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Background:
Review

Predicting the Outcomes of External Direct Current Cardioversion for Atrial Fibrillation: A Narrative Review of Current Evidence

by
Ibrahim Antoun
1,2,*,
Georgia R. Layton
2,3,4,
Ahmed Abdelrazik
1,
Mahmoud Eldesouky
1,
Sherif Altoukhy
1,
Mustafa Zakkar
2,3,4,
Riyaz Somani
1,2 and
G. André Ng
1,2,3,5,*
1
Department of Cardiology, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Leicester LE5 4PW, UK
2
Department of Cardiovascular Sciences, Clinical Science Wing, University of Leicester, Glenfield Hospital, Leicester LE1 7RH, UK
3
Leicester British Heart Foundation Centre of Research Excellence, Glenfield Hospital, Leicester LE3 9QP, UK
4
Department of Cardiac Surgery, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Leicester LE5 4PW, UK
5
National Institute for Health Research Leicester Research Biomedical Centre, Leicester LE5 4PW, UK
*
Authors to whom correspondence should be addressed.
J. Cardiovasc. Dev. Dis. 2025, 12(5), 168; https://doi.org/10.3390/jcdd12050168
Submission received: 15 February 2025 / Revised: 18 April 2025 / Accepted: 23 April 2025 / Published: 25 April 2025
(This article belongs to the Special Issue Extensive Approach to Atrial Fibrillation: Background and Future Perspectives)
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Abstract

:
Atrial fibrillation (AF) is the most common sustained arrhythmia associated with significant morbidity and mortality. External direct current cardioversion (DCCV) is a cornerstone intervention for rhythm control in AF; however, its success is influenced by various patient-specific and procedural factors. This review examines the predictors of DCCV success and AF recurrence with specific focus upon demographics, biochemical, cardiovascular imaging, and P-wave parameters and their likely ability to predict procedural outcomes. Demographic factors such as age, sex, and comorbidities influence DCCV outcomes, with prolonged AF duration, obesity, and heart failure being associated with higher failure rates. Elevated biochemical markers of inflammation and fibrosis, including C-reactive protein, galectin-3, and Type III procollagen-N-peptide, were predictive of poor outcomes. Imaging parameters, particularly left atrial (LA) volume and strain, emerged as critical indicators of atrial remodelling and DCCV failure. Increased P-wave duration and dispersion on electrocardiography were associated with an increased risk of recurrence. Biphasic waveforms and antiarrhythmic drugs, such as amiodarone and flecainide, improved cardioversion success. The predictors of DCCV success and recurrence reflect the interplay of structural, biochemical, and electrical remodelling in AF. Integrating these parameters into clinical practice can guide individualised patient management and improve outcomes. Further research is needed to validate these predictors and enhance precision medicine approaches in DCCV.

1. Introduction

Atrial fibrillation (AF) prevalence and incidence has grown over the last 20 years and will continue to increase over the following 30 years, becoming one of the largest public cardiovascular health challenges and epidemics globally [1,2,3,4]. External direct current cardioversion (DCCV) is a cornerstone treatment for rhythm control in patients with AF, aiming to restore sinus rhythm and improve both symptomatic and prognostic outcomes [5]. However, the success of DCCV is influenced by numerous patient-specific and procedural factors, necessitating an understanding of predictors that can optimise patient selection and post-cardioversion outcomes. It is important to note that anticoagulation is crucial before and after DCCV. The latest European Society of Cardiology guidelines emphasise the importance of delaying DCCV when the patient is not on anticoagulation and AF onset was more than 24 h earlier [5].
Demographics, imaging modalities, and electrophysiological markers have been explored as potential predictors of DCCV success and AF recurrence. Demographic factors such as age, sex, and comorbidities provide insights into underlying risk profiles, while imaging techniques like echocardiography allow for detailed cardiac structure and function assessment. Parameters derived from advanced imaging modalities, such as MRI, including left atrial (LA) size, volume, and strain, are critical markers of atrial remodelling and fibrosis and are strongly linked to AF persistence [6].
In addition to structural and demographic factors, defibrillator pad positions, medications, and electrophysiological characteristics, particularly those related to the P-wave [7], offer a unique perspective on atrial conduction and arrhythmogenic potential. P-wave duration, dispersion, and amplitude reflect atrial electrical remodelling and may be non-invasive markers for predicting cardioversion success and AF recurrence risk. This review examines the current evidence on predictors of DCCV success and recurrence in AF, focusing on demographic characteristics, procedural aspects, cardiovascular imaging, and P-wave metrics. By synthesising findings across these domains, this review seeks to identify gaps in knowledge, highlight areas for future research, and support clinical decision-making for individualised management strategies. This review adopts a narrative approach to synthesise a summary review of existing evidence regarding the broad predictors of outcomes following external direct current DCCV for AF (Figure 1). Whilst a systematic review methodology, as outlined by PRISMA guidelines, provides structured appraisal, our objective was to integrate the diverse types of evidence, including demographic, biochemical, cardiovascular imaging, and electrophysiological predictors, into a cohesive summary. This review aims to highlight interdisciplinary connections, emphasise clinical context, and identify current gaps that systematic methods might not adequately capture.

1.1. Technical Predictors

The positioning of defibrillator pads is a critical factor influencing the success rates of electrical cardioversion in patients with AF. Various studies have explored the optimal pad placement and its implications for cardioversion outcomes (Figure 2). Notably, the anterior–lateral configuration was suggested to be superior to the anterior–posterior configuration for successful cardioversion, according to two studies [8,9]. Another study indicated that the anterior–posterior position was superior to the anterolateral for persistent AF [10]. Two later studies have shown that the exact location of the pads was not crucial in determining DCCV outcome [11,12]. In addition to pad positioning, the type of waveform used during cardioversion also plays a significant role. A systematic review comparing biphasic and monophasic waveforms found that biphasic waveforms are generally more effective in achieving successful cardioversion outcomes [13]. The increasing number of previous cardioversions independently predicts the risk of AF recurrence following DCCV, with a higher risk of early recurrence associated with a greater number of prior cardioversions [14].
Furthermore, total energy delivered during the procedure is crucial. A single-centre study proposed that three shocks with 360J were more effective than escalated shocks at 125-150-200 J [15]. Manual pressure on the pads has also increased success rate, especially in obese patients [16]. Additionally, the type of electrodes used in cardioversion is important. In a randomised clinical trial involving 201 patients, hand-held paddles improved the odds of DCCV’s success compared to the adhesive patches [17], especially in obese patients [18]. The same study proposed that manual pressure augmentation techniques can benefit these patients [18].

