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Introduction
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia characterized by irregular atrial contractions that compromise ventricular function and cardiac output. In this study we investigated the impact of AF on myocardial oxygen utilization, hypothesizing that myocardial oxygen extraction is more pronounced in AF due to less efficient ventricular function and reduced myocardial blood flow.
MethodsWe conducted a prospective, observational study involving 45 patients undergoing AF catheter ablation at the University of Washington Medical Center between 2022 and 2024, categorizing them based on their presenting rhythm, i.e., sinus rhythm (SR; N = 27) or atrial fibrillation (AF; N = 18). During AF procedures blood samples for oxygen analyses were collected from the pulmonary artery, coronary sinus (CS), and left atrium. Cardiac magnetic resonance imaging assessed CS blood flow and left ventricular mass.
ResultsPatients in AF exhibited significantly higher myocardial oxygen extraction than those in SR (10.8 ± 1.4 vs 8.9 ± 2.0 mL O2/dL blood, P = .001). Additionally, AF patients had lower cardiac power (0.74 ± 0.14 vs 1.07 ± 0.32 W, P = .004), reduced CS flow (56.2 ± 34.0 vs 65.0 ± 19.2 mL/s, P = .42), and increased heart rate (80.6 ± 17.4 vs 64.0 ± 10.7 bpm, P = .002). In the AF group, symptomatic patients had significantly higher myocardial oxygen extraction for dyspnea, exercise intolerance, and palpitations ( P = .048, P = .018, and P = .028, respectively), with a trend observed for fatigue ( P = .07). No significant differences were found between symptomatic and asymptomatic patients in the sinus rhythm (SR) group. Multivariable regression analyses demonstrated that AF and AF-related symptoms were strongly associated with increased myocardial oxygen extraction.
ConclusionAF significantly affects myocardial oxygen utilization by reducing both myocardial blood flow and cardiac power, thereby requiring increased oxygen extraction. This 2-hit mechanism—reduced supply and increased demand—highlights the symptomatic challenges in managing AF and emphasizes the need for therapeutic strategies to optimize cardiac physiology in these patients.
Atrial fibrillation (AF) results in loss of atrial systole which significantly reduces atrial contribution to ventricular filling. In addition, AF leads to irregular ventricular activation and contraction, resulting in reductions in cardiac output and power. 1-4 The rapid ventricular rates, often associated with AF, further compound this problem by shortening diastolic filling time, further reducing ventricular preload. Diastolic filling patterns are more difficult to assess in AF; however, AF patients in sinus rhythm (SR) demonstrate impaired relaxation and higher ventricular filling pressures, which improve after maintenance of SR with catheter ablation. 5-7 Moreover, myocardial blood flow is significantly impaired in AF patients with no known coronary artery disease. 2 This impairment is likely multifactorial, influenced by endothelial dysfunction, increased left ventricular filling pressures, microvascular dysfunction, and altered coronary perfusion dynamics due to irregular ventricular contractions. 8-10 The convergence of these physiological effects on cardiac function contributes to AF symptoms, such as rest and exertional dyspnea, and may significantly impair the overall quality of life (QoL) of AF patients. 11
We hypothesized that, for patients in AF, the combination of reduced cardiac power, higher heart rates, and decreased myocardial blood flow in diastole necessitates higher oxygen extraction from the blood perfusing the myocardium.
Methods Study design and populationThis prospective, observational study included 45 patients who underwent catheter ablation for AF at the University of Washington Medical Center (UWMC, Seattle, WA) between 2022 and 2024. We excluded patients with previous catheter ablation, left ventricular ejection fraction <45% on any prior study, and/or active or previous malignancy. Our exclusion criteria were established to address the uncertainty surrounding AF patients presenting in SR, as it would be unclear whether their rhythm was due to the effects of ablation or spontaneous conversion. Additionally, we excluded patients with heart failure with reduced ejection fraction (HFrEF) to ensure the study population aligned with our targeted objectives. Patients were categorized into 2 groups, SR or AF, based on their presenting rhythm at the time of ablation. All patients were in a fasting state. The study was approved by the University of Washington (UW) Institutional Review Board (IRB), and all patients provided written informed consent.
Sample collectionCatheter ablation was performed under general anesthesia. Access was obtained via the femoral and right internal jugular veins. Following systemic heparinization (target activated clotting time of 350-450 seconds) transseptal puncture was performed under fluoroscopic and intracardiac echocardiographic guidance. Blood samples were drawn from the left atrium after transseptal access (representative of the arterial sample). A CSL catheter (Abbott Laboratories, Abbott Park, IL) with an internal lumen was used for blood sampling from the coronary sinus (cardiac venous sample). A right ventricular outflow tract sample was acquired for cardiac output calculation. Oxygen saturation was determined for each sample using an Avoximeter 1000E (Accriva Diagnostics, Inc., San Diego, CA). Each blood sample was then centrifuged at 4°C for plasma isolation, and plasma was subsequently frozen at −80°C for later use.
