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Electrical Remodeling in Atrial Fibrillation
From Experimental Findings to its
Non-invasive Quantification Using Frequency
Analysis of the Surface ECG

Andreas Bollmann, MD

Department of Cardiology, University Hospital Magdeburg,
Otto-von-Guericke University, Magdeburg, Germany

   Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice (65). Epidemiologic studies have shown that its prevalence and incidence doubles with each advancing decade beyond 50 years reaching 10% in octogenarians (44,47). AF is not only related with frequent symptoms, but also increases morbidity and mortality. Several studies have clearly demonstrated that AF constitutes a major risk factor for stroke (20,96,97). Some studies also indicate that AF confers an excess risk of mortality from cardiovascular and all causes (8,32,44).

   AF related symptoms and morbidity are moreover responsible for frequent physician visits and hospitalizations leading to high cost, that are markedly greater than for any other arrhythmia (9).

   Currently, the treatment of AF may be viewed as being based on trial and error, since no test is able to predict the natural history of this arrhythmia or its response to treatment. The most common parameter used to guide therapy is left atrial size even though its role for prediction of outcome following cardioversión is controversial. While some investigators have found higher AF recurrence rates in patients with left atrial enlargement (34,37,89) others have not (2,26,52,59,88).

   P-wave signal averaging has been explored as a means of estimating the risk of future AF episodes in patients with paroxysmal AF or after successful cardioversion of persistent AF (6,66,82,98). Although the sensitivity and specificity for prediction of AF recurrence was in the range between 70 and 80 % this technique can only be applied when the patient is in sinus rhythm. Therefore it is of no value for determining both the spontaneous behavior of the arrhythmia and the response to chemical or electrical cardioversion.

   As the number of therapeutic options including new antiarrhythmic drugs or non-pharmacologically approaches (ablation, implantable atrial defibrillator, pacing) increases, there is a clear need for tests that will quantify AF severity and guide AF management.

CHANGES IN ATRIAL ELECTROPHYSIOLOGY DURING AF
   The most widely accepted theory of AF mechanisms was proposed by Moe (56) as early as 1962. It postulated that AF perpetuation is based on the continuous propagation of multiple wavelets wandering throughout the atria. In 1985, mapping of experimentally induced AF in canine hearts 1 provided the first evidence supporting Moe's multiple wavelet hypothesis.

   The average size of reentry pathways during AF is dependent on atrial wavelength, defined as the product of conduction velocity and refractory period (92). Long wavelengths are associated with larger and fewer wavefronts while short wavelengths result in a greater number of smaller circuits (1,56,69,81).

   Over the past few years, several groups have developed new animal models of AF in order to assess structural, functional and electrophysiological requirements for AF induction and sustenance. One common finding was a progressive shortening of atrial refractory periods (AERP) and a maladaptation to rate in models involving chronic rapid pacing (58) or electrically maintained AF (94,95).

   The findings obtained in human studies are in close agreement with those obtained in experimental AF models. Several groups (24,25,51,99) have invasively studied the effects of AF on atrial refractoriness.

   Daoud et al. have investigated ERP changes in pacing-induced AF. They found a significant ERP shortening (up to 20%) after a few minutes (24,25). Similar observations have been reported by Yu and co-workers (99). The authors additionally showed that ERP shortening is a rate-dependent process with shorter cycle length leading to a greater decrease in AERP. Similarly, induced AF of about 10 minutes duration reduced the AERP by 15%. This was significantly more ERP shortening than that observed after atrial pacing with a cycle length of only 250 ms (4 Hz). Unfortunately, the authors did not provide data on AF cycle length of their induced episodes but from previous investigations (3,23) it can be assumed that the cycle length is less than 250 ms. This in turn is further support for the rate dependency of ERP shortening.

   While the former groups studied subjects without heart disease, Lubinski and colleagues evaluated electrical atrial remodeling in patients with structural heart disease (51). The majority of their 13 patients had coronary artery disease and some of them had depressed systolic left ventricular function. Serial measurements of right atrial ERPs and monophasic action potentials (MAP) were obtained after at least 10 minutes of induced AF and/or fast atrial pacing. The authors found a shortening of both AERP and MAP as soon as 3 minutes after AF induction with minimal values after 10 minutes.

