Author + information
- Received May 18, 2018
- Revision received July 17, 2018
- Accepted August 16, 2018
- Published online October 31, 2018.
- Moisés Rodríguez-Mañero, MD, PhDa,b,∗ (, )
- Miguel Valderrábano, MDc,
- Aurora Baluja, MD, PhDd,
- Omar Kreidieh, MDe,
- Jose Luis Martínez-Sande, MD, PhDa,b,
- Javier García-Seara, MD, PhDa,b,
- Johan Saenen, MD, PhDf,
- Diego Iglesias-Álvarez, MDa,b,
- Wim Boriesf,
- Luis Miguel Villamayor-Blancoa,
- María Pereira-Vázqueza,
- Ricardo Lage, MD, PhDa,b,
- Julián Álvarez-Escudero, MD, PhDd,
- Hein Heidbuchel, MD, PhDf,
- José Ramón González-Juanatey, MD, PhDa,b and
- Andrea Sarkozy, MD, PhDf
- aCardiology Department, Hospital Universitario Santiago de Compostela, Santiago de Compostela, IDIS, Spain
- bCentro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV CB16/11/00226 - CB16/11/00420), Madrid, Spain
- cDivision of Cardiac Electrophysiology, Department of Cardiology Houston Methodist Hospital, Houston, Texas
- dCritical Patient Translational Research Group, Department of Anesthesiology, Intensive Care and Pain Management, Hospital Clínico Universitario, Santiago de Compostela, Spain
- eCardiology Department, Newark Beth Israel Medical Center, Newark, New Jersey
- fCardiology Department, Cardiac Electrophysiology Section, University Hospital of Antwerp, Antwerp, Belgium
- ↵∗Address for correspondence:
Dr. Moisés Rodríguez-Mañero, Cardiology Department, Complejo Hospital Universitario de Santiago, Santiago de Compostela, Spain, CIBERCV, Travesía da Choupana s/n, Santiago de Compostela, 15706 A Coruña, Spain.
Objectives This study aimed: 1) to determine the voltage correlation between sinus rhythm (SR) and atrial fibrillation (AF)/atrial flutter (AFL) using multielectrode fast automated mapping; 2) to identify a bipolar voltage cutoff for scar and/or low voltage areas (LVAs); and 3) to examine the reproducibility of voltage mapping in AF.
Background It is unclear if bipolar voltage cutoffs should be adjusted depending on the rhythm and/or area being mapped.
Methods High-density mapping was performed first in SR and afterward in induced AF/AFL. In some patients, 2 maps were performed during AF. Maps were combined to create a new one. Points of <1 mm difference were analyzed. Correlation was explored with scatterplots and agreement analysis was assessed with Bland-Altman plots. The generalized additive model was also applied.
Results A total of 2,002 paired-points were obtained. A cutoff of 0.35 mV in AFL predicted a sinus voltage of 0.5 mV (95% confidence interval [CI]: 0.12 to 2.02) and of 0.24 mV in AF (95% CI: 0.11 to 2.18; specificity [SP]: 0.94 and 0.96; sensitivity [SE]: 0.85 and 0.75, respectively). When generalized additive models were used, a cutoff of 0.38 mV was used for AFL for predicting a minimum value of 0.5 mV in SR (95% CI: 0.5 to 1.6; SP: 0.94, SE: 0.88) and of 0.31 mV for AF (95% CI: 0.5 to 1.2; SP: 0.95, SE: 0.82). With regard to AF maps, there was no change in the classification of any left atrial region other than the roof.
Conclusions It is possible to establish new cutoffs for AFL and/or AF with acceptable validity in predicting a sinus voltage of <0.5 mV. Multielectrode fast automated mapping in AFL and/or AF seems to be reliable and reproducible when classifying LVAs. These observations have clinical implications for left atrial voltage distribution and in procedures in which scar distribution is used to guide pulmonary vein isolation and/or re-isolation.
