Modeling the Impact of Heart Rate Variability on Blood Pressure in Simulated Atrial Fibrillation Using the BioGears Physiology Engine

Abstract

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, is characterized by chaotic atrial activation and irregular ventricular response. While its electrophysiologic mechanisms are well studied, the systemic consequences of rhythm irregularity, particularly its impact on blood pressure variability (BPV), remain poorly understood. This study uses the BioGears Physiology Engine, an open-source whole-body simulator, to investigate how RR interval irregularity during AF, quantified by the root mean square of successive differences (RMSSD), influences beat-to-beat hemodynamic stability. Synthetic RR interval sequences were generated to span a physiological AF RMSSD range (80–250 ms) and imposed onto the BioGears cardiovascular model across ten virtual patients. For each simulation, systolic and diastolic BPV were quantified using standard deviation metrics, and linear regression was used to evaluate the relationship between RMSSD and BPV. Results demonstrated a strong positive correlation between RMSSD and both systolic and diastolic BPV across all patients. Simulated BPV values closely matched clinical observations, and a novel phenotype framework was proposed to stratify AF patients by hemodynamic tolerance based on RMSSD-BPV coupling. This work provides a mechanistic link between rhythm irregularity and systemic blood pressure instability in AF and introduces a modeling framework for evaluating arrhythmia tolerance in silico.