1.2. Antiarrhythmic Drugs Use

The use of antiarrhythmic drugs (AADs) following DCCV increased the success rate [19,20] with several agents have been suggested to improve sinus rhythm (SR) maintenance:
  • Flecainide and Propafenone: Both medications are classified as class IC antiarrhythmics and are known for their efficacy in converting AF to sinus rhythm, particularly in patients with paroxysmal AF. The effectiveness of these agents is comparable, with studies indicating that both flecainide and propafenone can achieve similar conversion rates to SR following DCCV [21]. Furthermore, flecainide is often recommended in clinical guidelines for early pharmacologic cardioversion due to its rapid onset of action and favourable safety profile in selected patients [22]. In addition to their efficacy in restoring sinus rhythm, flecainide and propafenone may influence long-term outcomes following DCCV. Research indicates that early rhythm control strategies, including administering these antiarrhythmics, can reduce the risk of AF recurrence and improve overall cardiovascular outcomes [23]. For instance, a systematic review highlighted that early pharmacologic cardioversion with agents like flecainide not only facilitates immediate conversion but may also serve as a bridge to longer-term rhythm control strategies [23,24].
Moreover, integrating these medications into managing AF can help mitigate the risk of thromboembolic events associated with cardioversion, particularly in patients with a high CHA2DS2-VA score [25]. However, it is crucial to consider the safety profile of these agents. Both flecainide and propafenone carry a risk of arrhythmia, particularly in patients with structural heart disease or significant left ventricular dysfunction [21,25]. Therefore, careful patient selection and monitoring are essential to optimise the benefits of these medications while minimising potential adverse effects. The decision to use flecainide or propafenone should be guided by individual patient characteristics, including the duration of AF, underlying heart conditions, and previous responses to antiarrhythmic therapy [23,26].
2.
Amiodarone is a widely utilised antiarrhythmic medication particularly in AF, where it is often administered to improve outcomes following DCCV. The efficacy of amiodarone in facilitating successful conversion from AF to SR has been documented in various studies, highlighting its role as a first-line agent in this setting. Research indicates that amiodarone can significantly enhance the success rate of electrical cardioversion according to a meta-analysis [27]. In addition to its efficacy, amiodarone’s pharmacological properties contribute to its effectiveness in this context. It acts by blocking sodium, calcium, and potassium channels and exhibiting beta-blocking effects, which help stabilise cardiac rhythm and facilitate successful cardioversion [28]. Combining electrical cardioversion with amiodarone administration has been shown to reduce the risk of immediate recurrence of AF following the procedure, thereby improving overall patient outcomes [29]. However, the use of amiodarone is not without concerns. While it is effective, there are potential risks associated with its administration, including the development of torsades de pointes, particularly in patients with underlying heart conditions [30]. Moreover, some studies have indicated that the pre-treatment with amiodarone does not always yield statistically significant improvements in cardioversion success rates compared to those who do not receive the drug, suggesting that its benefits may vary among different patient populations [31].
3.
Sotalol: Another class III antiarrhythmic agent commonly used in AF care pre-DCCV. In a systematic review and meta-analysis, sotalol demonstrated significant efficacy in the pharmacologic conversion of AF, with a higher rate of successful cardioversion than placebo [32]. Specifically, one study reported that sotalol had a cardioversion rate of 68% in rhythm control groups, significantly higher than the 42% observed in rate control groups [33]. This highlights Sotalol’s potential as a first-line agent for rhythm control in patients undergoing DCCV. Furthermore, intravenous sotalol has been explored as a rapid loading strategy, which may enhance the speed of achieving therapeutic levels and improve patient outcomes [34,35]. However, the safety profile of sotalol must be considered, particularly regarding its association with QT interval prolongation and the risk of TdP [36,37]. The American Heart Association (AHA) guidelines recommend careful monitoring of patients receiving sotalol, especially those with pre-existing conditions such as heart failure or renal impairment [38]. Despite these concerns, when used appropriately, sotalol has shown to be a safe and effective option for maintaining SR after DCCV, with studies indicating that the risk of adverse effects can be managed through careful patient selection and monitoring protocols [33,36].

1.3. Patient Factors (Demographics and Clinical Parameters)

Several patient demographics (including age, gender, and functional capacity) and clinical factors are associated with higher rates of AF recurrence after DCCV (Table 1) [39]. Older age, generally due to more structural atrial remodelling, maintain sinus rhythm at lower rates than younger patients [40]. Similarly, prolonged AF duration prior to DCCV is a significant predictor of failure due to the structural and electrical remodelling of the atria [41]. This is exacerbated by obesity through atrial enlargement, increased inflammation, and elevated left atrial pressure [41,42]. Left atrial size often signifies more several atrial remodelling or conduction disturbance, resulting in a higher likelihood of atrial recurrence [6]. Therefore, it is unsurprising that the presence of structural heart diseases which are also associated with left atrial enlargement, such as heart failure and valvular disease, also demonstrate higher risks of recurrence [6]. Female gender is associated with a higher risk of failure, potentially due to differences in atrial size as well as the hormonal influences of post-menopause [43]. At the same time, ischemic heart disease contributes to atrial fibrosis and scarring, reducing the success of cardioversion [44]. Advancing age further impairs outcomes due to age-related atrial fibrosis and reduced compliance [45], and heart failure—whether with reduced or preserved ejection fraction—worsens atrial remodelling and electrical dysfunction, making successful rhythm restoration more challenging [46,47,48]. Comorbid conditions such as diabetes mellitus [49], obstructive sleep apnoea (OSA) [50], renal impairment [51], and chronic obstructive pulmonary disease (COPD) also contribute to poor outcomes [52]; diabetes promotes atrial stiffness and inflammation, OSA induces atrial stretch and heightened sympathetic activity, renal impairment leads to systemic inflammation and volume overload, and COPD exacerbates arrhythmogenesis through hypoxia and right ventricular strain. The underlying pathology of AF influences the outcomes of DCCV. Most patients with hyperthyroidism-induced AF spontaneously restore their sinus rhythm following the return of the euthyroid state [53]. However, some patients develop hyperthyroidism-induced persistent AF, and this group has a significantly higher risk of recurrence following cardioversion compared to persistent AF of other origins [54]. Psychological factors like depression and anxiety further impair cardioversion success by disrupting autonomic balance and adherence to therapy [55]. At the same time, a high frailty score reflects reduced physiological reserve and a higher burden of comorbidities, which negatively affect procedural outcomes [56]. These factors highlight the multifaceted nature of DCCV failure and underscore the importance of individualised patient assessment and management.

1.4. Biochemical Markers

Several biochemical markers have been identified as predictors of DCCV outcomes in AF, reflecting the roles of inflammation, fibrosis, and atrial remodelling (Table 2). Elevated inflammatory markers, such as C-reactive protein (CRP), have been linked to poor DCCV success [59]. Fibrotic markers play a key role, including increased Type III procollagen-N-peptide [60], galectin-3 [61], and reduced circulating endothelial progenitor cells, which indicate endothelial dysfunction [62]. Elevated cardiac stress markers, such as B-type natriuretic peptide (BNP), correlate with atrial stretch and poor outcomes [63]. Additionally, increased interleukin-2 and heat shock protein 70, involved in inflammatory and cellular stress responses, have been linked to lower success rates [64]. Electrolyte disturbances like hypokalaemia and hypomagnesemia increase the occurrence of AF [65,66]. Therefore, correcting these imbalances before the procedure is crucial to optimising DCCV outcomes. These findings emphasise the multifactorial nature of DCCV outcomes, integrating inflammatory, fibrotic, genetic, and cardiac stress factors.

1.5. Genetic Predisposition

Genetic variants are increasingly recognised as potential contributors to AF susceptibility and recurrence, with emerging data suggesting they may also influence the efficacy of DCCV. Although the clinical utility of genetic screening remains limited, several polymorphisms—particularly on chromosome 4q25 near the PITX2 gene—have been associated with a higher likelihood of AF recurrence following successful DCCV [63,64]. These variants are believed to affect atrial electrical properties and structural remodelling, thereby lowering the threshold for AF reinitiation after cardioversion.
In addition to 4q25, mutations in genes encoding cardiac ion channels—such as SCN5A, KCNQ1, and CACNA1C—modulate sodium, potassium, and calcium currents, respectively, altering atrial conduction and excitability [65,66]. Variants in structural genes like NUP155 and LMNA, previously implicated in familial AF, may also play a role in atrial myopathy, thereby influencing cardioversion outcomes [67]. While most studies have focused on AF pathogenesis broadly, the relationship between these variants and DCCV outcomes specifically is gaining traction. For example, Shoemaker et al. [64] demonstrated that patients carrying the PITX2-linked 4q25 risk allele had significantly higher AF recurrence rates post-DCCV, independent of clinical risk factors.
Efforts to integrate genetic markers into risk prediction models have shown modest improvements in prognostic accuracy [68]. However, given that known variants account for only a small proportion of AF heritability, their standalone predictive value for DCCV outcomes remains limited. The development of polygenic risk scores and their combination with clinical and electrophysiological parameters may offer a more robust predictive framework in the future. Further research is needed to validate these approaches across diverse populations and to explore gene–environment interactions that modulate procedural success.