Cardiac Magnetic Resonance (CMR) image acquisition and analysisImaging was performed on a 1.5-Tesla CMR scanner equipped with 32-channel cardiac coils (Achieva, Philips Healthcare, Best, The Netherlands). Using an ECG-gated, breath-hold-balanced steady-state free precession sequence, vertical long-axis, horizontal long-axis, and short-axis cine images of the left ventricle (LV) were captured. To locate the coronary sinus, axial plane cine CMR was performed through the atrioventricular groove. The imaging plane for blood flow measurement was positioned perpendicular to the coronary sinus, 1.5 cm from its ostium. During breath-holding, phase contrast (PC) cine CMR of the coronary sinus was conducted using a vector ECG-triggered gradient echo sequence. Commercially available software (Circle Cardiovascular Imaging, Calgary, Alberta, Canada) was used to analyze cine, PC, and 4-D flow images. For CS blood flow analysis, the contours of the CS were semi-automatically traced on each frame for all PC images (Supplementary Figure), and the region of interest for each cardiac phase was drawn separately for phase-offset correction. Images in the 2- and 4-chamber views were segmented for the derivation of myocardial mass and left atrial volumes. Experienced cardiac MRI readers analyzed the underlying rhythm during CS net flow image acquisition by interpreting the presence or absence of atrial contractions.
Cardiac output and cardiac power calculationArterial and pulmonary artery blood samples were obtained as above. VO2 was calculated from closed circuit mechanical ventilation. The hemoglobin value was taken from a preprocedural laboratory assessment. Cardiac output was calculated using these parameters via the Fick method. The above calculated cardiac output and the intraprocedural mean arterial pressure (MAP) at the time of blood sampling were used to calculate cardiac power.
Patient symptoms and AF limitationsThe modified European Heart Rhythm Association (mEHRA) symptom classification was used to assess symptoms and quality of life in our cohort. The score was obtained following the patient's last clinic visit prior to their ablation procedure. The mEHRA classification consists of 5 classes ranked on AF symptom severity and impact on daily life activity with a score of (1) being the absence of symptoms, (2A) symptoms present but daily life activities not affected, (2B) symptoms present and daily life activities affected, (3) severe, (4) disabling symptoms. 12
Statistical analysesContinuous variables were reported as mean ± SD, or as median and interquartile ranges, depending on an assessment of normality by the Kolmogorov–Smirnov test. 13 Categorical variables were expressed as numbers and percentages. For continuous variables, group differences were assessed using the 2-sided t-test or Mann–Whitney U-test, as appropriate. For categorical variables, the chi-square or Fisher's exact test was used, when appropriate. Univariable and multivariable linear regression models for association with O 2 extraction were fitted with predefined variables. Statistical significance was defined as a P-value < .05. Variables identified as statistically significant and clinically relevant were included in the multivariable regression analysis for Model 1. In Model 2, CS net flow and cardiac power were intentionally included in the regression analysis as they represent key components of the study hypothesis. It is important to emphasize that this analysis is hypothesis-generating due to the limitations imposed by the sample size.
Results Cohort characteristicsA total of 45 patients were included in the analysis: 27 (60%) patients were in SR and 18 (40%) in AF at the time of the ablation. Of patients in the SR group, 25/27 (93%) had paroxysmal AF. Patients in SR tended to be older than those in AF (69±9 years vs 63 ± 13; P = .1). There were 14 (52%) females in the SR group and 6 (33%) in the AF group ( P = .2). As for QoL assessment, 59% of SR group patients had an mEHRA score >1, while only 78% of patients in the AF group patients had a score > 1 ( P = .19). Beta-blocker use was 82% in the SR compared to 56% in the AF group ( P = .06). Antiarrhythmic drug use was 44% in the SR group vs 11% in the AF group ( P = .018). Table 1 summarizes the baseline characteristics of the 2 groups.
Cardiac parameters, coronary sinus flow, and oxygen extractionAll cardiac MRI scan were acquired on average 5 ± 4 months prior to blood sampling. Heart rate was slower in patients in SR (64.0 ± 10.7 bpm) than in those in AF (80.6 ± 17.4 bpm) ( P = .002). Cardiac output was similar in patients in SR compared to patients in AF (4.3 ± 1.5 L/min vs 4.1 ± 0.8 L/min, P = .65. MAP was 72.5 (65-84) mmHg for patients in SR vs 69.5 (63-74) mmHg for patients in AF ( P = .46). Cardiac power was significantly higher in patients in SR compared to AF (1.07 ± 0.32 W vs 0.74 ± 0.14 W; P = .004).
