Relationship Between Local Atrial Fibrillation Interval and Refractory Period in the Isolated Canine Atrium
Background Atrial refractory periods and their spatial distribution are important determinants of atrial reentrant arrhythmias. The objective of this study was to demonstrate a correlation between the local atrial fibrillation interval (AFI) and local effective refractory period (ERP).
Methods and Results To measure the local ERP and local AFI under stable conditions without hemodynamic, autonomic, or reflex influences, isolated perfused canine whole atria were used (n=8). The isolated atria were mounted on two endocardial electrodes. Bipolar electrograms were simultaneously recorded from 253 endocardial sites, and 16 to 20 randomly distributed electrodes were used to measure the local ERP by the extrastimulus technique. In all studies, several episodes of AF were induced by a single extrastimulus. The ERP and minimum AFI converged with increasing duration of AF. The convergence was more rapid if the total duration of AF analyzed came from multiple episodes of AF. The correlation coefficient between the local ERP and minimum local AFI was .92 (n=119, P<.001). The minimum AFI was used to construct AFI distribution maps at all 253 sites. Activation block during premature stimulation correlated with regions of long AFI.
Conclusions The minimum local AFI measured from at least 10 seconds of AF approximates the local ERP. Construction of a minimum local AFI map during AF can be used to predict the distribution of refractoriness and can be used to predict sites of functional block. Contrary to studies done in intact animals and patients, the AFI were longer than the ERPs, suggesting that reflex changes may shorten ERP in the intact heart.
It is generally accepted that AF is caused by multiple and concurrently circulating reentrant circuits.1 2 3 Recordings of electrograms have demonstrated short and variable fibrillation intervals similar to ventricular fibrillation. Opthof et al4 suggested that during ventricular fibrillation, myocardial cells are reexcited as soon as their refractory period ends. They demonstrated by transmembrane potential recording that there was no diastolic interval between successive action potentials and that there was only a small difference in refractory periods during ventricular fibrillation. In contrast to ventricular fibrillation, larger variations in local fibrillation intervals have been observed during AF.5 Possible explanations for this have included either variation in local refractory periods or the presence of a significant excitable gap. Mapping in both canine and human AF suggests that there is a high degree of spatial organization of wave fronts and that cells do not reexcite as soon as their refractory periods end.6 7 Still, the major determinant of the maximum rate at which a region of myocardium can reexcite is the local ERP. Furthermore, the magnitude and spatial distribution of the ERP will determine whether intra-atrial reentry is initiated and maintained.8 9 10 However, to measure the local refractory period at multiple sites by the extrastimulus technique requires an extensive period of time. A method to rapidly estimate the distribution of refractory periods would help in elucidating a potential underlying substrate of AF. It would also allow evaluation of interventions that change ERP. The hypothesis of this study is that during AF, an individual site will eventually activate at an interval near its ERP. The objective of this study was to use local activation intervals during AF to estimate the local ERP. Specifically, the goals of this study were (1) to demonstrate the temporal stability of local refractory periods under controlled conditions in the isolated canine atrial model, (2) to demonstrate a correlation between the local ERP and local minimum AFI, (3) to use the computer-calculated local AFI as a predictor of the local ERP, and (4) to use AFI distribution maps to predict sites of conduction block.
Normal mongrel dogs (n=8) weighing 28.6±1.6 kg were anesthetized with pentobarbital (30 mg/kg IV). The animals were intubated and ventilated with a positive-pressure ventilator. A median sternotomy was performed, and the azygos vein was ligated and divided. After the pericardium was opened, the fat pad on the right AV groove was opened to expose the right coronary artery and its atrial and ventricular branches. After heparinization of the animals with sodium heparin (100 U/kg IV), the ventricular branches of the right coronary artery were ligated. The inferior and superior venae cavae were ligated and divided, and the aorta was cross-clamped. Cold crystalloid cardioplegia solution (500 mL) was infused into the aortic root, and both atria were rapidly excised. The proximal right and left coronary arteries were separately cannulated with polyethylene tubing (ID, 0.86 mm; OD, 1.27 mm). After the ventricular branches of the left coronary artery were dissected and ligated, the ventricular myocardium was trimmed away. Both intact atria were mounted on separate endocardial electrodes through each AV valve orifice. The preparation was placed in a temperature-controlled bath at 37°C and perfused with Krebs-Henseleit solution (pH, 7.4) through each coronary cannula. A flow rate of 10 to 15 mL/min was used to maintain a coronary perfusion pressure of 80 to 100 mm Hg. The preparation was also continuously superfused. Within 1 minute of establishing of perfusion, the preparation beat spontaneously in sinus rhythm. After a 30-minute stabilization period, data acquisition was started. Bipolar electrograms were simultaneously recorded from 253 endocardial sites during control conditions at the beginning and end of the study (sinus rhythm) and during the ERP measurement and induction of AF with a single extrastimulus. Sixteen to 20 electrodes that were randomly selected from both atria were used to measure the local ERP. The electrode locations were chosen to cover all regions of both atria. The pacing threshold was determined at each electrode site. Only sites with a threshold of <1 mA were used. The pacing stimulus was set at two times threshold. The S1S1 interval was set at 300 ms, and the extrastimulus was delivered after a train of eight paced complexes. ERP was determined by initially increasing the S1S2 interval by 10 ms until capture occurred. The S1S2 interval was then decreased by 1 ms until failure to capture occurred on three successive attempts. ERP determinations were repeated in the same manner at the same sites ≈2 hours later for statistical comparison to determine electrophysiological stability of the preparation. In all studies, several episodes of sustained AF were induced during the measurement of ERP. During fibrillation, the electrophysiological data were saved for 5 to 18 seconds from the time of initiation of AF. In cases of sustained AF >3 minutes in duration, infusion of 2 to 3 mL of cold crystalloid cardioplegia solution was required to defibrillate the atria chemically. Data analysis was resumed 10 minutes after restoration of sinus rhythm.