1.6. Cardiovascular Imaging

Cardiovascular imaging has proven to be a valuable tool for assessing structural and functional characteristics, whether echocardiography or magnetic resonance is used. It aids in predicting the success of DCCV in AF. Several imaging parameters correlate with DCCV outcomes, providing insight into the underlying atrial substrate and its impact on rhythm restoration.
Two studies have demonstrated that reduced left atrial (LA) appendage flow velocity is a key predictor of DCCV failure [69,70]. Decreased appendage flow velocity indicates impaired atrial contractility and may reflect advanced atrial remodelling, which reduces the likelihood of maintaining sinus rhythm. Atrial strain reflects the atrial myocardium’s ability to deform during the cardiac cycle, and its reduction signifies decreased atrial compliance and fibrosis.
Structural atrial remodelling also plays a significant role in DCCV success. Two studies by Marchese et al. demonstrated that an increased left atrial volume index (LAVI), an indicator of atrial enlargement, correlates with a higher likelihood of DCCV failure [71,72]. Additionally, valvular AF, particularly in patients with mitral valve disease, is strongly associated with increased LA volume [73], which, according to a recent meta-analysis, impairs the outcomes of DCCV and increases the recurrence rates [6].
While LAVI is significant, an increase in right atrial (RA) volume exceeding LAVI is an even stronger predictor of poor outcomes [74], emphasising the importance of evaluating both atria in predicting rhythm control success. Similarly, two studies confirmed that left atrial dilation is strongly associated with a reduced probability of successful cardioversion [75,76]. Atrial dilation reflects chronic pressure overload, structural remodelling, and fibrosis, all of which impair atrial electrical function (Table 3).

1.7. Electrocardiogram Markers

The assessment of AF outcomes following DCCV can be significantly enhanced by utilising various electrocardiographic (ECG) predictors. A comprehensive understanding of these predictors is essential for optimising patient management and improving clinical outcomes. The main predictive ECG parameters described are P wave-related. Increased P wave duration (PWD) and dispersion have been associated with DCCV failure [7,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92] (Table 4). It is evidence that integrating multiple ECG predictors, mainly P-wave dispersion and PWD, can significantly enhance the prediction of DCCV outcomes in AF patients [93].

2. The Role of Artificial Intelligence

Emerging evidence highlights the promising role of machine learning algorithms and artificial intelligence (AI) in predicting outcomes after electrical cardioversion for AF. These advanced computational approaches can effectively analyse and integrate large-scale heterogeneous datasets, encompassing clinical demographics, biochemical markers, cardiovascular imaging, and electrophysiological parameters, into robust predictive models. Recently, machine learning models demonstrated superior predictive performance compared to traditional statistical methods, providing more personalised and accurate risk stratification. Specifically, Kwon et al. developed a predictive model using machine learning algorithms, accurately forecasting AF recurrence after electrical cardioversion among patients with persistent AF. Their findings illustrate how integrating diverse clinical data through advanced machine-learning techniques significantly enhances prognostic precision [94]. Additionally, Núñez-García et al. established and validated a prognostic model leveraging machine learning to predict cardioversion outcomes, further underscoring the potential of these methods to refine patient selection, optimise clinical decision-making, and improve patient-specific outcomes following cardioversion [95]. Nevertheless, despite these promising developments, further validation in larger, multicentre cohorts and prospective studies is essential to ensure generalizability and effective integration of AI-derived models into routine clinical practice. Future research directions should also address AI models’ interpretability and real-world applicability within clinical workflows, thereby enabling widespread adoption in personalised AF management strategies.

3. Conclusions

This review article analyses all well-established predictors of DCCV outcomes. A comprehensive understanding of these predictors is essential for optimising patient selection, improving procedural success, and reducing AF recurrence. This review highlights the multifactorial nature of DCCV outcomes; integrating evidence from demographic, biochemical, imaging, and electrocardiographic domains. Demographic factors such as age, sex, and comorbid conditions, combined with structural changes identified through advanced imaging modalities, underscore the significant influence of atrial remodelling on cardioversion success. Biochemical markers provide critical insights into the roles of inflammation, fibrosis, and atrial stress in AF persistence, while genetic polymorphisms and electrophysiological characteristics, particularly P-wave metrics, offer additional tools for risk stratification. Cardiovascular imaging, particularly echocardiography, provides crucial insights into atrial structure and function, with parameters such as left atrial volume index, atrial strain, and appendage flow velocity offering prognostic value. Right atrial enlargement and biatrial remodelling further enhance risk stratification. These imaging-derived markers complement demographic and biochemical predictors to inform clinical decision-making.
Technical aspects of DCCV, including pad positioning, waveform type, and the use of antiarrhythmic drugs, also play pivotal roles in improving procedural outcomes. Advances in cardiovascular imaging and electrophysiology have further enhanced the ability to predict success and guide individualised treatment strategies.
Despite these advances, the current literature reveals significant gaps in knowledge. Many predictors lack consensus or require further validation through large, prospective, and multicentre studies. Future research should also explore integrating these predictors into practical clinical decision-making algorithms, focusing on precision medicine approaches that tailor interventions to individual patient profiles. We acknowledge the inherent limitations of narrative reviews, including potential selection bias and less reproducibility compared to systematic reviews. Nonetheless, our integrative narrative approach highlights the multifaceted interactions influencing cardioversion outcomes and provides clinically relevant insights that can inform future systematic evaluations. In conclusion, whilst our understanding DCCV predictors has significantly improved with time, continued interdisciplinary research is essential to refine this knowledge and to use it to generate predictive tools and integrate these effectively into clinical practice. By doing so, the management of AF in patients undergoing DCCV can be better optimised, improving both early and late outcomes and minimising the long-term morbidity of this condition.

Author Contributions

I.A.: Conceptualization, writing—original manuscript. M.E., G.R.L., A.A., M.Z., S.A., G.A.N. and R.S.: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

G Andre Ng is supported by a British Heart Foundation Programme Grant (RG/17/3/32,774) and the Medical Research Council Biomedical Catalyst Developmental Pathway Funding Scheme (MR/S037306/1). Mustafa Zakkar is supported by British Heart Foundation award (CH/12/1/29419) to the University of Leicester and Leicester NIHR Biomedical Research Centre (NIHR203327).