For CS oxygen saturation, patients in SR had a higher saturation 50.4 ± 11.1% compared to 44.0 ± 6.3% in AF ( P = .032). There was also a difference in the hemoglobin concentration in patients in SR vs AF (14.1 ± 1.3 g/L vs 15±1.3 g/L, P = .024). Myocardial oxygen extraction, defined as the difference in the volume of oxygen per dL of blood between the arterial sample (left atrial blood) and the venous sample (CS blood), was significantly increased in patients in AF when compared to patients in SR (10.8 ± 1.4 vs 8.9 ± 2.0 mL O 2/dL blood, P = .001) ( Figure 1 ).
Myocardial perfusion adjusted for ventricular mass, calculated as the minute volume of CS blood flow/100 g of myocardial mass, was lower for patients in AF (56.2 ± 34.0 mL/s in AF vs 65.0 ± 19.2 mL/s in SR ( P = .42). In terms of the underlying rhythm during the scan, 3(11%) patients from the SR group were in AF during MRI acquisition vs 2(11%) patient from the AF group were in SR.
As a marker of oxygen utilization efficiency, we next calculated the rate of oxygen extraction per unit of generated cardiac power, which was 5.6 ± 2.2 mL/W·min in SR vs 9.6 ± 6.5 mL/W·min in AF ( P = .1). Table 2 summarizes the results on selected cardiac parameters, coronary sinus flow, and oxygen extraction presented above.
Atrial fibrillation related symptomsPatients in different rhythms were stratified based on their reported symptoms, with a focus on AF related symptoms, including exercise intolerance, dyspnea, fatigue, and palpitations. In the AF group, a higher proportion of patients reported symptoms across all categories: exercise intolerance (72%), fatigue (67%), and palpitations (61%), except for dyspnea (44%). Conversely, a greater proportion of patients in the sinus rhythm (SR) group reported no symptoms in all categories.
Among patients in the AF group, those reporting dyspnea, exercise intolerance, and palpitations demonstrated significantly higher myocardial oxygen extraction compared to asymptomatic patients (11.55 ± 1.47 vs. 10.21 ± 1.18 mL/dL, 11.31 ± 1.19 vs 9.48 ± 1.26 mL/dL, and 11.39 ± 1.32 vs 9.89 ± 1.21 mL/dL, respectively; P = .048, P = .018, and P = .028). For fatigue, a trend toward higher oxygen extraction was observed in symptomatic patients compared to asymptomatic ones (11.23 ± 1.08 vs 9.96 ± 1.81 mL/dL), but this difference did not reach statistical significance ( P = .07).
In contrast, no significant differences in oxygen extraction were observed between symptomatic and asymptomatic patients in the SR group across any symptom category. The findings are summarized in ( Figure 2 ).
Linear regression analysesUnivariable and multivariable linear regression were used to analyze associations with myocardial oxygen extraction ( Table 3 ). Using univariable regression, AF and mEHRA score were significantly associated with myocardial oxygen extraction (β = 1.87 mL/dL of blood, P = .001 and β = 1.59mL/dL of blood, P = .005). Using multivariable regression, AF and mEHRA score remained significantly associated with oxygen extraction, after adjusting for heart rate, diastolic blood pressure, and LV mass index (Model 1). After adjusting for CS blood flow, cardiac power, and BNP the association between myocardial oxygen extraction and AF remained significant (β= 3.93 mL/dL of blood; P = .019) while mEHRA score was not (Model 2).
DiscussionOur results demonstrate that myocardial oxygen extraction, the difference in oxygen content between arterial blood and coronary sinus blood, is strongly associated with the underlying rhythm. In this cohort of patients undergoing AF ablation, patients in AF demonstrated a significantly higher myocardial oxygen extraction than patients in SR. Moreover, patients in an AF rhythm with self-reported symptoms such exercise intolerance, dyspnea, and palpitations showed statistically significant increase in oxygen extraction across the myocardium when compared patients in AF with no reported symptoms. There was also a noticed difference in oxygen extraction between patient with reported symptoms and no reported symptoms in the SR group; however, it was not statistically significant.
Although the ventricles account for the bulk of overall myocardial oxygen extraction, studies in swine models indicate that the atria can display a higher relative metabolic demand—particularly during AF—when adjusted for their smaller size. 14 This elevated atrial oxygen consumption reflects AF-associated metabolic remodeling, including shifts in substrate utilization, mitochondrial function, and oxidative stress, 15 underscoring the unique metabolic adaptations that contribute to AF pathophysiology.