A 256-channel computerized data acquisition and analysis system was used to collect, process, and display data. The mapping system was based on a VaxStation II/GPX graphics workstation connected to two 128-channel PDP 11/23+–based data acquisition subsystems. Each PDP system contains two data translation DT3362 64-channel analog-to-digital converter boards and a 4-Mb memory board. The PDPs are diskless systems run by a control program that is downloaded from the Vax. The PDP memory is configured as a circular buffer, which allows the most recent 16 seconds of data to be saved. When the desired data are obtained, it is uploaded to the Vax via a direct memory access interface. The system uses a 256-channel bipolar amplifier system designed and built in-house with selectable high- and low-pass filters and gains. The systems are run with software developed in-house (GLAS) for data acquisition control, data management, raw data display, and data analysis.
A selected electrogram was continuously monitored throughout the study. Bipolar electrograms were recorded at a gain of 1000, with a frequency response of 50 to 500 Hz. Each channel was digitized at 1000 Hz with 12-bit resolution. Local endocardial activation times were determined from the maximum amplitude of the bipolar electrograms. All electrograms were edited visually to verify accuracy of the computer-picked activation time. In particular, the data were examined for double potentials, which resulted from recordings made near an activation block or rotating wave fronts. All electrograms that had deflections occurring within 100 ms of another activation were considered candidates for double potentials. Both electrograms were examined relative to the surrounding electrograms. Usually, electrograms from two adjacent sites had activations that corresponded with each deflection of the electrogram in question. The largest potential was always chosen from the double potential, and the smaller one was not counted as an activation. Typically, one deflection was much smaller in amplitude, as illustrated in Fig 1⇓, electrogram 208, cycles six and seven. In addition, the computer did not pick activations occurring within 60 ms of another activation. This usually eliminated the computer's picking both deflections in a double potential; however, this was verified manually by a check of adjacent sites. Each AFI was calculated by subtracting the local activation time between two adjacent local activations during a 2-second period of AF. A total of 8 to 18 seconds of AF were then analyzed to calculate the minimum, mean, and median AFIs during that time span. Examples of four electrograms recorded during AF are shown in Fig 1⇓, along with the AFI and the mean, SD, and minimum values. Also shown is the local ERP determined by extrastimulation. The minimum AFIs calculated at all 253 sites were used to construct maps that were displayed on a schematic diagram or three-dimensional surface model of both atria.
Paired and unpaired t tests were used for statistical analysis. Correlations were made with the standard Pearson's correlation, and probabilities were calculated for each correlation coefficient. Linear regression was used to relate the AFI to the ERP. Results were considered to be statistically significant when P<.05. All data, unless otherwise noted, were expressed as mean±SD. All statistical analyses were performed with the SYSTAT statistical program.11
All animals received humane care in compliance with the “Principles of Laboratory Animal Care” formulated by the National Society for Medical Research and the Guide for the Care and Use of Laboratory of Animals prepared by the National Academy of Science and published by the National Institutes of Health (NIH publication 86-23, revised 1985). In addition, the study protocol was approved by the Washington University Animal Studies Committee.