   That MAP and ERP shortening is an essential feature in AF is further supported by several studies (18,22,29,43,48,53,100) measuring these parameters in AF patients after cardioversion and comparing them with normal controls. Controversy exists, however, whether or not AERP maladaptation to rate is present in human AF (61).

   Kumagai et al. (48) studied several atrial electrophysiological properties in 12 patients with chronic (more than 1 year) lone AF 24 hours after successful electrical cardioversion. In their study, ERP measured at the HRA was significantly shorter in AF patients than in the 12 controls. Similarly, Franz and co-workers (29) found shorter MAPs at two right atrial sites in 7 patients with chronic (> 3 weeks) AF 15 to 30 minutes after electrical cardioversion compared with 9 control subjects. This change showed a preponderance at longer pacing cycle lengths supporting the observation in animals (94) that ERP loses its ability to adapt to rate. Based on a study in 19 patients with chronic (> 6 months) AF and 20 age-matched controls Yu and colleagues also concluded that AF significantly shortens the AERP and impairs its rate adaptation response (100). Additionally, Kamalvand and associates (43) noted in 13 chronic (> 1 year) AF patients that the 'normal' pattern of atrial refractory dispersion with longer ERP at the right atrial appendage compared with the midlateral right atrial wall is lost. In this study, right atrial appendage MAP duration shortened with decreasing cycle length in both AF and normals, and did not show normal rate adaptation at the mid-lateral right atrium. Protein levels for the L-type Ca2+ channel and several potassium channels have been found to correlate positively with AERP and its rate adaptation in both paroxysmal and persistent AF. Patients having reduced ion channel expression had also a shorter AERP and poorer rate adaptation (22).

   In contrast, normal or nearly normal adaptation to rate immediately after cardioversion of persistent AF was reported in 77% of the paced atrial sites by Pandozi and colleagues (61). Based on their findings, this group questionnes if the reduced adaptation found in other studies is due to the presence of different basal atrial electrophysiological properties in patients who will develop AF, or to the high atrial rate during AF.

   Interestingly, AERP changes have been shown to be of reversible nature (53,86,100) what would support, that they are the result of the high rate. Normalization of both ERP shortening and maladaptation was observed as early as 12 to 24 hours following cardioversion (53,86). In those patients in whom maladaptation persisted, the likelihood for an early AF relapse was higher (53) which supports an early study by Attuel et al. (4) showing, in turn, that poor or absent rate adaptation of AERP is related with vulnerability to AF.

AF CLASSIFICATIONS BASED ON ELECTROPHYSIOLOGICAL PROPERTIES
   A commonly used AF classification uses fibrillatory wave amplitude recorded on the surface ECG, usually found in lead V1 (64). An arbitrary threshold of 0.1 mV subdivides AF into two categories: coarse (> 0.1 mV) and fine (< 0.1 mV). The clinical utility of this division seems very limited. Echocardiographic studies have investigated the relation between the left atrial diameter or LAA flow velocity and fibrillatory amplitude during AF. The results of these studies are conflicting. While some investigators (5,7) have found larger fibrillatory waves in patients with left atrial enlargement, others (11,50,57), have not. Controversy also exists for the relation between fibrillatory wave amplitude and LAA flow velocity. Li et al. (50) have studied 78 patients with AF using transesophageal echocardiography. They found that patients with coarse AF had a lower LAA velocity and subsequently higher rates of spontaneous echo contrast as well as thrombus formation. In contrast, Blackshear et al. (11) demonstrated no correlation between fibrillatory wave size and LAA flow velocity and subsequent thromboembolic risk in 53 patients enrolled in the SPAF-III trial.

   More recent studies have attempted to classify AF based on the morphology or complexity of intracardiac electrograms.