Institutional Review Board Statement

Ethical review and approval were waived for this study due to the review article not generating data.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data relating to this study are available upon reasonable request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lippi, G.; Sanchis-Gomar, F.; Cervellin, G. Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. Int. J. Stroke 2021, 16, 217–221. [Google Scholar] [CrossRef] [PubMed]
  2. Antoun, I.; Alkhayer, A.; Aljabal, M.; Mahfoud, Y.; Alkhayer, A.; Simon, P.; Kotb, A.; Barker, J.; Mavilakandy, A.; Hani, R.; et al. Thirty-Day Unplanned Readmissions Following Hospitalization for Atrial Fibrillation in a Tertiary Syrian Centre: A Real-World Observational Cohort Study. Heart Rhythm O2 2024, 5, 854–859. [Google Scholar] [CrossRef]
  3. Antoun, I.; Alkhayer, A.; Aljabal, M.; Mahfoud, Y.; Alkhayer, A.; Kotb, A.I.; Barker, J.; Somani, R.; Ng, G.A.; Zakkar, M. Incidence, outcomes, and predictors of new heart failure in syrian conflict-affected population following hospitalization for atrial fibrillation: A retrospective cohort study. Perfusion 2024. Online ahead of print. [Google Scholar] [CrossRef]
  4. Antoun, I.; Aljabal, M.; Alkhayer, A.; Mahfoud, Y.; Alkhayer, A.; Simon, P.; Kotb, A.; Barker, J.; Mavilakandy, A.; Naseer, M.U.; et al. Atrial fibrillation inpatient management patterns and clinical outcomes during the conflict in Syria: An observational cohort study. Perfusion 2024, 40, 711–719. [Google Scholar] [CrossRef] [PubMed]
  5. Van Gelder, I.C.; Rienstra, M.; Bunting, K.V.; Casado-Arroyo, R.; Caso, V.; Crijns, H.J.G.M.; De Potter, T.J.R.; Dwight, J.; Guasti, L.; Hanke, T.; et al. 2024 ESC Guidelines for the management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): Developed by the task force for the management of atrial fibrillation of the European Society of Cardiology (ESC), with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Endorsed by the European Stroke Organisation (ESO). Eur. Heart J. 2024, 45, 3314–3414. [Google Scholar] [CrossRef]
  6. Raniga, D.; Goda, M.; Hattingh, L.; Thorning, S.; Rowe, M.; Howes, L. Left atrial volume index: A predictor of atrial fibrillation recurrence following direct current cardioversion—A systematic review and meta-analysis. Int. J. Cardiol. Heart Vasc. 2024, 51, 101364. [Google Scholar] [CrossRef]
  7. Antoun, I.; Li, X.; Kotb, A.; Barker, J.; Mavilakandy, A.; Koev, I.; Vali, Z.; Somani, R.; Ng, G.A. Body surface mapping of P-waves in sinus rhythm to predict recurrence following cardioversion for atrial fibrillation. Front. Cardiovasc. Med. 2024, 11, 1417029. [Google Scholar] [CrossRef] [PubMed]
  8. Maqsood, N.; Cooper, J.M.; Whitman, I.R. Letter by Maqsood Et Al Regarding Article, “Anterior–Lateral Versus Anterior–Posterior Electrode Position for Cardioverting Atrial Fibrillation”. Circulation 2022, 145, 21. [Google Scholar] [CrossRef]
  9. Schmidt, A.S.; Lauridsen, K.G.; Møller, D.S.; Christensen, P.D.; Dodt, K.K.; Rickers, H.; Løfgren, B.; Albertsen, A.E. Anterior–Lateral Versus Anterior–Posterior Electrode Position for Cardioverting Atrial Fibrillation. Circulation 2021, 144, 1995–2003. [Google Scholar] [CrossRef]
  10. Kirchhof, P.; Eckardt, L.; Loh, P.; Weber, K.; Fischer, R.-J.; Seidl, K.-H.; Böcker, D.; Breithardt, G.; Haverkamp, W.; Borggrefe, M. Anterior-posterior versus anterior-lateral electrode positions for external cardioversion of atrial fibrillation: A randomised trial. Lancet 2002, 360, 1275–1279. [Google Scholar] [CrossRef]
  11. Kirkland, S.; Stiell, I.; AlShawabkeh, T.; Campbell, S.; Dickinson, G.; Rowe, B.H. The efficacy of pad placement for electrical cardioversion of atrial fibrillation/flutter: A systematic review. Acad. Emerg. Med. 2014, 21, 717–726. [Google Scholar] [CrossRef]
  12. Stiell, I.G.; Sivilotti, M.L.; Taljaard, M.; Birnie, D.; Vadeboncoeur, A.; Hohl, C.M.; McRae, A.D.; Rowe, B.H.; Brison, R.J.; Thiruganasambandamoorthy, V.; et al. Electrical versus pharmacological cardioversion for emergency department patients with acute atrial fibrillation (RAFF2): A partial factorial randomised trial. Lancet 2020, 395, 339–349. [Google Scholar] [CrossRef] [PubMed]
  13. Nguyen, S.T.; Belley-Côté, E.P.; Ibrahim, O.; Um, K.J.; Lengyel, A.; Adli, T.; Qiu, Y.; Wong, M.M.; Sibilio, S.; Benz, A.P.; et al. Techniques Improving Electrical Cardioversion Success for Patients with Atrial Fibrillation: A Systematic Review and Meta-Analysis. EP Eur. 2022, 25, 318–330. [Google Scholar] [CrossRef]
  14. Jacobs, V.; May, H.T.; Bair, T.L.; Crandall, B.G.; Cutler, M.J.; Day, J.D.; Le, V.; Mallender, C.; Osborn, J.S.; Weiss, J.P. The Impact of Repeated Cardioversions for Atrial Fibrillation on Stroke, Hospitalizations, and Catheter Ablation Outcomes. J. Atr. Fibrillation 2019, 11, 2164. [Google Scholar] [PubMed]
  15. Schmidt, A.S.; Lauridsen, K.G.; Torp, P.; Bach, L.F.; Rickers, H.; Løfgren, B. Maximum-Fixed Energy Shocks for Cardioverting Atrial Fibrillation. Eur. Heart J. 2019, 41, 626–631. [Google Scholar] [CrossRef] [PubMed]
  16. Khalighi, K.; Talebian, A.; Toor, R.; Mirabbasi, S.A. Successful External Cardioversion via Fluoroscopic Electrode Positioning in Patients With Enlarged Trans-Thoracic Diameter. Am. J. Case Rep. 2018, 19, 171–175. [Google Scholar] [CrossRef]
  17. Kirchhof, P.; Mönnig, G.; Wasmer, K.; Heinecke, A.; Breithardt, G.; Eckardt, L.; Böcker, D. A trial of self-adhesive patch electrodes and hand-held paddle electrodes for external cardioversion of atrial fibrillation (MOBIPAPA). Eur. Heart J. 2005, 26, 1292–1297. [Google Scholar] [CrossRef]
  18. Voskoboinik, A.; Moskovitch, J.; Plunkett, G.; Bloom, J.; Wong, G.; Nalliah, C.; Prabhu, S.; Sugumar, H.; Paramasweran, R.; McLellan, A.; et al. Cardioversion of Atrial Fibrillation in Obese Patients: Results From the Cardioversion-BMI Randomized Controlled Trial. J. Cardiovasc. Electrophysiol. 2018, 30, 155–161. [Google Scholar] [CrossRef]
  19. Fetsch, T.; Bauer, P.; Engberding, R.; Koch, H.P.; Lukl, J.; Meinertz, T.; Oeff, M.; Seipel, L.; Trappe, H.J.; Treese, N. Prevention of atrial fibrillation after cardioversion: Results of the PAFAC trial. Eur. Heart J. 2004, 25, 1385–1394. [Google Scholar] [CrossRef]
  20. Lafuente-Lafuente, C.; Valembois, L.; Bergmann, J.F.; Belmin, J. Antiarrhythmics for maintaining sinus rhythm after cardioversion of atrial fibrillation. Cochrane Database Syst. Rev. 2015, 3, CD005049. [Google Scholar] [CrossRef]
  21. Tracevskis, M. Effectiveness of Amiodarone, Propafenone and Synchronized Electrical Cardioversion for Conversion of Atrial Fibrillation Paroxysm to Sinus Rhythm in Rigas Stradins University Hospital Department of Emergency Medicine. Clin. Med. Health Res. J. 2023, 3, 485–488. [Google Scholar] [CrossRef]
  22. Aliot, E.; Capucci, A.; Crijns, H.J.; Goette, A.; Tamargo, J. Twenty-five years in the making: Flecainide is safe and effective for the management of atrial fibrillation. EP Eur. 2010, 13, 161–173. [Google Scholar] [CrossRef] [PubMed]
  23. deSouza, I.S.; Tadrous, M.; Sexton, T.; Benabbas, R.; Carmelli, G.; Sinert, R. Pharmacologic Cardioversion of Recent-Onset Atrial Fibrillation and Flutter in the Emergency Department: A Systematic Review and Network Meta-Analysis. Ann. Emerg. Med. 2020, 76, 14–30. [Google Scholar] [CrossRef]
  24. Castrichini, M.; Restivo, L.; Fabris, E.; Massa, L.; Di Meola, R.; Beltrame, D.; De Luca, A.; Korcova, R.; Milo, M.; Sinagra, G. Prevalence and predictors of persistent sinus rhythm after elective electrical cardioversion for atrial fibrillation. J. Cardiovasc. Med. 2021, 22, 626–630. [Google Scholar] [CrossRef] [PubMed]
  25. Coppola, G.; Manno, G.; Mignano, A.; Luparelli, M.; Zarcone, A.; Novo, G.; Corrado, E. Management of Direct Oral Anticoagulants in Patients With Atrial Fibrillation Undergoing Cardioversion. Medicina 2019, 55, 660. [Google Scholar] [CrossRef]
  26. Airaksinen, J. How to Optimize Cardioversion of Atrial Fibrillation. J. Clin. Med. 2022, 11, 3372. [Google Scholar] [CrossRef]
  27. Um, K.J.; McIntyre, W.F.; Healey, J.S.; Mendoza, P.A.; Koziarz, A.; Amit, G.; Chu, V.A.; Whitlock, R.P.; Belley-Côté, E.P. Pre- and post-treatment with amiodarone for elective electrical cardioversion of atrial fibrillation: A systematic review and meta-analysis. EP Eur. 2019, 21, 856–863. [Google Scholar] [CrossRef]
