Quantifying the Hemodynamic Effects of Ventricular Fibrillation using a Verified Computational Model

TL;DR

This work addresses the challenge of connecting cellular electrophysiology to whole-organ hemodynamics in Ventricular Fibrillation by developing a verified closed-loop 0D cardiovascular framework. It quantifies the immediate hemodynamic collapse under a prescribed VF state, reporting a 62.4% drop in cardiac output driven by severe diastolic filling impairment and reduced contractility. Beyond the demonstration, the paper articulates a concrete multiscale roadmap to couple the 0D core with electrophysiology and autonomic regulation, enabling emergent VF dynamics and reflex responses. An interactive simulator based on the verified 0D model is provided to support education, rapid hypothesis testing, and future integration of multiscale components, with source code publicly accessible. Together, these contributions establish a mechanistic baseline and a software foundation for next-generation, potentially patient-specific cardiovascular simulations of VF.

Abstract

Ventricular Fibrillation (VF) is a malignant cardiac arrhythmia and the leading cause of sudden cardiac death, characterized by disorganized, high-frequency ventricular activity that results in the rapid loss of coordinated pump function and circulatory collapse. While the clinical manifestations of VF are well established, the multiscale mechanisms linking cellular electrophysiology to whole-organ mechanical failure remain challenging to study experimentally. Computational modeling therefore provides a critical platform for mechanistic investigation. This work presents a hierarchical computational study of VF beginning with the implementation and verification of a closed-loop, lumped-parameter (0D) hemodynamic model of the cardiovascular system. The verified model is used to quantify the global circulatory consequences of a prescribed VF state, demonstrating a 62.4% reduction in cardiac output and highlighting the dominant role of impaired ventricular filling and contractile failure. Recognizing the limitations of prescribing arrhythmia dynamics, we then propose a pathway toward an integrated, multiscale framework coupling the 0D hemodynamic core with models of cardiac electrophysiology and autonomic regulation to enable simulation of emergent arrhythmogenic behavior and reflex responses. Finally, we introduce an interactive simulator derived from the verified 0D model, designed to support education, hypothesis testing, and future integration of multiscale components. This work establishes a mechanistic baseline and software foundation for next-generation computational studies of VF and cardiovascular control.

Figures (4)

• Figure 1: Simulated results for the healthy baseline condition (HR=75 bpm, 100% Contractility). Top panel shows the simulated Aortic Pressure. Bottom panel shows the corresponding Right Venous-Atrial Pressure.
• Figure 2: Simulated results for the prescribed Ventricular Fibrillation condition (HR=350 bpm, 50% Contractility). Top panel shows the simulated Aortic Pressure. Bottom panel shows the corresponding Right Venous-Atrial Pressure.
• Figure 3: Quantitative comparison of Cardiac Output (CO) between the healthy baseline simulation (5.88 L/min) and the prescribed Ventricular Fibrillation simulation (2.21 L/min), demonstrating a 62.4% reduction.
• Figure 4: The graphical user interface of the interactive cardiovascular simulator, developed using Python and Streamlit. It allows preset selection, manual parameter adjustment via sliders (sidebar), visualization of key metrics, hemodynamic waveforms, and PV loops (main area), and includes data export functionality. The interface utilizes scipy.integrate.solve_ivp for ODE solving and matplotlib for plotting.