   Wells et al. used a single bipolar atrial electrogram recorded after cardiac surgery to stratify the arrhythmia (91). Discrete signals with intervening isoelectric intervals were defined as Type I. Type II fibrillation had discrete activation without periods of complete electrical quiescence in between. Type III was disorganized with no clear isoelectric intervals. Type IV was characterized as alternation between the Type I and Type III pattern. Besides of being only descriptive, this classification has two further limitations. As acknowledged by Wells himself, the four AF types were not stable over time. Another limitation of assessments based on focal or regional activation patterns was shown by Li et al. (49). They noted that all four patterns of AF described by Wells et al. were present concurrently at different locations during experimental AF.

   Konings and associates classified AF in patients with the Wolff-Parkinson-White syndrome undergoing cardiac surgery by using high-density mapping of the right atrial free wall (46). They categorized the arrhythmia based on the number and complexity of activation wavefronts within the 3.6 cm diameter region being studied. Type I AF was characterized by a single uniform wavefront, Type II AF showed one nonuniform or two simultaneous wavefronts and in a more complex type III AF multiple wavefronts were present simultaneously. Their analysis was performed in induced AF episodes and was limited to 4-second ECG recordings.

   Holm and co-workers, by applying high resolution epicardial mapping of the right atrial free wall to 16 patients with persistent AF, have identified three different atrial activation patterns (38). They classified activation as: (1) unorganized activation with several simultaneously present activation waves (inconsistent pattern), (2) predominantly organized activation with either frequent episodes of uniform activation or frequent episodes of activation with focal spread (consistent pattern) and (3) focal preferable activation.

   Although all these studies have provided further insight into the electrophysiological mechanism underlying AF its clinical utility is limited by the need for invasive recordings, partially obtained during cardiac surgery and requiring highly sophisticated data analysis. Furthermore, another limitation has been pointed out by Gallagher and Camm (33). They correctly noted that any attempt to divide a continuous variable such as the complexity of intracardiac signals into discontinuous categories must be arbitrary.

   One alternative based on the cross-correlation of two endocardial signals has been suggested by Botteron and Smith (19). They calculated a so-called 'activation space constant' from five bipolar right atrial recordings in order to quantify the extend of spatial organization of AF activation by one single, objective measure. Other groups introduced several indices of AF organization such as the coherence spectrum (72) or the mean-squared error method (77).

RATIONALE FOR USING FIBRILLATORY FREQUENCY TO CHARACTERIZE HUMAN AF
   As indicated, ERP and conduction velocity determine atrial wavelength. Previous experimental and clinical investigations have proposed a close inverse correlation between atrial refractory period and the frequency of electrical atrial activation (23,45,68). The aforementioned mapping studies have found a clear correlation between fibrillatory frequency (atrial cycle length) and the complexity of the arrhythmia. Konings' Type I AF, characterized by a single uniform wavefront, exhibited a mean frequency of 5.7 Hz (174 ms); Type II AF, defined as showing one nonuniform or two simultaneous wavefronts and even more complex type III fibrillatory frequency, measured 6.6 Hz (150 ms) and 7.4 Hz (136 ms), respectively (46). Similarly, Holm's mapping study revealed a significantly higher median cycle length during the consistent activation compared to the inconsistent activation, while the conduction velocity was similar among the different AF patterns (38).

   In two early studies (73,79) it was proposed to quantify fibrillatory frequency from the surface ECG by means of Fourier analysis. While Rosenbaum and Cohen used this technique to quantitatively subdivide AF and atrial flutter (73), Slocum and colleagues automatically identified AF among different rhythms from the surface ECG (79).

SIGNAL PROCESSING USED TO DETERMINE FIBRILLATORY FREQUENCY FROM THE SURFACE ECG
   Frequency analysis of the surface ECG involves filtering, subtraction of the QRST, and Fourier transformation. This method including its validation and its possible utility was described in detail in two independent papers (13,39).

   Since the atrial and ventricular activities overlap spectrally, linear filtering techniques are not suitable for extraction of the fibrillatory signal from the surface ECG. Instead, subtraction of averaged QRST complexes needs to be performed.