To that end, additional variables were assessed to understand their potential relationships with myocardial oxygen extraction. These analyses demonstrated that, compared to patients in SR, cardiac power was significantly decreased and HR significantly increased in patients with AF. Cardiac output is reduced in AF due to the loss of coordinated atrial contraction and irregular ventricular rhythm. 16 In addition, the loss of atrial systole and shorter diastole due to more rapid ventricular rate in AF reduces preload and subsequent contractility through a Frank-Starling mechanism. 17 This diminishes stroke volume and contributes to a decrease in cardiac power, despite a higher heart rate, while the MAP remained relatively unchanged between SR and AF groups. The elevated heart rate observed in AF, and accounted for in the multivariable regression analysis, heightens oxygen demand, further exacerbating the imbalance in oxygen supply. Our regression analysis did not demonstrate a significant association between CS net flow or cardiac power and oxygen extraction, potentially due to confounding between these variables. However, given the sample size, the analysis may be underpowered to detect such an association. A larger cohort is needed for a more robust assessment, and thus, the findings from the multivariable regression should be interpreted with caution.
Moreover, in the AF group, 39% of patients were in persistent atrial fibrillation, compared to 7% of patients in the sinus rhythm group. Persistent atrial fibrillation is often associated with more advanced structural and functional remodeling of the myocardium, including fibrosis, impaired contractility, and altered metabolic profiles. 18-20 These factors likely contribute to increased oxygen extraction, reflecting the greater metabolic demand and reduced efficiency of a less healthy myocardium.
Our novel approach to quantifying coronary sinus flow through cardiac MRI, as a close surrogate for myocardial blood flow, showed that patients in AF had lower coronary sinus flow and thus reduced myocardial perfusion rates. The use of cardiac MRI to quantify coronary sinus flow has been previously validated in patients with coronary artery disease. 21 , 22 These findings suggest that coronary blood flow is decreased, forcing the myocardium to extract more oxygen from the limited supply, and therefore leading to a higher arteriovenous oxygen difference. 23-26 To note that the analysis of CS flow in AF patients is highly dependent on the underlying rhythm during MRI acquisition. AF leads to significant hemodynamic consequences on myocardial blood flow, resulting in reduced coronary perfusion. This reduction is further exacerbated by elevated ventricular rates, which compromise diastolic filling time and coronary blood flow. 27 , 28 Additionally, the higher hemoglobin levels in AF may reflect an adaptive response to this imbalance. 29
In AF patients, increased myocardial oxygen extraction, especially in those with exercise intolerance and dyspnea, may indicate a supply-demand oxygen mismatch, and the greatest difference in oxygen extraction is noted in patients with severe symptoms. 30 This suggests that in AF, particularly in those with exercise intolerance, impaired myocardial blood flow drives increased oxygen extraction as a compensatory mechanism. 31 , 32 Taken together, our findings suggest that the observed increase in myocardial oxygen extraction in AF likely represents a “2-hit” scenario, with both an increased myocardial oxygen demand and a reduced myocardial blood flow with a resulting net increase in oxygen consumption per unit power generated.
ConclusionOur approach demonstrated the strong relationship of the underlying rhythm in AF patients with myocardial oxygen extraction. Our hypothesis-generating findings suggest that a complex interplay between reduced myocardial blood flow and diminished cardiac power, combined with decreased cardiac efficiency, result in increased oxygen extraction. Additionally, we were able to link myocardial physiology in AF to clinical symptoms, showing that patients with increased oxygen extraction experienced more AF-related symptoms, including exercise intolerance, dyspnea, palpitations, and more limitations from these symptoms after adjusting for the underlying rhythm.
LimitationsWe acknowledge that the sample size in our study is insufficient to achieve the typical statistical power required for a robust multivariable adjustment. As such, this analysis should be considered hypothesis-generating, providing a basis for future research rather than definitive conclusions. The number of variables included in the multivariable regression analysis was limited due to the sample size; therefore, we prioritized incorporating the most relevant and impactful variables to ensure the robustness of the analysis. AF burden was not measured in our cohort which probably has an implication on AF related symptoms. Oxygen extraction was calculated based on samples obtained during electrophysiological studies performed under general anesthesia and mechanical ventilation, which may not represent ambulatory myocardial physiology.
