A computational model to study hemodynamics during atrial fibrillation

Abstract

Atrial fibrillation (AF) is associated with reduced cardiac output, which is correlated with increased symptomatic burden and declined quality of life. Predicting hemodynamic effects of AF remains challenging due to the complex interplay of multiple contributing mechanisms. Computational modeling offers a valuable tool for simulating hemodynamics. However, existing models are lacking the capabilities to both replicate beat-to-beat hemodynamic variations during AF while being well suited for fitting to clinical data. In this study, we present a computational model comprising: 1) an electrical subsystem that generates uncoordinated atrial and irregular ventricular activation times characteristic of AF, and 2) a mechanical subsystem that simulates hemodynamics using a reduced order model. The model was fitted to replicate individual hemodynamic measurements from 17 patients in the SMURF study during both normal sinus rhythm (NSR) and AF. The fitted model matched a large majority (75%) of blood pressure and intracardiac pressure measurements in both NSR and AF with absolute simulation errors well below 10 mmHg. Furthermore, a large majority of left atrial and left ventricular ejection fraction measurements during NSR were matched with absolute simulation errors well below 10%. The model consistently underestimated right ventricular diastolic pressure during NSR while overestimating right ventricular systolic and mean left atrial pressures during AF. The presented approach of modeling atrial activity in AF as uncoordinated atrial contractions, rather than no atrial contraction, achieved lower overall absolute simulation errors when fitting to individual patients. This computationally efficient model provides a platform for future investigations of patient-specific hemodynamics during AF.