   After high-pass filtering (4th order Butterworth filter, 0.5 Hz cut-off), QRST complexes are subtracted using a template matching and averaging algorithm. Each R wave is automatically identified using a peak detection algorithm. QRST intervals are centered around this fiducial point, averaged in order to create a template, and then aligned with each QRST in the sample and subtracted.

   The final step of the process involves frequency analysis of this derived signal. The fibrillatory baseline signal is filtered (8th order Chebyshev filter, 0.5 to 80 Hz frequency range) and reduced to 200 Hz. In order to prevent the onset and offset of the signal from introducing artifact, a 512 points Hanning window with 128 points overlap is applied. This signal is then subjected to a 2048-point discrete Fourier transformation and displayed as a power spectrum by calculating the squared magnitude of each sample frequency. Peak frequency is determined in the 3 to 12 Hz range (Figure 1).

Figure 1. Signal processing technique used to determine peak fibrillatory frequency of surface ECG lead V1. Top panel: ECG segments with AF. Middle panel: Typical appearance of the signal after filtering and subtraction of QRST complexes using a template matching and averaging algorithm. Bottom panel: Frequency power spectrum produced by Fourier transformation of the signal in the middle panel. Most of the power is concentrated in a single narrow peak in both examples. Patient A (left panel) had a fibrillatory frequency of 5.8 Hz as opposed to 7.3 Hz in Patient B (right panel).

   Reliable and accurate identification of the atrial frequency spectrum is dependent on the suppression of the QRST complexes. The template and subtraction technique currently used does not account for rate related changes in the QT interval, possible U waves or changes in the electrical axes due to breathing. This may result in energy from other signals contributing to the processed signal. More sophisticated signal processing techniques such as the 'Spatiotemporal QRST Cancellation Technique' (85) or a separate subtraction of QRS and T wave templates (35) may improve the performance of this test even further.

FREQUENCY SPECTRUM IN AF
   The vast majority of ECG recordings analyzed in previous investigations (13,15,17,39,63,79,80) have exhibited single-narrow banded frequency spectra when applying the described signal processing. Spectral decomposition of bipolar electrograms during experimental AF in sheep heart has revealed that the dominant frequency may result from repetitive activations by a reentrant source such as a single focal source within the pulmonary veins (41). The lower frequency components may represent various degrees of spatiotemporal organization of waves propagating from that source to the rest of the atria (42,78). Atrial anatomy and conduction properties may dictate to what ratio the rest of both atria can follow the primary source.

   Fibrillatory frequency in human AF determined from the surface ECG exhibits a marked interindividual variability ranging between 4 and 9 Hz corresponding with a cycle length of 110 to 250 ms (13,15,17,39,63,79,80) which is in agreement to the values obtained from intraatrial recordings in previous investigations (3,23,31,36,46).

   Only few studies have directly compared frequency spectra from intracardiac electrograms with the surface ECG (13,39,70,80). There is agreement that the mean frequency of right atrial activation is represented in fibrillatory waves recorded in the surface ECG, mainly lead V1 (13,39,70,80).

   Fibrillatory frequency extracted from esophageal leads by applying similar data processing techniques may in contrast closer reflect atrial septal and left atrial activity (63) depending on the individual anatomy (39).

   The gross atrial fibrillatory pattern, studied by multiple simultaneous endocardial (31) or epicardial recordings (38) is reproducible already in repeated measurements of several seconds. While there is some short-term variability in fibrillatory frequency obtained from the surface ECG (13,39) and considerable diurnal variability (14,17,55), repeated daily frequency measurements at identical medication at the same time under similar conditions discloses an insignificant frequency variability (54).

FIBRILLATORY FREQUENCY AND AF BEHAVIOR
   Several investigators have analyzed the relation between (invasively assessed) fibrillatory frequency and AF perpetuation or termination.