  28. Nattel, S.; Talajic, M.; Fermini, B.; Roy, D. Amiodarone: Pharmacology, clinical actions, and relationships between them. J. Cardiovasc. Electrophysiol. 1992, 3, 266–280. [Google Scholar] [CrossRef]
  29. Pye, E. Early Recognition With Capnography, and Successful Resuscitation, of Severe Cardiac Dysfunction in a Horse Receiving Amiodarone During Transvenous Electric Cardioversion (TVEC). Equine Vet. Educ. 2023, 36, e113–e120. [Google Scholar] [CrossRef]
  30. Nordkin, I.; Levinas, T.; Rosenfeld, I.; Halabi, M. Torsades De Pointes After Prolonged Intravenous Amiodarone Therapy for Atrial Fibrillation. Clin. Case Rep. 2020, 9, 391–394. [Google Scholar] [CrossRef]
  31. Seo, J.; Singh, M.K. Electrical Cardioversion in a Dog With Atrial Fibrillation and Hypothyroidism. Vet. Rec. Case Rep. 2018, 6, e000583. [Google Scholar] [CrossRef]
  32. Milan, D.J.; Saul, J.P.; Somberg, J.C.; Molnar, J. Efficacy of Intravenous and Oral Sotalol in Pharmacologic Conversion of Atrial Fibrillation: A Systematic Review and Meta-Analysis. Cardiology 2016, 136, 52–60. [Google Scholar] [CrossRef] [PubMed]
  33. Sawh, C.; Rashid, S. The Safety and Efficacy of Sotalol in the Management of Acute Atrial Fibrillation: A Retrospective Case Control Study. Interv. Cardiol. 2016, 8, 637–642. [Google Scholar] [CrossRef]
  34. Lakkireddy, D.; Ahmed, A.; Atkins, D.; Bawa, D.; Garg, J.; Bush, J.; Charate, R.; Bommana, S.; Pothineni, N.V.K.; Kabra, R.; et al. Feasibility and Safety of Intravenous Sotalol Loading in Adult Patients With Atrial Fibrillation (DASH-AF). Jacc Clin. Electrophysiol. 2023, 9, 555–564. [Google Scholar] [CrossRef] [PubMed]
  35. Feuerborn, M.L.; Dechand, J.; Vadlamudi, R.S.; Torre, M.; Freedman, R.A.; Groh, C.A.; Navaravong, L.; Ranjan, R.; Varela, D.; Bunch, T.J.; et al. Protocol Development and Initial Experience With Intravenous Sotalol Loading for Atrial Arrhythmias. Crit. Pathw. Cardiol. A J. Evid.-Based Med. 2022, 22, 1–4. [Google Scholar] [CrossRef]
  36. Mascarenhas, D.; Mudumbi, P.C.; Kantharia, B.K. Outpatient Initiation of Sotalol in Patients With Atrial Fibrillation: Utility of Cardiac Implantable Electronic Devices for Therapy Monitoring. Am. J. Cardiovasc. Drugs 2021, 21, 693–700. [Google Scholar] [CrossRef]
  37. Rabatin, A.; Mj, S.; Jm, B.; Houmsse, M. Safety of Twice Daily Sotalol in Patients With Renal Impairment: A Single Center, Retrospective Review. J. Atr. Fibrillation 2018, 11, 2047. [Google Scholar] [CrossRef]
  38. Singh, J.P.; Blomström-Lundqvist, C.; Turakhia, M.P.; Fazeli, M.S.; Kreidieh, B.; Crotty, C.P.; Kowey, P.R. Dronedarone Versus Sotalol in Patients With Atrial Fibrillation: A Systematic Literature Review and Network Meta-analysis. Clin. Cardiol. 2023, 46, 589–597. [Google Scholar] [CrossRef]
  39. Charitakis, E.; Dragioti, E.; Stratinaki, M.; Korela, D.; Tzeis, S.; Almroth, H.; Liuba, I.; Jönsson, A.H.; Charalambous, G.; Karlsson, L.O.; et al. Predictors of recurrence after catheter ablation and electrical cardioversion of atrial fibrillation: An umbrella review of meta-analyses. EP Eur. 2023, 25, 40–48. [Google Scholar] [CrossRef]
  40. Curtis, A.B.; Karki, R.; Hattoum, A.; Sharma, U.C. Arrhythmias in patients ≥80 years of age: Pathophysiology, management, and outcomes. J. Am. Coll. Cardiol. 2018, 71, 2041–2057. [Google Scholar] [CrossRef]
  41. Frick, M.; Frykman, V.; Jensen-Urstad, M.; Östergren, J. Factors predicting success rate and recurrence of atrial fibrillation after first electrical cardioversion in patients with persistent atrial fibrillation. Clin. Cardiol. 2001, 24, 238–244. [Google Scholar] [CrossRef] [PubMed]
  42. Brodsky, M.A.; Allen, B.J.; Capparelli, E.V.; Luckett, C.R.; Morton, R.; Henry, W.L. Factors determining maintenance of sinus rhythm after chronic atrial fibrillation with left atrial dilatation. Am. J. Cardiol. 1989, 63, 1065–1068. [Google Scholar] [CrossRef]
  43. Westerman, S.; Wenger, N. Gender differences in atrial fibrillation: A review of epidemiology, management, and outcomes. Curr. Cardiol. Rev. 2019, 15, 136–144. [Google Scholar] [CrossRef] [PubMed]
  44. Suttorp, M.J.; Kingma, J.H.; Koomen, E.M.; van’t Hof, A.; Tijssen, J.G.; Lie, K.I. Recurrence of paroxysmal atrial fibrillation or flutter after successful cardioversion in patients with normal left ventricular function. Am. J. Cardiol. 1993, 71, 710–713. [Google Scholar] [CrossRef] [PubMed]
  45. Alt, E.; Ammer, R.; Lehmann, G.; Pütter, K.; Ayers, G.M.; Pasquantonio, J.; Schömig, A. Patient characteristics and underlying heart disease as predictors of recurrent atrial fibrillation after internal and external cardioversion in patients treated with oral sotalol. Am. Heart. J. 1997, 134, 419–425. [Google Scholar] [CrossRef]
  46. van den Berg, M.P.; Tuinenburg, A.E.; van Veldhuisen, D.J.; de Kam, P.J.; Crijns, H.J.G.M. Cardioversion of atrial fibrillation in the setting of mild to moderate heart failure. Int. J. Cardiol. 1998, 63, 63–70. [Google Scholar] [CrossRef]
  47. Caputo, M.; Urselli, R.; Capati, E.; Navarri, R.; Sinesi, L.; Furiozzi, F.; Ballo, P.; Palazzuoli, A.; Favilli, R.; Mondillo, S. Usefulness of left ventricular diastolic dysfunction assessed by pulsed tissue Doppler imaging as a predictor of atrial fibrillation recurrence after successful electrical cardioversion. Am. J. Cardiol. 2011, 108, 698–704. [Google Scholar] [CrossRef]
  48. Melduni, R.M.; Cullen, M.W. Role of left ventricular diastolic dysfunction in predicting atrial fibrillation recurrence after successful electrical cardioversion. J. Atr. Fibrillation 2012, 5, 654. [Google Scholar]
  49. Soran, H.; Younis, N.; Currie, P.; Silas, J.; Jones, I.; Gill, G. Influence of diabetes on the maintenance of sinus rhythm after a successful direct current cardioversion in patients with atrial fibrillation. Q.J.M. 2008, 101, 181–187. [Google Scholar] [CrossRef]
  50. Kanagala, R.; Murali, N.S.; Friedman, P.A.; Ammash, N.M.; Gersh, B.J.; Ballman, K.V.; Shamsuzzaman, A.S.; Somers, V.K. Obstructive sleep apnea and the recurrence of atrial fibrillation. Circulation 2003, 107, 2589–2594. [Google Scholar] [CrossRef]
  51. Schmidt, M.; Daccarett, M.; Rittger, H.; Marschang, H.; Holzmann, S.; Jung, P.; Bojanic, D.; Ketteler, M.; Brachmann, J.; Rieber, J. Renal dysfunction and atrial fibrillation recurrence following cardioversion. J. Cardiovasc. Electrophysiol. 2011, 22, 1092–1098. [Google Scholar] [CrossRef] [PubMed]
  52. Pisters, R.; Nieuwlaat, R.; Prins, M.H.; Le Heuzey, J.-Y.; Maggioni, A.P.; Camm, A.J.; Crijns, H.J.G.M.; Euro Heart Survey Investigators. Clinical correlates of immediate success and outcome at 1-year follow-up of real-world cardioversion of atrial fibrillation: The Euro Heart Survey. Europace 2012, 14, 666–674. [Google Scholar] [CrossRef] [PubMed]
  53. Kostopoulos, G.; Effraimidis, G. Epidemiology, prognosis, and challenges in the management of hyperthyroidism-related atrial fibrillation. Eur. Thyroid. J. 2024, 13, e230254. [Google Scholar] [CrossRef]
  54. Siu, C.-W.; Jim, M.-H.; Zhang, X.; Chan, Y.-H.; Pong, V.; Kwok, J.; Kung, A.W.; Lau, C.-P.; Tse, H.-F. Comparison of atrial fibrillation recurrence rates after successful electrical cardioversion in patients with hyperthyroidism-induced versus non-hyperthyroidism-induced persistent atrial fibrillation. Am. J. Cardiol. 2009, 103, 540–543. [Google Scholar] [CrossRef] [PubMed]
  55. Koleck, T.A.; Mitha, S.A.; Biviano, A.; Caceres, B.A.; Corwin, E.J.; Goldenthal, I.; Creber, R.M.; Turchioe, M.R.; Hickey, K.T.; Bakken, S. Exploring depressive symptoms and anxiety among patients with atrial fibrillation and/or flutter at the time of cardioversion or ablation. J. Cardiovasc. Nurs. 2021, 36, 470–481. [Google Scholar] [CrossRef]
  56. Mlynarska, A.; Mlynarski, R.; Marcisz, C.; Golba, K.S. Modified frailty as a novel factor in predicting the maintenance of the sinus rhythm after electrical cardioversion of atrial fibrillation in the elderly population. Clin. Interv. Aging 2020, 15, 1193–1199. [Google Scholar] [CrossRef]
  57. Lange, H.W.; Herrmann-Lingen, C. Depressive symptoms predict recurrence of atrial fibrillation after cardioversion. J. Psychosom. Res. 2007, 63, 509–513. [Google Scholar] [CrossRef]