Within 30 minutes after reperfusion with Krebs-Henseleit solution, the spontaneous rhythm stabilized. Data acquisition was started at this time. The mean duration of the study, from the beginning to the end of data acquisition, was 215±36 minutes. The spontaneous cycle length of sinus rhythm was measured at the beginning and the end of the study, with mean cycle lengths of 665±69 and 664±63 ms, respectively (n=8, P=.876, NS). The mean ERPs of all measured sites during the initial and later recordings were 145±27 and 144±25 ms, respectively (n=173, P=.471, NS).
In all studies, multiple episodes of sustained AF (>3 minutes) were induced by a single extrastimulus during measurement of ERP. Fig 2A⇓ illustrates an example electrogram recorded during 12 seconds of AF induced by a single extrastimulus. The individual cycle lengths of each AFI are shown over the 12-second recording period in Fig 2B⇓. The ERP measured at this same recording site was 118 ms. In Fig 2C⇓, a histogram shows the distribution of AFIs for the recording period. The distribution of AFIs from 19 different sites at which the ERP was measured is illustrated in Fig 3⇓ for a 10-second episode of AF. The data are shown as the AFIs minus the measured ERP. The aggregate data are consistent with the data shown in Fig 2⇓, in which the majority of AFIs are longer than the local ERP. In the data shown in Fig 3⇓, >93% of the AFIs are longer than the measured ERP.
To compare the local ERP with the local AFI, sites that showed an ERP difference of <10 ms over the time course of the study and a pacing threshold of <1 mA in both the initial and later measurements were selected. The smaller of the two ERPs was used for comparison with the AFI. The mean ERP at the sites at which AF could be induced was 124±18 ms (n=21), in comparison to the ERP of all sites, which was 141±25 ms (n=173, P<.005). Each AFI was calculated during a 2-second period of AF. Four to 9 of these 2-second periods were then analyzed to calculate the minimum, mean, and median values encompassing the total duration of AF analyzed. The ERP and minimum AFI values converged with analysis of an increasing time interval of AF (Fig 4⇓). The mean absolute difference between the local ERP and local minimum AFI was 33 ms when only a 2-second period of AF was analyzed. However, as longer durations were analyzed, the mean absolute difference decreased to 18 ms at 4 seconds, 10 ms at 6 seconds, 6 ms at 8 seconds, etc (Fig 4⇓). Although the mean absolute difference decreased as more seconds of AF were analyzed, 10 seconds was chosen as the maximum time interval because the rate of decrease of the mean absolute difference was physiologically insignificant beyond this point.
The correlation coefficient between the local ERP and minimum local AFI was .922 (n=119, P<.001) when 10 seconds of AF was analyzed (Fig 5A⇓). During that time interval, a range of 2 to 4 different episodes of AF were included, with a mean of 2.6 episodes. The number of local activations ranged between 5 and 8 beats per second. The mean and median local AFIs were also calculated and compared with the minimum local AFI (Fig 5⇓). The correlation coefficients between the local ERP and mean local AFI and between the local ERP and median local AFI were .658 (n=119, P<.001) and .735 (n=119, P<.001), respectively, when the same duration of AF was analyzed. Although both the mean and median AFIs showed statistically significant correlations, the percentage of variation accounted for by linear regression models was <60%, whereas that of minimum AFI was 86%. In two studies, a single 10-second run of AF was analyzed. The correlation coefficient between the local ERP and minimum AFI was .771 (n=34, P<.001). This was higher than those of mean AFI (r=.438, n=34, P<.04) and of median AFI (r=.594, n=34, P<.001) (Table⇓).
An example minimum AFI map constructed from a total of 10 seconds of AF for all 250 electrode sites is shown in Fig 6⇓ displayed on a three-dimensional surface model of the endocardial surfaces of the atria. Sites at which AF was induced with a single extrastimulus are denoted by asterisks on the maps. Note that many of the sites in the atrium that induced AF are at sites of short ERP near a region of long ERP. Activation sequence maps of the A1 and A2 beats are shown along with electrograms recorded (Fig 7⇓) from the three marked sites in Fig 6⇓ recorded during a single extrastimulation. Block of activation is demonstrated corresponding with the region of long AFI.
This study demonstrates a correlation between ERP and the minimum AFI (r=.92). The correlation is better than that for the mean (r=.66) and median (r=.74) AFIs. As a result of the strong correlation, the minimum AFI determined over at least a 10-second period of AF can be used in conjunction with a multipoint mapping system to determine the field of ERP with high resolution. The model used is also unique in that it allowed estimation of the ERP independent of reflex changes and atrial pressure changes associated with AF. In addition, as demonstrated in this study (Figs 6 and 7⇑⇑), the map of the estimated ERP distribution can then be used to predict sites of block and potential reentry.