   The spontaneous behavior of the arrhythmia is related with the baseline fibrillatory frequency. Asano et al. induced AF with rapid pacing in 30 patients undergoing electrophysiologic study (3). Those patients where AF terminated spontaneously (n=20) had an average fibrillatory frequency of 5.7 Hz (176 ms), significantly lower than the 6.4 Hz (157 ms) recorded in the group of patients where the arrhythmia persisted (n=10). Similar observations were made by Boahene et al. who also measured the fibrillatory cycle length from the right atrium in 55 patients with Wolff-Parkinson-White syndrome (12). These investigators also found that patients with sustained AF (n=45) had shorter mean cycle lengths compared to patients with non-sustained AF (n=10).

   As already indicated, in both experimental models of AF and in clinical studies, persistent rapid atrial rates have been shown to produce a marked, progressive AERP shortening. The decrease in refractoriness is accompanied by a comparable increase in the fibrillatory frequency in the goat model (94). Similar results were obtained in humans. Capucci et al. induced AF in 25 patients with a history of paroxysmal AF (23). The authors found a different behavior of atrial rates between sustained and self-terminating AF episodes. In both studies long-lasting fibrillation was associated with an increase in fibrillatory frequency within 5 minutes after AF onset.

   In contrast, spontaneous AF termination is accompanied by a decrease in fibrillatory frequency. Both Asano et al. (3) and Capucci et al. (23) noted a similar behavior of atrial activation prior AF termination. In both studies evaluating induced AF episodes fibrillatory frequency was around 5.5 Hz (180 ms) at baseline and 5 Hz (200 ms) prior termination. Similarly, Sih et al. monitored atrial rate in 4.27 sec segments in 7 induced AF episodes (76). The authors reported a significant decrease in mean atrial rate. However, termination in 3 out of 7 episodes was accompanied by a rate increase. The authors provided only limited information on AF duration (4 episodes were shorter than 4.8 minutes). Therefore, a frequency decrease is not an universal precursor to AF termination. However, since baseline frequency was already low in short episodes, there might be a critical frequency below which termination occurs. These data are in close agreement with one single study exploring fibrillatory frequency from Holter ECG recordings in paroxysmal human AF (15). AF episodes that persisted for longer than 15 minutes had a baseline fibrillatory frequency of 5.3 Hz, significantly higher than the 4.8 Hz found in shorter episodes. A frequency increase was observed in long-lasting AF episodes after 5 minutes, whereas no significant frequency change occurred in shorter AF episodes. Prior spontaneous AF termination fibrillatory frequency decreased (Figure 2).

Figure 2. Temporal pattern of fibrillatory frequency in spontaneous paroxysmal AF. Top panel: AF episodes lasting less than 15 minutes. Botton panel: Longer-lasting AF episodes. Note the lower fibrillatory frequency of short AF episodes. In those, frequency was constant, whereas in longer-lasting AF episodes fibrillatory frequency increased within 5 minutes after AF onset (O) and decreased prior termination (T).

   Progressive atrial electrical remodeling seems to be responsible for the transition from paroxysmal to persistent AF. Atrial electrophysiologic properties were studied in paroxysmal (n=8) and chronic (n=11) human AF and compared with control subjects (n=10) by Tse et al. (87). The authors observed heterogeneous changes in different atrial parts with the normal spatial distribution of AERPs totally reversed in chronic AF, but preserved in paroxysmal AF. These observations are in accordance with findings from Botteron and Smith (19). These authors, by calculating the aforementioned activation space constant (ASC) in 20 patients, illustrated that the spatial organization was smaller in patients with chronic AF (n=5, ASC=1.84±0.36 cm), whereas the group of patients with no AF history exhibited the highest degree of AF organization (n=3, ASC=3.06±0.4 cm). An intermediate value was observed in patients with paroxysmal AF (n=12, ASC=2.8±1.4 cm). Similarly, there was a fibrillatory frequency increase from paroxysmal (5.1±0.7 Hz) to chronic AF (6.9±0.5 Hz) when estimating fibrillatory frequency from the surface ECG (15).