  58. García-Izquierdo, E.; Fajardo-Simón, L.; Cruz-Utrilla, A.; Aguilera-Agudo, C.; Jiménez-Sánchez, D.; Sánchez-García, M.; Lobo, A.B.; Escudier-Villa, J.M.; Ortega-Marcos, J.; Silva-Melchor, L. The role of anxiety in patients with persistent atrial fibrillation undergoing elective cardioversion: An observational study. Psychosom. Med. 2020, 82, 744–750. [Google Scholar] [CrossRef]
  59. Liu, T.; Li, G.; Li, L.; Korantzopoulos, P. Association between C-reactive protein and recurrence of atrial fibrillation after successful electrical cardioversion. J. Am. Coll. Cardiol. 2007, 49, 1642–1648. [Google Scholar] [CrossRef]
  60. Kawamura, M.; Munetsugu, Y.; Kawasaki, S.; Onishi, K.; Onuma, Y.; Kikuchi, M.; Tanno, K.; Kobayashi, Y. Type III procollagen-N-peptide as a predictor of persistent atrial fibrillation recurrence after cardioversion. EP Eur. 2012, 14, 1719–1725. [Google Scholar] [CrossRef]
  61. Gürses, K.M.; Yalçın, M.U.; Koçyiğit, D.; Canpınar, H.; Ateş, A.H.; Canpolat, U.; Yorgun, H.; Güç, D.; Aytemir, K. Serum galectin-3 level predicts early recurrence following successful direct-current cardioversion in persistent atrial fibrillation patients. Turk. Kardiyol. Dern. Ars. 2019, 47, 564–571. [Google Scholar] [PubMed]
  62. Siu, C.-W.; Watson, T.; Lai, W.-H.; Lee, Y.-K.; Chan, Y.-H.; Ng, K.-M.; Lau, C.-P.; Lip, G.Y.H.; Tse, H.-F. Relationship of circulating endothelial progenitor cells to the recurrence of atrial fibrillation after successful conversion and maintenance of sinus rhythm. EP Eur. 2009, 12, 517–521. [Google Scholar] [CrossRef]
  63. Zografos, T.; Maniotis, C.; Katsivas, A.; Katritsis, D. Relationship between brain natriuretic peptides and recurrence of atrial fibrillation after successful direct current cardioversion: A meta-analysis. Pacing Clin. Electrophysiol. 2014, 37, 1530–1537. [Google Scholar] [CrossRef] [PubMed]
  64. Rigopoulos, A.G.; Kalogeropoulos, A.S.; Tsoporis, J.N.; Sakadakis, E.A.; Triantafyllis, A.S.; Noutsias, M.; Gupta, S.; Parker, T.G.; Rizos, I. Heat shock protein 70 is associated with cardioversion outcome and recurrence of symptomatic recent onset atrial fibrillation in hypertensive patients. J. Cardiovasc. Pharmacol. 2021, 77, 3. [Google Scholar] [CrossRef]
  65. Wu, Y.; Kong, X.-J.; Ji, Y.-Y.; Fan, J.; Ji, C.-C.; Chen, X.-M.; Ma, Y.-D.; Tang, A.-L.; Cheng, Y.-J.; Wu, S.-H. Serum electrolyte concentrations and risk of atrial fibrillation: An observational and mendelian randomization study. BMC Genom. 2024, 25, 280. [Google Scholar] [CrossRef]
  66. Lu, Y.-Y.; Cheng, C.-C.; Chen, Y.-C.; Lin, Y.-K.; Chen, S.-A.; Chen, Y.-J. Electrolyte disturbances differentially regulate sinoatrial node and pulmonary vein electrical activity: A contribution to hypokalemia- or hyponatremia-induced atrial fibrillation. Heart Rhythm. 2016, 13, 781–788. [Google Scholar] [CrossRef] [PubMed]
  67. Parvez, B.; Shoemaker, M.B.; Muhammad, R.; Richardson, R.; Jiang, L.; Blair, M.A.; Roden, D.M.; Darbar, D. Common genetic polymorphism at 4q25 locus predicts atrial fibrillation recurrence after successful cardioversion. Heart Rhythm. 2013, 10, 849–855. [Google Scholar] [CrossRef]
  68. Shoemaker, M.B.; Bollmann, A.; Lubitz, S.A.; Ueberham, L.; Saini, H.; Montgomery, J.; Edwards, T.; Yoneda, Z.; Sinner, M.F.; Arya, A. Common genetic variants and response to atrial fibrillation ablation. Circ. Arrhythm. Electrophysiol. 2015, 8, 296–302. [Google Scholar] [CrossRef]
  69. Verhorst, P.M.; Kamp, O.; Welling, R.C.; Van Eenige, M.J.; Visser, C.A. Transesophageal echocardiographic predictors for maintenance of sinus rhythm after electrical cardioversion of atrial fibrillation. Am. J. Cardiol. 1997, 79, 1355–1359. [Google Scholar] [CrossRef]
  70. Kostakou, P.; Kostopoulos, V.; Stamatelatou, M.; Tryfou, E.; Mihas, K.; Damaskos, D.; Tsougos, E.; Olympios, C.; Kouris, N. Tissue Doppler imaging of left atrial appendage during transoesophageal echocardiography predicts successful cardioversion in patients with atrial fibrillation. Eur. Heart J. Cardiovasc. Imaging 2021, 22 (Suppl. S1), jeaa356.102. [Google Scholar] [CrossRef]
  71. Marchese, P.; Bursi, F.; Delle Donne, G.; Malavasi, V.; Casali, E.; Barbieri, A.; Melandri, F.; Modena, M.G. Indexed left atrial volume predicts the recurrence of non-valvular atrial fibrillation after successful cardioversion. Eur. J. Echocardiogr. 2010, 12, 214–221. [Google Scholar] [CrossRef] [PubMed]
  72. Marchese, P.; Malavasi, V.; Rossi, L.; Nikolskaya, N.; Donne, G.D.; Becirovic, M.; Colantoni, A.; Luciani, A.; Modena, M.G. Indexed left atrial volume is superior to left atrial diameter in predicting nonvalvular atrial fibrillation recurrence after successful cardioversion: A prospective study. Echocardiography 2012, 29, 276–284. [Google Scholar] [CrossRef]
  73. Marques-Alves, P.; Marinho, A.V.; Domingues, C.; Baptista, R.; Castro, G.; Martins, R.; Gonçalves, L. Left atrial mechanics in moderate mitral valve disease: Earlier markers of damage. Int. J. Cardiovasc. Imaging 2020, 36, 23–31. [Google Scholar] [CrossRef] [PubMed]
  74. Luong, C.; Thompson, D.J.; Bennett, M.; Gin, K.; Jue, J.; Barnes, M.E.; Colley, P.; Tsang, T.S. Right atrial volume is superior to left atrial volume for prediction of atrial fibrillation recurrence after direct current cardioversion. Can. J. Cardiol. 2015, 16, 29–35. [Google Scholar] [CrossRef]
  75. Fornengo, C.; Antolini, M.; Frea, S.; Gallo, C.; Grosso Marra, W.; Morello, M.; Gaita, F. Prediction of atrial fibrillation recurrence after cardioversion in patients with left-atrial dilation. Eur. Heart J. Cardiovasc. Imaging 2015, 16, 335–341. [Google Scholar] [CrossRef]
  76. Toufan, M.; Kazemi, B.; Molazadeh, N. The significance of the left atrial volume index in prediction of atrial fibrillation recurrence after electrical cardioversion. J. Cardiovasc. Thorac. Res. 2017, 9, 54. [Google Scholar] [CrossRef] [PubMed]
  77. Di Salvo, G.; Caso, P.; Lo Piccolo, R.; Fusco, A.; Martiniello, A.R.; Russo, M.G.; D’Onofrio, A.; Severino, S.; Calabró, P.; Pacileo, G. Atrial myocardial deformation properties predict maintenance of sinus rhythm after external cardioversion of recent-onset lone atrial fibrillation: A color Doppler myocardial imaging and transthoracic and transesophageal echocardiographic study. Circulation 2005, 112, 387–395. [Google Scholar] [CrossRef]
  78. Opolski, G.; Ścisło, P.; Stanisławska, J.; Górecki, A.; Steckiewicz, R.; Torbicki, A. Detection of patients at risk for recurrence of atrial fibrillation after successful electrical cardioversion by signal-averaged P-wave ECG. Int. J. Cardiol. 1997, 60, 181–185. [Google Scholar] [CrossRef]
  79. Choi, J.-H.; Kwon, H.-J.; Kim, H.R.; Park, S.-J.; Kim, J.S.; On, Y.K.; Park, K.-M. Electrocardiographic predictors of early recurrence of atrial fibrillation. Ann. Noninvasive Electrocardiol. 2021, 26, e12884. [Google Scholar] [CrossRef]
  80. Dogan, A.; Avsar, A.; Ozturk, M. P-wave dispersion for predicting maintenance of sinus rhythm after cardioversion of atrial fibrillation. Am. J. Cardiol. 2004, 93, 368–371. [Google Scholar] [CrossRef]
  81. Stafford, P.J.; Kamalvand, K.; Tan, K.; Vincent, R.; Sulke, N. Prediction of maintenance of sinus rhythm after cardioversion of atrial fibrillation by analysis of serial signal-averaged P waves. Pacing Clin. Electrophysiol. 1998, 21, 1387–1395. [Google Scholar] [CrossRef] [PubMed]
  82. Aytemir, K.; Aksoyek, S.; Yildirir, A.; Ozer, N.; Oto, A. Prediction of atrial fibrillation recurrence after cardioversion by P wave signal-averaged electrocardiography. Int. J. Cardiol. 1999, 70, 15–21. [Google Scholar] [CrossRef] [PubMed]
  83. Raitt, M.H.; Ingram, K.D.; Thurman, S.M. Signal-averaged P wave duration predicts early recurrence of atrial fibrillation after cardioversion. Pacing. Clin. Electrophysiol. 2000, 23, 259–265. [Google Scholar] [CrossRef]
  84. Dixen, U.; Joens, C.; Parner, J.; Rasmussen, V.; Pehrson, S.; Jensen, G. Prolonged signal-averaged P wave duration after elective cardioversion increases the risk of recurrent atrial fibrillation. Scand. Cardiovasc. J. 2004, 38, 147–151. [Google Scholar] [CrossRef]