The pulmonary veins (PVs) are important trigger sites of paroxysmal atrial fibrillation (AF), and their electrical isolation from the left atrium (LA) is associated with a high rate of freedom from AF (1,2). In persistent AF, pulmonary vein isolation (PVI) is less successful (3), possibly because additional arrhythmogenic atrial sites beyond the PVs are responsible for AF maintenance. Atrial fibrosis and scar tissue serve as an important substrate for focal and re-entrant activity involved in persistent AF (4–7). Therefore, electroanatomic mapping has been suggested to delineate areas of scar tissue for targeting. Areas with low-voltage amplitude on mapping correspond to unexcitable tissue and delayed gadolinium enhancement on magnetic resonance imaging in multiple studies (8–12). Therefore, voltage-guided AF substrate modification by ablation targeting low-voltage areas (LVAs) has been performed to improve long-term AF ablation efficacy (13–20). Most of these procedures have performed mapping using established cutoffs for voltage during sinus rhythm (SR), and 1 study performed mapping during AF (15).
There remain multiple unresolved issues with LVA-guided substrate modification. Although some studies have showed that measured voltages are higher during SR than during AF, none have studied voltages in other atrial arrhythmias, such as atrial flutter (AFL) (11,21). Furthermore, it is unclear if bipolar voltage cutoffs should be adjusted depending on the rhythm, catheter type (22) (electrode size and interelectrode distance), and/or anatomic area being mapped. Most studies have used cutoffs in SR using an ablation catheter (3.5-mm tip), but the corresponding thresholds in AF are not known. Finally, there are no data regarding the reproducibility of LVA mapping in AF.
We aimed to: 1) determine the bipolar voltage cutoff for scar and/or LVAs using small sized, closely spaced multielectrode fast automated mapping (MFAM) during AF and AFL that corresponds to a sinus bipolar voltage of 0.5 mV; 2) determine the voltage correlation between SR and AF and/or AFL; and 3) to examine the reproducibility of LA voltage mapping in AF.
This was a multicenter prospective study, performed in 3 hospitals with experience in the field of AF mapping and ablation. Patient demographics, clinical characteristics, and medications were exported from patient records. All participants provided written, informed consent for both the ablation procedure and inclusion in medical research at the time of procedure.
High-density atrial mapping
Patients with AF who underwent first or repeat ablation also underwent sequential mapping of the LA using a PentaRay catheter (Biosense Webster, Diamond Bar, California), which contains 5 splines with 4 electrodes each (1 mm, spaced 2-6-2 mm apart). In SR, atrial electrograms were captured by setting the window of interest from −50 to −350 ms, preceding the sharp component of each ventricular QRS complex as a reference. High-density bipolar voltage mapping of the LA was performed with an equal distribution of points using a fill threshold of 5 to 7 mm, with a minimum of 1,000 points in each LA map. Low-voltage zones were defined as 0.1 to 0.49 mV (peak-to-peak bipolar voltage) and transitional zones were considered ≥0.5 to 1.4 mV. If the patient was in AF, the patient was cardioverted at the beginning of the study to obtain the first map in SR (hence, avoiding map displacement after the cardioversion). Afterward, AF and/or AFL was induced by atrial burst pacing from the distal or mid-coronary sinus at a cycle of 250 to 180 ms. In those patients in whom the 2 maps were performed during AF, a 10-min waiting period was performed (2 obtained maps called AF1 and AF2).
In addition, high-density mapping was added at sites where LVAs were recorded to exactly delineate the extent of the LVA. Stability was selected at 5 mm. To avoid poor contact points, we set the interior and exterior projection distance filtering to 5 mm from the geometry surface. In SR, points that did not conform to the surface electrocardiogram P-wave morphology or 75% of the maximum voltage of the preceding electrogram were excluded. Signals were filtered at 30 to 400 Hz and displayed at 100 mm/s. The electrogram at each point acquired on the LA shell was manually reviewed to exclude noise or a pacing artefact before being accepted. We included 7 segments (septum, anterior wall, floor inferior, lateral wall, posterior wall, roof, and PVs). Each LA−PV junction was defined as the region that extended 5 mm proximal to the PV ostia circumferentially and that documented an impedance rise of 10 ohms compared with the LA.
