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Journal of the American College of Cardiology Ó 2014 by the American College of Cardiology Foundation Published by Elsevier Inc. EDITORIAL COMMENT Differentiating Paroxysmal From Persistent Atrial Fibrillation Long-Term Electrocardiographic Monitoring Is Mightier Than the Clinician* Suneet Mittal, MD Ridgewood, New Jersey Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice. In caring for a patient with AF, a clinician must establish the pattern of arrhythmia, determine associated symptoms, and assess for underlying comorbidities in order to define a short- and long-term management strategy. Current clinical guidelines advocate differentiating between paroxysmal, persistent, long-standing persistent, and permanent patterns of AF (1). Although it is recognized that these categories are not mutually exclusive, these guidelines suggest that it is practical to categorize a given patient by his or her most frequent pattern of presentation. See page 2840 An implicit premise is that a clinician can accurately determine a patient’s pattern of AF, presumably through a combination of history taking and available electrocardiographic (ECG) information. However, in this issue of the Journal, Charitos et al. (2) provide compelling evidence to challenge this premise. They evaluated 1,195 patients (from the OMNI and TRENDS studies) (3,4) with a history of nonpermanent AF who were undergoing implantation of a pacemaker or defibrillator system (from a single manufacturer) that incorporated an atrial lead. Just prior to device implantation, the patient’s clinician was asked to classify whether their patient had paroxysmal or persistent AF. The patients were then followed to a maximum of 365 days (minimum follow-up 180 days). Patients who underwent a *Editorials published in the Journal of the American College of Cardiology reflect the views of the authors and do not necessarily represent the views of JACC or the American College of Cardiology. From the Electrophysiology Laboratory, Arrhythmia Institute of the Valley Hospital Health System, Ridgewood, New Jersey. Dr. Mittal is a consultant to Biotronik, Boston Scientific, Medtronic, Scottcare, and St. Jude Medical; and has received a research grant from Biosense Webster. Vol. 63, No. 25, 2014 ISSN 0735-1097/$36.00 http://dx.doi.org/10.1016/j.jacc.2014.04.020 specific intervention for AF (e.g., cardioversion, catheter ablation) during follow-up were subsequently excluded. The clinician-defined pattern of AF was compared to device-derived classification, which served as the gold standard. Specifically, based on device data, patients were stratified as having no AF (no day with >5 min of AF), paroxysmal AF (at least 1 day of AF but <7 consecutive days with >23 h of AF), persistent AF (at least 7 consecutive days with >23 h of AF), and permanent AF (all days with >23 h of AF). In addition, the authors determined: 1) the burden of AF, defined as the proportion of the monitored time a patient was in AF; and 2) the density of AF, defined as the temporal aggregation of AF burden. The latter is represented by an index that can range from 0 (low density; AF is distributed evenly over the monitored period) to 1 (high density; a continuous single episode of AF). Several important findings emerged from this study. First, although the study only included patients with a known history of AF and excluded patients with permanent AF, 399 (33%) patients had no AF whatsoever and 36 (3%) patients had continuous AF during follow-up. Second, although patients with persistent AF had a higher burden of AF than patients with paroxysmal AF, there was large overlap in the distribution of AF burden across patients within the 2 (physician-defined) AF classifications. Some patients with very low burden of AF were classified as having persistent AF, whereas some patients with very high burden of AF were classified as having paroxysmal AF. Third, quite surprisingly, a higher left ventricular ejection fraction and the presence of coronary disease were independently associated with a lower probability of a patient being classified as having persistent AF by their physician for the same underlying AF burden. Fourth, device-derived AF data more accurately reflected the temporal persistence of AF and were not influenced by patient characteristics. Ultimately, the correlation between clinical and device-derived classification of AF temporal persistence was quite poor. The temporal persistence of AF is used to connote progression of disease. Thus, it is determined in each patient in order to “group” patients (presumably into a similar stage of disease) and guide decisions regarding management, including the appropriateness and strategy for catheter ablation (1,5). In paroxysmal AF, triggers (typically from the pulmonary veins) are thought to be responsible for initiating AF and thus the pulmonary veins are usually targeted for ablation; recent guidelines impart a Class I indication for catheter ablation in patients with symptomatic AF refractory or intolerant to at least 1 Class I or III antiarrhythmic medication when a rhythm control strategy is desired (1). In contrast, as AF progresses to a persistent form, it is thought that, in addition to the triggers, the substrate becomes important and likely also needs be targeted for ablation (5–7). The same guidelines impart a Class IIa indication for catheter ablation in patients with persistent AF and a Class IIb indication for ablation in patients with longstanding persistent AF (1). 