  85. Guo, X.H.; Gallagher, M.M.; Poloniecki, J.; Yi, G.; Camm, A.J. Prognostic significance of serial P wave signal-averaged electrocardiograms following external electrical cardioversion for persistent atrial fibrillation: A prospective study. Pacing Clin. Electrophysiol. 2003, 26, 299–304. [Google Scholar] [CrossRef] [PubMed]
  86. Ehrlich, J.; Schadow, K.; Steul, K.; Zhang, G.; Israel, C.; Hohnloser, S. Prediction of early recurrence of atrial fibrillation after external cardioversion by means of P wave signal-averaged electrocardiogram. Z. Kardiol. 2003, 92, 540–546. [Google Scholar] [CrossRef]
  87. Perzanowski, C.; Ho, A.T.; Jacobson, A.K. Increased P-wave dispersion predicts recurrent atrial fibrillation after cardioversion. J. Electrocardiol. 2005, 38, 43–46. [Google Scholar] [CrossRef]
  88. Budeus, M.; Hennersdorf, M.; Perings, C.; Wieneke, H.; Erbel, R.; Sack, S. Prediction of the recurrence of atrial fibrillation after successful cardioversion with P wave signal-averaged ECG. Ann. Noninvasive Electrocardiol. 2005, 10, 414–419. [Google Scholar] [CrossRef]
  89. Başar, N.; Malçok Gürel, O.; Ozcan, F.; Ozlü, M.F.; Biçer Yeşilay, A.; Cağlı, K.; Sen, N.; Işleyen, A.; Uygur, B.; Akpınar, I.; et al. Diagnostic accuracy of P-wave dispersion in prediction of maintenance of sinus rhythm after external cardioversion of atrial fibrillation. Anadolu. Kardiyol. Derg. 2011, 11, 34–38. [Google Scholar] [CrossRef]
  90. Gonna, H.; Gallagher, M.M.; Guo, X.H.; Yap, Y.G.; Hnatkova, K.; Camm, A.J. P-wave abnormality predicts recurrence of atrial fibrillation after electrical cardioversion: A prospective study. Ann. Noninvasive Electrocardiol. 2014, 19, 57–62. [Google Scholar] [CrossRef]
  91. Blanche, C.; Tran, N.; Carballo, D.; Rigamonti, F.; Burri, H.; Zimmermann, M. Usefulness of P-wave signal averaging to predict atrial fibrillation recurrences after electrical cardioversion. Ann. Noninvasive Electrocardiol. 2014, 19, 266–272. [Google Scholar] [CrossRef] [PubMed]
  92. Fujimoto, Y.; Yodogawa, K.; Maru, Y.-J.; Oka, E.; Hayashi, H.; Yamamoto, T.; Iwasaki, Y.-K.; Hayashi, M.; Shimizu, W. Advanced interatrial block is an electrocardiographic marker for recurrence of atrial fibrillation after electrical cardioversion. Int. J. Cardiol. 2018, 272, 113–117. [Google Scholar] [CrossRef] [PubMed]
  93. German, D.M.; Kabir, M.M.; Dewland, T.A.; Henrikson, C.A.; Tereshchenko, L.G. Atrial fibrillation predictors: Importance of the electrocardiogram. Ann. Noninvasive Electrocardiol. 2016, 21, 20–29. [Google Scholar] [CrossRef] [PubMed]
  94. Kwon, S.; Lee, E.; Ju, H.; Ahn, H.-J.; Lee, S.-R.; Choi, E.-K.; Suh, J.; Oh, S.; Rhee, W. Machine learning prediction for the recurrence after electrical cardioversion of patients with persistent atrial fibrillation. Korean Circ. J. 2023, 53, 677–689. [Google Scholar] [CrossRef]
  95. Nunez-Garcia, J.C.; Sanchez-Puente, A.; Sampedro-Gómez, J.; Vicente-Palacios, V.; Jiménez-Navarro, M.; Oterino-Manzanas, A.; Jimenez-Candil, J.; Dorado-Diaz, P.I.; Sanchez, P.L. Outcome analysis in elective electrical cardioversion of atrial fibrillation patients: Development and validation of a machine learning prognostic model. J. Clin. Med. 2022, 11, 2636. [Google Scholar] [CrossRef]
Jcdd 12 00168 g001
Figure 1. A summary of the demographic, imaging, investigative, and pharmacological factors known to predict freedom from atrial fibrillation following direct current cardioversion. DCCV: direct current cardioversion. MRI: magnetic resonance imaging. LA: left atrium. RA: right atrium. AF: atrial fibrillation.
Figure 1. A summary of the demographic, imaging, investigative, and pharmacological factors known to predict freedom from atrial fibrillation following direct current cardioversion. DCCV: direct current cardioversion. MRI: magnetic resonance imaging. LA: left atrium. RA: right atrium. AF: atrial fibrillation.
Jcdd 12 00168 g001
Jcdd 12 00168 g002
Figure 2. Anterolateral and anteroposterior external cardioversion positions.
Figure 2. Anterolateral and anteroposterior external cardioversion positions.
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Table 1. The role of demographics and clinical factors in predicting electrical cardioversion outcomes for atrial fibrillation.
Table 1. The role of demographics and clinical factors in predicting electrical cardioversion outcomes for atrial fibrillation.
StudyStudy Identified Demographic or Clinical Factor Associayed with DCCV Failure
Frick et al., 2001, Brodsky et al., 1989 [41,42]Obesity, Increasing AF duration
Suttorp et al., 1993 [44]Females, Ischaemic heart disease
Alt et al., 1997 [45]Increasing age
van den Berg et al., 1998, Caputo et al., 2011, Melduni and Cullen, 2012 [46,47,48]Heart failure (reduced and preserved ejection fraction)
Lange and Herrmann-Lingen, 2007 [57]Low mood
Soran et al., 2008 [49]Diabetes melitus
Kanagala et al., 2003 [50]Obstructive sleep apnoea
Schmidt et al., 2011 [51]Renal impairment
Pisters et al., 2012 [52]Chronic obstructive lung disease
Mlynarska et al., 2020 [56]High frailty score
García-Izquierdo et al., 2020 [58]Anxiety
Table 2. Biochemical predictors of direct current cardioversion failure for atrial fibrillation.
Table 2. Biochemical predictors of direct current cardioversion failure for atrial fibrillation.
StudyBiochemical Markers Relation to Direct Current Cardioversion Failure
Liu et al., 2007 [59]Increased C-reactive protein
Siu et al., 2009 [62]Circulating endothelial progenitor cell count ↓ (fibrotic marker)
Kawamura et al., 2012 [60]Type III procollagen-N-peptide ↑ (fibrotic marker)
Parvez et al., 2013, Shoemaker et al., 2015 [67,68]Polymorphisms on the 4q25 chromosome
Zografos et al., 2014 [63]Increased B-type natriuretic peptide
Gürses et al., 2019 [61]Galectin 3 (fibrotic marker)
Rigopoulos et al., 2021 [64]Serum interleukin 2 ↑, heat shock protein 70 ↑ (involved in cellular protein folding)
↑: increase. ↓: decrease.
Table 3. Correlation between cardiovascular imaging and cardioversion recurrence.
Table 3. Correlation between cardiovascular imaging and cardioversion recurrence.
StudyImaging Correlation with DCCV Failure
Verhorst et al., 1997, Kostakou et al., 2021 [69,70]LA appendage flow ↓
Di Salvo et al., 2005 [77]Atrial strain ↓
Marchese et al., 2010, Marchese et al., 2012 [71,72]LAVI ↑
Luong et al., 2015 [74]RA volume ↑ > LAVI ↑
Fornengo et al., 2015, Toufan et al., 2017 [75,76]LA dilation
Marques-Alves et al., 2020 [73]Mitral valve disease
↑: increase. ↓: decrease.
Table 4. Correlation between P-wave parameters and DCCV recurrence.
Table 4. Correlation between P-wave parameters and DCCV recurrence.
Author and YearAFnFollow-upECGParameterRecurrence ChangeRecurrence Cut-off
Opolski et al., 1997 [78]PersAF356 monthsSAECGPWD↑>137 ms
Stafford et al., 1998 [81]PersAF (77%)311 weekSAECGP wave energy↑25%> drop
Aytemir et al., 1999 [82]PersAF736 monthsSAECGFiltered PWD↑>128 ms
Raitt et al., 2000 [83]PersAF201 yearSAECGPWD↑>130-140 ms
Guo et al., 2003 [85]PersAF606 monthsSAECGFiltered PWD↑Nil
Ehrlich et al., 2003 [86]No mention1111 weekSAECGPWD↑>145 ms
Dixen et al., 2004 [84]PersAF1311 monthSAECGPWD↑>160 ms
Dogan et al., 2004 [80]PersAF (45%)646 monthsSAECGPWDisp↑>46 ms
Perzanowski et al., 2005 [87]PersAF456 monthsSAECGPWDisp↑>80 ms
Budeus et al., 2005 [88]PersAF1411 yearSAECGPWD↑>126 ms
Başar et al., 2011 [89]PersAF261 year12 leadsPWDisp↑
Gonna et al., 2014 [90]PersAF771 month12 leadsPWD↑>125 ms
Blanche et al., 2014 [91]PersAF1339 monthsSAECGNilNilNil
Fujimoto et al., 2018 [92]PersAF1411 month12 leadsPWDisp↑Nil
Choi et al., 2021 [79]PersAF2722 months12 leadsPWD, PTFV1↑>134 ms, >50 ms.mm
Antoun et al., 2024 [7]PersAF5212 monthsBody surface mappingPWD↑>161 ms
PersAF: persistent atrial fibrillation. PWD: p wave duration. PWDisp: p wave dispersion. PTFV1: p wave terminal force in V1. SAECG: signal averaged electrocardiogram. Nil: nothing. ↑: increase. ↓: decrease.
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Antoun, I.; Layton, G.R.; Abdelrazik, A.; Eldesouky, M.; Altoukhy, S.; Zakkar, M.; Somani, R.; Ng, G.A. Predicting the Outcomes of External Direct Current Cardioversion for Atrial Fibrillation: A Narrative Review of Current Evidence. J. Cardiovasc. Dev. Dis. 2025, 12, 168. https://doi.org/10.3390/jcdd12050168

