Cardiocirculatory Computational Models for the Study of Hypertension

TL;DR

This paper develops patient-specific cardiocirculatory models, including 0D and 3D-0D models, to create digital twins for hypertension, enabling detailed analysis and potential real-time clinical applications.

Contribution

It introduces a combined 3D-0D electromechanical model for hypertension, integrating detailed ventricular mechanics with systemic circulation, and demonstrates its accuracy over traditional 0D models.

Findings

3D-0D model accurately simulates hypertension dynamics

Sensitivity analysis identifies key parameters influencing outcomes

Model calibration improves predictive accuracy for different hypertension types

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 enhance the 3D-0D model. The results demonstrate that the 3D-0D model yields an accurate 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.