2850 JACC Vol. 63, No. 25, 2014 July 1, 2014:2849–51 Mittal Paroxysmal Versus Persistent Atrial Fibrillation Recent data suggest that atrial fibrosis, a marker of the underlying substrate, can be visualized using cardiac magnetic resonance imaging and used to individualize the strategy for catheter ablation (8–10). Interestingly, similar to the findings reported by Charitos et al. (2), clinician derived assessment of temporal persistence of AF correlates poorly with the location and extent of atrial fibrosis (8). This may explain, in part, why some patients with presumed paroxysmal AF respond poorly whereas others with presumed persistent AF respond favorably to pulmonary vein isolation alone. In the future, it would be of great interest to correlate the relationship between more objective data (i.e., devicederived) about the temporal persistence of AF with cardiac magnetic resonance imaging findings. When evaluating a patient with AF, there are a number of parameters that can be assessed. These include the frequency of episodes, the duration of the longest episode, and the cumulative burden of episodes. To date, greatest emphasis has been placed on the longest duration of an individual episode. This forms the basis of our definitions of paroxysmal, persistent, and long-standing persistent AF, which have been used as markers of disease progression. In addition, the duration of individual episodes has been used to assess a patient’s risk of thromboembolism (4,11). Charitos et al. (2) show us the limitations of this approach. First, there was a large overlap in AF burden between patients clinically classified as paroxysmal versus persistent AF. Second, for incompletely understood reasons, at equivalent burdens of AF, the clinical classification of paroxysmal versus persistent AF was heavily influenced by patient characteristics. In contrast, when using data derived through continuous long-term ECG recordings, there was a clear and well-demarcated increase in AF burden as patients progressed from having no AF to paroxysmal AF (mean burden 3%) to persistent AF (mean burden 39%) to permanent AF (mean burden 99%), which appears more reflective of disease progression. In this study, an implanted atrial lead was used to derive device based ECG data. However, this is not feasible in the vast majority of AF patients. Available options for obtaining ECG data in most patients range from a variety of available ambulatory external ECG recording systems to implantable loop recorders (12,13). Charitos et al. (14) have previously offered us important insights into the optimal frequency and method of ECG monitoring. In particular, the duration of monitoring is fundamentally dependent on the patient’s burden and density of AF. Unfortunately, neither of these parameters is known nor can be predicted in any given patient. Patients with high-density AF require more frequent ECG monitoring to achieve higher AF detection sensitivity than patients with low density AF, especially when the AF burden is <40% (14). However, the frequency and duration of external ECG monitoring necessary to optimize diagnostic yield is impractical due to difficulty with patient compliance and restrictive coverage policies of insurance providers. There are 2 promising options to address the long-term ECG needs of this patient population. The first option is to consider implantation of a stand-alone loop recorder for long-term ECG monitoring. These devices, which have recently become miniaturized to the point of being potentially implantable in an office setting, have dedicated algorithms that determine the presence of absence of AF and can provide information (wirelessly and remotely) about the longest episode and burden of AF (15). The other is to continue our investigation of “smartphone” based ECG applications, which have proven feasible as a tool for community wide screening of AF (16). A daily check of the ECG may provide a reasonable qualitative assessment of the AF burden and allow for more precise clinical discrimination between paroxysmal and persistent patterns of disease. However, the reliance on patient compliance and inability to precisely quantify either the duration of longest AF episode and actual burden of AF may be important limitations. The current study is not without its limitations. Had the OMNI and TRENDS investigators known that this was an additional aim of these studies, they may have attempted to more accurately determine the temporal pattern of AF in their patients. In addition, it is possible that in some patients AF actually progressed during the 1-year observation period. Finally, patients undergoing implantation of a pacemaker or defibrillator with an atrial lead may not be representative of the overall universe of AF patients. Nonetheless, the authors are correct in stating that clinical decisions (rate vs. rhythm control; antiarrhythmic medications vs. catheter ablation; choice of lesion sets during ablation) are routinely being made based on clinical classifications of the temporal persistence of AF, which appear to have limited accuracy. Charitos and colleagues have again highlighted the importance and necessity of long-term ECG recordings to more objectively, and thus more accurately, classify the temporal persistence of AF in any given patient. Whether this can be adequately accomplished without an implantable system needs further investigation. Reprint requests and correspondence: Dr. Suneet Mittal, Electrophysiology Laboratory, The Valley Hospital, 223 North Van Dien Avenue, Ridgewood, New Jersey 07450. E-mail: mittsu@ valleyhealth.com. REFERENCES 1. January CT, Wann LS, Alpert JS, et al. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol 2014 Mar 28 [E-pub ahead of print]. 2. Charitos EI, Pürerfellner H, Glotzer TV, Ziegler PD. Clinical Classifications of atrial fibrillation poorly reflect its temporal persistence: Insights from 1,195 patients continuously monitored with implantable devices. J Am Coll Cardiol 2014;63:2840–8. 3. Sweeney MO, Sakaguchi S, Simons G, Machado C, Connett JE, Yang F, for the OMNI study investigators. 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Performance of a new leadless implantable cardiac monitor in detecting and quantifying atrial fibrillation: results of the XPECT trial. Circ Arrhythm Electrophysiol 2010;3:141–7. Lowres N, Neubeck L, Salkeld G, et al. Feasibility and cost effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies: the SEARCH-AF study. Thromb Haemost 2014;111:1167–76. Key Words: arrhythmia - atrial fibrillation - monitoring.