AMA Style

Antoun I, Layton GR, Abdelrazik A, Eldesouky M, Altoukhy S, Zakkar M, Somani R, Ng GA. Predicting the Outcomes of External Direct Current Cardioversion for Atrial Fibrillation: A Narrative Review of Current Evidence. Journal of Cardiovascular Development and Disease. 2025; 12(5):168. https://doi.org/10.3390/jcdd12050168

Chicago/Turabian Style

Antoun, Ibrahim, Georgia R. Layton, Ahmed Abdelrazik, Mahmoud Eldesouky, Sherif Altoukhy, Mustafa Zakkar, Riyaz Somani, and G. André Ng. 2025. "Predicting the Outcomes of External Direct Current Cardioversion for Atrial Fibrillation: A Narrative Review of Current Evidence" Journal of Cardiovascular Development and Disease 12, no. 5: 168. https://doi.org/10.3390/jcdd12050168

APA Style

Antoun, I., Layton, G. R., Abdelrazik, A., Eldesouky, M., Altoukhy, S., Zakkar, M., Somani, R., & Ng, G. A. (2025). Predicting the Outcomes of External Direct Current Cardioversion for Atrial Fibrillation: A Narrative Review of Current Evidence. Journal of Cardiovascular Development and Disease, 12(5), 168. https://doi.org/10.3390/jcdd12050168

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