A major difference and a unique observation of this study compared with in vivo studies is how the ERP relates to the AFI. In the in vivo studies, the AFI was generally much shorter than the ERP. It has been generally thought that this is due to rate-dependent shortening of the ERP. In the present study, however, the mean AFI was longer. The present model is unique in that it is an in vitro model with no neuronal reflexes or pressure changes. In the in vivo studies, the data were taken in models in which reflex and dynamic atrial pressure changes occur. Both increased pressure and increased autonomic tone can decrease ERP.12 13 The difference suggests that the AFIs measured in the in vivo studies may reflect both the intrinsic ERP, rate-dependent changes, and changes induced in ERP by reflexes and pressure changes. The minimum AFI in vivo may more accurately reflect the true ERP during AF, even if it does not correlate as well with the ERP determined during pacing at a slower rate. Furthermore, rate-dependent shortening of ERP may not be as pronounced during AF because of the variability of the intervals.
The ERP in the right atrium was compared with that in the left atrium. As has been demonstrated previously,8 our data show that, even in the isolated atria, the left atrial refractory periods are shorter than the right atrial refractory periods. The mean ERPs in the right atrium and left atrium were 153±27 ms (n=84) and 130±18 ms (n=89), respectively, showing a statistically significant difference (P<.001). This may permit faster reentrant circuits to form in the left atrium, but repetitive 1:1 activation of the right atrium may be precluded by the longer refractory period of the right atrium.9 The mean refractory periods and the difference between the left and right atria are similar to those recorded in the intact animal.9 13
In this study, several episodes of sustained AF were induced by a single extrastimulus during measurement of ERP. The ERP at the sites at which AF was induced was much shorter than at other sites. This implies that the shorter S1S2 was allowed to propagate from those sites, reaching other sites that were still refractory and causing entrance block (Figs 6 and 7⇑⇑) and subsequently AF. During AF, local atrial electrograms vary in both configuration and cycle length (Figs 1 and 2⇑⇑). In this study, the relative differences between the AFI and ERP ranged from −44 to +223 ms, with skew of the distribution positive to the ERP. Allessie et al5 attributed the variation in local fibrillation intervals to either the variation in duration of local refractoriness or the presence of a significant excitable gap. The data in the present study suggest that there is an excitable gap, because most intervals are greater than the ERP. It is unlikely that the refractory period is greater during fibrillation than during extrastimulation, because the average AFIs are significantly shorter than the basic S1S1 drive rate (300 ms).
Previous studies have shown that the mean or median AFI correlates with ERP. Lammers et al14 demonstrated a significant correlation between the median AFI and ERP and suggested that the analysis of local AFI could be used to estimate the spatial dispersion of refractory periods. However, the correlation was made at only a single site. Although Lammers et al demonstrated a very good correlation, 8% of their data included much shorter AFIs, ranging between 5 and 50 ms. In retrospect, these probably represent double potentials and not individual activation. To eliminate the problem of picking both deflections of a double potential, the AFI data could be fitted to a statistical distribution and a lower hinge of the distribution used to estimate ERP. However, this type of analysis was not required because of the high spatial resolution and low noise of the data. If there is a significant amount of noise in the signal or if the spatial resolution is less, this technique may be needed to eliminate the effect of picking both deflections when only one activation occurs. Ramdat Misier et al15 also showed a close correlation between the mean AFI and ERP. They made the correlation at only four sites. More recently, Morillo et al16 demonstrated a strong correlation between ERP and mean ERP from a limited number of sites in a chronically paced canine model of AF.
In the present study, the ERP and minimum AFI converged with analysis of an increasing time interval of AF. The mean absolute difference decreased as more seconds of AF were analyzed; however, 10 seconds was chosen as the maximum time interval because the mean absolute difference curve became flat beyond this point. During this time interval, 2 to 4 different runs of AF were included, with a mean of 2.6 runs. The mean number of local activations during this interval was 36. Considering that atrial refractoriness shows cross-species difference and tends to increase with body size, the number of local activations would theoretically be lower in humans than in dogs. Therefore, a longer period of AF may need to be analyzed in humans. In this study, the correlation coefficients between the ERP and mean AFI and between the ERP and median AFI were analyzed, resulting in an R2 value <60%. A higher correlation coefficient was obtained between the ERP and minimum AFI, with an R2 value of 86%, when 10 seconds of AF was analyzed. A single run of AF of 10-second duration was also analyzed in two studies. The correlation coefficient between the local ERP and minimum AFI was .771, which showed a lower value than that obtained from analysis of several runs. This value, however, was still higher than those of the mean or median AFI. These results suggest that analysis of at least a 10-second duration of AF that includes more than two runs and about 35 activations gives the best prediction of ERP from the minimum AFI.