   Conversely, the reversal of AERP (cycle length) changes have been found to depend on the duration of sinus rhythm after cardioversion. Hobbs and colleagues (36) measured right (161±12 ms) and left atrial (167±26 ms) cycle lengths prior electrical cardioversion. In 17 patients AF recurred after a mean time of 129 hours (range 1 to 740 hours). At this time, AF cycle length had increased to 167±26 ms and 168±22 ms, respectively, with a positive correlation between the cycle length increase and sinus rhythm duration.

   These findings suggest that fibrillatory frequency may be useful for quantifying the magnitude of remodeling that has occurred in patients with AF.

PREVENTION OF ELECTRICAL REMODELING BY VERAPAMIL
   The mechanisms behind electrical remodeling have been attributed at least partially to intracellular calcium loading (25,99). More recently, calcium channel antagonists have been suggested to prevent an AERP shortening and subsequently reduce the atrial electrical remodeling process in humans (25,99) when given prior to the AF onset. In a study performed by Daoud and colleagues (25) AF was induced in 47 patients. In 17 patients with verapamil pretreatment no AERP shortening was found during AF whereas in patients pretreated with procainamide or saline infusion a 20 % AERP reduction occurred. Furthermore, the AF reinduction rate was markedly lower in patients with verapamil pretreatment. Similar results were obtained by Yu and co-workers (99) who treated 60 patients with 6 different antiarrhythmic drugs including verapamil before AF induction. They found AERP shortening to be a rate-dependent process with verapamil, but not other antiarrhythmic drugs markedly attenuating this effect.

   Different results were obtained, however, when oral verapamil was given to patients with persistent AF. Either no effect on atrial refractory periods but a delayed recovery from electrical remodeling (74) or shorter AERPs in verapamil treated patients were noted (60). It was speculated that early verapamil administration prevents refractory period shortening by preventing intracellular calcium overload which is known to reduce the L-type Ca2+ current. In contrast, a later administration, at a time when electrical remodeling has already occurred, can not reverse the calcium overload. This in turn, and the additional calcium channel blockade reduce the L-type Ca2+ current further thereby leading to a shorter action potential duration and subsequently shorter refractoriness (60).

     Our group has found a lower fibrillatory frequency in patients taking long-term oral verapamil when compared to patients without this drug (Figure 3) (16). Similarly, Meurling et al. by using a comparable ECG processing technique observed a significant reduction in fibrillatory frequency (maximum after 5 days with steady state thereafter) in 10 patients with persistent AF after oral verapamil administration (54). While there was a significant day - night difference in fibrillatory frequency in our verapamil treated group, it was absent in patients without verapamil (Figure 3). This finding is supported by another study from Meurling et al. (55), who noted no diurnal frequency fluctuation in patients with a high mean frequency (comparable to our untreated patients), as opposed to a significant nightly decrease in patients with a lower mean frequency (comparable to our treated patients).

Figure 3. Fibrillatory frequency in patients with long-term verapamil treatment (n=27) compared to patients without (n=30) this treatment (top panel). Circadian fibrillatory frequency variation in both groups (botton panel).

   The lower frequency found in these studies after long-term oral verapamil is in contrast, to an increase in both atrial rate and dispersion of atrial refractoriness immediately after a 10 minute verapamil infusion (67). In this study performed by Ramanna and colleagues (67) in 15 patients with chronic AF (> 1 year) no measurements were obtained, however, after a longer treatment period (e.g. after 1 or 7 days). An acute frequency increase may therefore not preclude a chronic frequency decrease. Differences in the study protocols may contribute to the different findings. For instance, the observed increase in fibrillatory frequency after acute verapamil infusion (67) might be mediated by an increased sympathetic tone following hemodynamic alterations (30). From previous investigations it is known that this autonomic change contributes to higher atrial rates (14,40).

   Furthermore, verapamil could suppress pulmonary vein foci and/or excert favourable long-term effects on the reversal of morphological remodeling (62).