Two separate LA shells (bipolar voltage maps) were created for each patient in 2 different clinical rhythms or in AF at different times. To compare local bipolar voltages from each site in different rhythms, each map was tagged in a separate color before being combined on a new map (Figure 1). Complete transparency was used to manually review points obtained in the same location on each mapping procedure and/or LA shell. Only those point pairs with a distance of <1 mm between them were analyzed. Once the 2 points were compared, they were tagged in a different color to avoid duplication.
Following the maps, PVI was performed with circumferential ablation around both ipsilateral PVs using a dragging technique.
Numerical data were tested for normality using the Shapiro-Wilk test, and for homoskedasticity with Levene’s test, then summarized with mean, median, SD, and interquartile range values where appropriate. Voltages by rhythm and region were represented by a ridgeline plot shown in Figure 2, ordered by median voltage. Correlation was explored with scatterplots and agreement analysis with Bland-Altman (Tukey mean difference) plots.
The bias and mean limits of agreement from the Bland-Altman-Tukey analyses were used to predict the SR voltage that would be equivalent to a new simulated set of AFL and AF voltages. A peak-to-peak voltage of 0.5 mV in SR was defined as a reference threshold value that defined scarred tissue (10–12,23). The AFL and/or AF thresholds were defined as the peak-to-peak voltage equivalent to a sinus amplitude of 0.5 mV and were set to be the corresponding values when the predicted sinus value matched 0.5 mV.
To evaluate the performance of this method, predicted SR values were obtained from the measured AFL and/or AF values, and then compared with the actual sinus voltages. Using the proportion of predicted sinus values that fell into the same category (either <0.5 mV or ≥0.5 mV) as their measured sinus counterparts, we were able to calculate sensitivity, specificity, false positive and negative ratios, and accuracy.
To find the regional AFL and/or AF peak-to-peak voltages equivalent to 0.5 mV in SR, we adjusted a generalized additive model (GAM), taking into account the patient as an independent random effect. Flexible penalized splines were used to model the continuous covariate with the following formulas (simplified):
Similar to the prediction that used the Bland-Altman analyses, we calculated the threshold in either AFL or AF voltages that corresponded to a minimal sinus voltage of 0.5 mV when using the lower band of the 95% confidence interval (CI) for a given predicted mean sinus voltage. To evaluate the performance of this method, we predicted SR voltages using the generalized additive model, and then compared them with the actual sinus voltages. Using the proportion of predicted sinus values that fell into the same category (either <0.5 or ≥0.5mV) as their measured sinus counterparts, we were able to calculate sensitivity, specificity, false positive, negative ratios, and accuracy.
Estimates of thresholds were calculated both for the global model and for every region.
Patient and map characteristics
A total of 31 patients were included in the study. Twenty-one of them underwent repeat procedures, and the rest were de novo procedures. Mean age was 67 ± 12 years; 21 patients were men (68%), 21 had nonparoxysmal AF (68%), and the mean LA diameter was 43 mm. All patients underwent sequential maps in SR and AF. Ten patients also underwent mapping during AFL. A total of 2,002 paired points (4,004 points) were obtained from the 31 patients’ maps: 962 paired points (48.1%) that compared sinus SR versus AF, 545 (27.2%) that compared SR versus AFL, and finally 495 (24.7%) that compared AF versus AF. Online Table 1 describes the number of points and their locations. Briefly, the most sampled region corresponded to the posterior wall, with 609 (30.4%) paired points. The LA appendage was the least sampled to minimize the risks of the procedure (63 paired points; 3.1%).
Mean regional LA voltage distribution in SR, AFL, AF1, and AF2 are shown in Table 1. Values are significantly higher in SR than in AFL and AF. Moreover, we observed distinct regional differences in voltage values even when patients were in the same rhythm. Table 2 and Figure 3 show regional differences for each rhythm. The Kendall rank correlation coefficient (Kendall's tau coefficient), which was used to measure the ordinal association between 2 measured quantities, is shown in Figures 3A, 3C, and 3E. Although the Kendall's tau coefficient is modest, as seen graphically, it could be due to greater variation with the higher voltages. It seems to show a good relationship for values <0.5 mV, but this is lost at higher voltages. Figures 3B, 3D, and 3F show a graphic comparison of regional bipolar voltage correlation.
