Computational model of haemodynamics during atrial fibrillation
Plappert, Felix LU
; Oomen, Pim J.A.
; Jones, Clara E.
; Charitakis, Emmanouil
; Karlsson, Lars O.
; Platonov, Pyotr G.
LU
; Wallman, Mikael
and Sandberg, Frida
LU
(2025)
In Journal of Physiology
- Abstract
Atrial fibrillation (AF) is associated with reduced cardiac output, which is correlated with increased symptomatic burden and declined quality of life. Predicting haemodynamic effects of AF remains challenging because of the complex interplay of multiple contributing mechanisms. Computational modelling offers a valuable tool for simulating haemodynamics. However, existing models are lacking the capabilities to both replicate beat-to-beat haemodynamic variations during AF at the same time as being well suited for fitting to clinical data. In the present study, we present a computational model comprising: (1) an electrical subsystem that generates unco-ordinated atrial and irregular ventricular activation times characteristic of AF and... (More)
Atrial fibrillation (AF) is associated with reduced cardiac output, which is correlated with increased symptomatic burden and declined quality of life. Predicting haemodynamic effects of AF remains challenging because of the complex interplay of multiple contributing mechanisms. Computational modelling offers a valuable tool for simulating haemodynamics. However, existing models are lacking the capabilities to both replicate beat-to-beat haemodynamic variations during AF at the same time as being well suited for fitting to clinical data. In the present study, we present a computational model comprising: (1) an electrical subsystem that generates unco-ordinated atrial and irregular ventricular activation times characteristic of AF and (2) a mechanical subsystem that simulates haemodynamics using a reduced order model. The model was fitted to replicate individual haemodynamic 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 at the same time as overestimating right ventricular systolic and mean left atrial pressures during AF. The presented approach of modelling atrial activity in AF as unco-ordinated 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 haemodynamics during AF. (Figure presented.). Key points: Atrial fibrillation (AF) is linked to the heart pumping less blood, a higher symptomatic burden and a lower quality of life. Although computational models can help us understand the blood circulation in patients with AF, no current models can both replicate beat-to-beat changes during AF and be fitted to individual patients. We developed a computational model that simulates beat-to-beat haemodynamic changes resulting from the unco-ordinated atrial and irregular electrical activation times characteristic of AF. The computational model was fitted to 17 patients and matched a large majority of arterial and intracardiac pressure measurements and ejection fraction measurements well below 10 mmHg and 10%, respectively. This computationally efficient model provides a platform for future investigations of patient-specific haemodynamics during AF.
(Less)
- author
- Plappert, Felix
LU
; Oomen, Pim J.A.
; Jones, Clara E.
; Charitakis, Emmanouil
; Karlsson, Lars O.
; Platonov, Pyotr G.
LU
; Wallman, Mikael
and Sandberg, Frida
LU
- organization
- publishing date
- 2025
- type
- Contribution to journal
- publication status
- epub
- subject
- keywords
- atrial fibrillation, atrioventricular node, computational modelling, haemodynamics, RR series characteristics, SMURF
- in
- Journal of Physiology
- publisher
- The Physiological Society
- external identifiers
-
- pmid:41420468
- scopus:105025364490
- ISSN
- 0022-3751
- DOI
- 10.1113/JP289469
- project
- Data Driven Tools for Atrial Fibrillation: Tracking of Risk Factors and Disease Progression
- Ph.D. project: Diagnostic Biomarkers in Atrial Fibrillation - Autonomic Nervous System Induced Modulation as a Sign of Disease Progression
- Diagnostic Biomarkers in Atrial Fibrillation - Autonomic Nervous System Response as a Sign of Disease Progression
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025 The Author(s). The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.
- id
- 02d39bc3-bca3-4b64-afd5-32e710c265bd
- date added to LUP
- 2026-01-08 08:49:03
- date last changed
- 2026-02-05 12:51:26
@article{02d39bc3-bca3-4b64-afd5-32e710c265bd,
abstract = {{<p>Atrial fibrillation (AF) is associated with reduced cardiac output, which is correlated with increased symptomatic burden and declined quality of life. Predicting haemodynamic effects of AF remains challenging because of the complex interplay of multiple contributing mechanisms. Computational modelling offers a valuable tool for simulating haemodynamics. However, existing models are lacking the capabilities to both replicate beat-to-beat haemodynamic variations during AF at the same time as being well suited for fitting to clinical data. In the present study, we present a computational model comprising: (1) an electrical subsystem that generates unco-ordinated atrial and irregular ventricular activation times characteristic of AF and (2) a mechanical subsystem that simulates haemodynamics using a reduced order model. The model was fitted to replicate individual haemodynamic 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 at the same time as overestimating right ventricular systolic and mean left atrial pressures during AF. The presented approach of modelling atrial activity in AF as unco-ordinated 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 haemodynamics during AF. (Figure presented.). Key points: Atrial fibrillation (AF) is linked to the heart pumping less blood, a higher symptomatic burden and a lower quality of life. Although computational models can help us understand the blood circulation in patients with AF, no current models can both replicate beat-to-beat changes during AF and be fitted to individual patients. We developed a computational model that simulates beat-to-beat haemodynamic changes resulting from the unco-ordinated atrial and irregular electrical activation times characteristic of AF. The computational model was fitted to 17 patients and matched a large majority of arterial and intracardiac pressure measurements and ejection fraction measurements well below 10 mmHg and 10%, respectively. This computationally efficient model provides a platform for future investigations of patient-specific haemodynamics during AF.</p>}},
author = {{Plappert, Felix and Oomen, Pim J.A. and Jones, Clara E. and Charitakis, Emmanouil and Karlsson, Lars O. and Platonov, Pyotr G. and Wallman, Mikael and Sandberg, Frida}},
issn = {{0022-3751}},
keywords = {{atrial fibrillation; atrioventricular node; computational modelling; haemodynamics; RR series characteristics; SMURF}},
language = {{eng}},
publisher = {{The Physiological Society}},
series = {{Journal of Physiology}},
title = {{Computational model of haemodynamics during atrial fibrillation}},
url = {{http://dx.doi.org/10.1113/JP289469}},
doi = {{10.1113/JP289469}},
year = {{2025}},
}