EFFECTS OF ANTIARRHYTHMIC DRUGS ON FIBRILLATORY FREQUENCY
   Several studies have investigated the impact of acute antiarrhythmic drug administration on atrial rates in human AF. Procainamide (12,71,76,83), propafenone (10,12), disopyramide (3), flecainide (10), cibenzoline (21), sotalol (39) and ibutilide (13,83) have all been found to reduce the average frequency of fibrillatory activity. These findings are in concordance with one single study (17) analyzing the effects of orally administered antiarrhythmic drugs in 12 patients by applying the introduced technique (Figure 4). More recently, our group has found that short-term (3 days) oral flecainide (200 mg/d) leads to a greater frequency reduction (1.8±0.6 Hz vs. 1.0±0.6 Hz) than oral amiodarone (1200 mg/d) (Bollmann, unpublished data).

Figure 4. Fibrillatory frequency obtained from the ECG before and after antiarrhythmic (AA) drug administration (top panel). Individual response. Amio = Amiodarone, Flec = Flecainide, Sot = Sotalol (botton panel).

   Recently, ibutilide, a new intravenous, selective class III antiarrhythmic agent that prolongs action potential duration and refractoriness, has been evaluated for rapid termination of sustained AF in several trials (27,28,84). Ellenbogen et al. reported an overall conversion rate of atrial fibrillation and atrial flutter to sinus rhythm of 29% and 38%, respectively (27,28). Although these investigators found an association between arrhythmia duration and success, no subset showed a response rate above 42%.

   The baseline fibrillatory rate recorded directly from the atrial endocardium was however, predictive of conversion to sinus rhythm with ibutilide (83). Stambler and colleagues identified a mean right atrial cycle length of 160 ms (6.25 Hz) as valuable cutoff-point. No patient with shorter cycle lengths was converted by ibutilide, whereas conversion occurred in 64% of those with longer cycle lengths (lower frequency). These results are in close agreement with findings from a similar study using frequency analysis of the surface ECG (13). A cutoff of 6 Hz (166 ms) was 72% sensitive and 100% specific for drug-induced AF termination (Figure 5).

Figure 5. Fibrillatory frequency prior ibutilide infusion in converters and non-converters. Note that 2 converters had the same baseline fibrillatory frequency.

   Class I and III drugs are believed to increase atrial wavelength in experimental (75,90) and human AF (3) leading to a reduction in the number of reentry circuits.

   More recently, it was suggested, however, that several class I and III antiarrhythmic agents may exert their antifibrillatory actions by a conduction delay at pivot points causing also an increase in fibrillatory frequency and a widening of the excitable gap with fewer wavelets (93).

   Patients with a low fibrillatory frequency may have a small number of wavelets (long wavelength), whereas those with higher frequencies have multiple wavelets (short wavelength). In the former group ibutilide by decreasing fibrillatory frequency may have increased wavelength (or the excitable gap) and therefore reduced the number of wavelets that could coexist. This would have increased the statistical chance that all wavelets might extinguish simultaneously and terminate the fibrillatory process (90). In contrast, in patients with a high fibrillatory frequency, although ibutilide decreased fibrillatory frequency, atrial wavelength may not have prolonged beyond a critical length allowing subsequent AF termination.

   Given the expense of antiarrhythmic therapy and the risk of side effects including proarrhythmia, a test that differentiates responders from non-responders is likely to be cost-effective.

CONCLUSIONS
In human AF, atrial fibrillatory frequency exhibits a marked interindividual variability. Low frequency fibrillation is more likely to terminate spontaneously or to respond to antiarrhythmic therapy, while high frequency fibrillation is more often persistent and drug refractory. The ECG signal processing technique described allows the average frequency of fibrillatory activity to be quantified using the surface ECG. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in the genesis and perpetuation of AF. The influence of pharmaceutical interventions can be monitored directly. Thus, determination of fibrillatory frequency from the surface ECG may prove useful for non-invasive assessment of the electrophysiologic state of the atria in patients with AF. Further studies are necessary to determine the role of this test in the management of AF.

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2nd Virtual Congress of Cardiology

Dr. Florencio Garófalo
Steering Committee
President
Dr. Raúl Bretal
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President
Dr. Armando Pacher
Technical Committee - CETIFAC
President
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rbretal@fac.org.ar
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