The rate of convergence of the AFI to the ERP may give insight into the organization of the AF. The slower convergence of AFI to the ERP in a single episode of AF compared with multiple episodes, both of the same duration, suggests that there is a high degree of organization of the AF. The more random the activation process, the more rapid the rate of convergence will be, since any one region would be activated from an increasing number of directions at a wider range of time intervals. This would increase the probability that the region would activate near its ERP. Conversely, if a region activates synchronously, areas within that region with shorter ERP may never be stimulated by a wave front at a time near its ERP. An example of this is seen during regular flutter, in which all regions respond at the same rate. In this case, the AFI would never converge to the ERP.
The measurement of AFI could be used to evaluate antiarrhythmic drug therapy quantitatively, particularly class 3 agents that act to prolong refractory period. It is anticipated that the AFI would increase with prolongation of the ERP. Measurements of AFI at various concentrations of a drug would show how effective the drug was in prolonging ERP. In addition, when the AF terminated, it would delineate the critical amount of ERP prolongation needed to terminate AF in a particular patient.
Selected Abbreviations and Acronyms
|AFI||=||atrial fibrillation interval|
|ERP||=||effective refractory period|
This study was supported in part by National Institutes of Health grants R01-HL-32257 and R01-HL-33722.
- Received March 5, 1996.
- Revision received August 8, 1996.
- Accepted August 22, 1996.
- Copyright © 1996 by American Heart Association
Moe GK. On the multiple wavelet hypothesis of atrial fibrillation. Arch Int Pharmacodyn Ther. 1962;140:183-188.
Allessie MA, Lammers WJEP, Bonke FIM, Hollen J. Experimental evaluation of Moe's multiple wavelet hypothesis of atrial fibrillation. In: Zipes EP, Jalife J, eds. Cardiac Electrophysiology and Arrhythmias. Orlando, Fla: Grune & Stratton, Inc; 1985:265-275.
Schuessler RB, Grayson TM, Bromberg BI, Cox JL, Boineau JP. Cholinergically mediated tachyarrhythmias induced by a single extrastimulus in the isolated canine right atrium. Circ Res. 1992;71:1254-1267.
Opthof T, Ramdat Misier AR, Coronel R, Vermeulen JT, Verberne HJ, Frank RGJ, Moulijn AC, van Capelle FJL, Janse MJ. Dispersion of refractoriness in canine ventricular myocardium: effects of sympathetic stimulation. Circ Res. 1991;68:1204-1215.
Allessie MA, Kirchhof C, Scheffer GJ, Chorro F, Brugada J. Regional control of atrial fibrillation by rapid pacing in conscious dogs. Circulation. 1991;84:1689-1697.
Cox JL, Canavan TE, Schuessler RB, Cain ME, Lindsay BD, Stone C, Smith PK, Corr PB, Boineau JP. The surgical treatment of atrial fibrillation, II: intraoperative electrode physiologic mapping and description of the electrophysiologic basis of atrial flutter and atrial fibrillation. J Thorac Cardiovasc Surg. 1991;101:406-426.
Allessie MA. Reentrant mechanisms underlying atrial fibrillation. In: Zipes DP, Jalife J, eds. Cardiac Electrophysiology: From Cell to Bedside. Philadelphia, Pa: WB Saunders Co; 1995:562-566.
Systat for Windows: Statistics, Version 5 Edition. Evanston, Ill: SYSTAT, Inc; 1992.
Calkins H, El-Atassi R, Kalbfleisch S, Langberg J, Morady F. Effects of an acute increase in atrial pressure on atrial refractoriness in humans. Pacing Clin Electrophysiol. 1992;15(pt 1):1674-1680.
Schuessler RB, Boineau JP, Bromberg BI, Hand DE, Yamauchi S, Cox JL. Normal and abnormal activation of the atrium. In: Zipes DP, Jalife J, eds. Cardiac Electrophysiology: From Cell to Bedside. Philadelphia, Pa: WB Saunders Co; 1995:543-562.
Lammers WJEP, Allessie MA, Rensma PL, Schalij MJ. The use of fibrillation cycle length to determine spatial dispersion in electrophysiological properties and to characterize the underlying mechanism of fibrillation. New Trends Arrhythmias. 1986;2:109-112.
Morillo CA, Klein GJ, Jones DL, Guiraudon CM. Chronic rapid atrial pacing: structural, functional, and electrophysiological characteristics of a new model of sustained atrial fibrillation. Circulation. 1995;92:1588-1595.