CRediT authorship contribution statementLee Bockus: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. Ahmad Kassar: Writing – review & editing, Writing – original draft, Visualization, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Yaacoub Chahine: Software, Methodology, Data curation. Nadia Chamoun: Writing – review & editing, Data curation, Conceptualization. Romanos Haykal: Writing – review & editing, Data curation. Jason Li: Writing – review & editing. Dennis Wang: Writing – review & editing, Validation. Kevin O’Brien: Writing – review & editing, Validation. Rong Tian: Writing – review & editing, Validation, Methodology, Investigation. Nona Sotoodehnia: Writing – review & editing, Validation, Supervision. Karen Ordovas: Methodology. Nazem Akoum: Writing – review & editing, Visualization, Validation, Supervision, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
Conflicts of interestAll authors attest that there are no conflicts of interest to declare.
FundingNo extramural funding was used to support this work. The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the paper and its final contents.
Supplementary materialsSupplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ahj.2025.04.013.
Appendix Supplementary materialsImage, application 1
| Underlying rhythm | SR
| AF
| |
| Age (years) | 68.6 ± 8.8 | 63.16±12.9 | .1 |
| Female sex | 14 (52%) | 6 (33%) | .2 |
| BMI | 28.0 ± 5.9 | 29.4 ± 5.4 | .57 |
| Hypertension | 15 (56%) | 10 (59%) | .83 |
| CAD | 6 (22%) | 4 (22%) | 1.0 |
| Diabetes | 1 (4%) | 3 (6%) | .31 |
| Hyperlipidemia | 20 (74%) | 16 (89%) | .28 |
| Diagnosed AF subtype | .019 | ||
| | 25 (93%) | 11 (61%) | |
| | 2 (7%) | 7 (39%) | |
| mEHRA Score | .19 | ||
| | 11 (41%) | 4 (22%) | |
| Medications | |||
| Beta blocker | 22 (82%) | 10 (56%) | .06 |
| CCB | 5 (19%) | 4 (22%) | 1.0 |
| Antiarrhythmic | 12 (44%) | 2 (11%) | .018 |
| Loop diuretic | 2 (7%) | 1 (6%) | 1.0 |
| | SR | AF | |
| Heart rate (bpm) | 64.0 ± 10.7 | 80.6 ± 17.4 | .002 |
| Systolic BP (mmHg) | 133.4 ± 16.2 | 135 ± 15.8 | .75 |
| Diastolic BP (mmHg) | 77.0 ± 10.6 | 88.6 ± 11.6 | .002 |
| | |||
| Hemoglobin (g/L) | 14.1 ± 1.3 | 15 ± 1.3 | .024 |
| Arterial BNP | 100.4 ± 89.8 | 159.7 ± 103.7 | .049 |
| | |||
| Ejection fraction (%) | 62.5 ± 5.9 | 60.7 ± 5.4 | .32 |
| LVEDVi (mL/m 2) | 45.6 ± 15.3 | 56.1 ± 17.6 | .097 |
| | |||
| PA oxygen saturation (%) | 78.1 ± 4.8 | 74.3 ± 3.9 | .032 |
| LA oxygen saturation (%) | 97.8 ± 1.2 | 97.8 ± 1.3 | .88 |
| CS oxygen saturation (%) | 50.4 ± 11.1 | 44.0 ± 6.3 | .032 |
| Myocardial oxygen extraction (mL O
| 8.9 ± 2.0 | 10.8 ± 1.4 | .001 |
| MAP (mmHg) | 72.5 (65-84) | 69.5 (63-74) | .46 |
| Cardiac output (L/min) | 4.3 ± 1.5 | 4.1 ± 0.8 | .65 |
| Cardiac power (W) | 1.07 ± 0.32 | 0.74 ± 0.14 | .004 |
| | |||
| LV mass index (g/m 2) | 52.4 ± 11.1 | 58.4 ± 11.8 | .042 |
| CS blood flow (mL/min) | 78.1 ± 29.9 | 65.0 ± 38.1 | .35 |
| CS blood flow/100g | 65.0 ± 19.2 | 56.2 ± 34.0 | .42 |
| Oxygen extraction / cardiac power (mL/W·min) | 5.6 ± 2.2 | 9.6 ± 6.5 | .1 |
| Model 1 | ||||
| Univariate | | Multivariate | | |
| Underlying rhythm | 1.87 | .001 | 1.77 | .031 |
| Heart rate (BPM) | 0.034 | .09 | −0.008 | .749 |
| Diastolic blood pressure (mmHg) | 0.037 | .15 | ||
| BNP | −0.003 | .38 | ||
| LV mass index (g/m 2) | 0.001 | .96 | −0.025 | .343 |
| mEHRA score | 1.59 | .005 | 0.58 | .028 |
©2025. Elsevier Inc.