to indirectly calibrate the 3D–0D model parameters. The results demonstrate that the 3D–0D model yields a realistic representation of cardiocirculatory dynamics in the presence of hypertension; this model represents a powerful step toward digital twins for real-time hypertension control, providing refined and clinically meaningful insights beyond those achievable with 0D models alone.

Fields of Study & Subjects

Cardiac electrophysiology and arrhythmias Cardiovascular Health and Disease Prevention Nitric Oxide and Endothelin Effects

Publication details

Journal / source
Journal of Computational Physics
Publisher
Elsevier BV
Volume
567
Pages
115324–115324
Publication date
22 August 2026
Language
EN