Because bipolar voltages within the PVs are low during SR and AF, in we offer additional detail in Online Figure 1 into the correlation pattern between sinus (A1), AFL (A2), and AF (A3) in the PV region. Graphs corresponding to the correlation between sinus and AF voltages, scaled at 0 to 0.25, 0 to 0.5, and 0.5 to 1.5, are shown in Online Figures 1B1 to 1B3. Online Figures 1B1 to 1B3 also shows grouped voltages, whereas Figures 2A to 2C show voltages by region. In Online Figures 2B and 2C, data are insufficient to correctly draw 95% CI bands for the local regressions. In the appendage region, there was only 1 point <0.5, and there were no voltages in the 0.5 and 1.5 ranges. The positive correlation between both voltages was maintained across all ranges. However, the dispersion also consistently increased with the increase in voltages, making log transformation of the data necessary to correct this finding.
Voltage correlation between SR-AF and/or AFL with a potential cutoff value for LVA
As previously explained, 2 different statistical analyses were performed to define a cutoff value between 1) AFL and SR, and 2) AF and SR.
After correlation and Bland-Altman analyses, the corresponding plots showed a heavily heteroskedastic funnel-like structure (Figure 4), which was corrected after natural-logarithm transformation of the data, followed by exponentiation of the final estimates. Final voltage thresholds and their prediction assessment values were calculated according to the methods described by Bland-Altman (25) and Euser et al. (26) for logarithm transformed data, and are shown in their respective tables.
In Figure 5 and Online Table 2, using Bland-Altman model, we showed the predicted voltage for SR (with its 95% CI) for each specific point obtained in AFL and in AF. As shown, 0.35 mV in AFL predicted a sinus voltage of 0.5 (95% CI: 0.12 to 2.02), and 0.25 mV in AF predicted a sinus voltage of 0.5 (95% CI: 0.11 to 2.18) (negative predicted value: 0.94, positive predicted value: 0.85, specificity: 0.94, sensitivity: 0.85, and accuracy: 0.92; negative predicted value: 0.93, positive predicted value: 0.83, specificity: 0.96, sensitivity: 0.75, and accuracy: 0.91, respectively).
In contrast, if generalized additive models were used, the best model gave rise to a cutoff of 0.31 mV in AF for predicting a minimum value of 0.5 mV in SR (0.5 to 1.2), with a specificity of 0.95 and a sensitivity of 0.82. For AFL, a value of 0.38 mV predicted a minimum value of 0.5 mV in SR of 0.55 (0.5 to 1.6), with a specificity of 0.94 and a sensitivity of 0.88. Negative and positive predictive values, and accuracy are shown in Table 3. This correlation can be seen in a dynamic way in the MASH-AF 1 study (27).
Reliability of LA voltage map in AF
As previously mentioned, there were differences in mean regional LA voltage between AF1 and AF2. The Kendall's tau coefficient is shown in Figure 2D. However, although there are significant differences between the 2 AF maps, these values were clinically irrelevant; if we used the conventional division of low-voltage zones (0.1 to 0.49 mV), transitional zones (0.5 to 1.49 mV), and healthy areas (1.5), there was no change in the classification of any LA region other than the roof, which changed from a transitional zone to normal tissue (1.45 to 4.1 to 1.85 to 6.14). Furthermore, when we examined the 545 points for concordance in classifying a segment as LVA or not (<0.5 mV or ≥0.5 mV), 24 points were ≥0.5 mV in AF1 and <0.5 in AF2 map, and 26 points were vice versa. Concordance was far more prevalent with 93 points being <0.5 mV in both maps and 352 points ≥0.5 mV in AF1 and AF2.
Finally, the mean value matched well with the value obtained in the first map, although the CI showed a wide distribution (Online Table 2C). For instance, 2.15 mV in AF1 corresponded to a mean value of 2.27 mV in the second map, but with a 95% CI of 0.498 to 9.286.
This was a prospective study that examined differences in LA voltage between SR and AF or AFL in the same patient using point-by-point comparison by high-density, closely spaced small electrode bipolar voltage mapping. We also assessed the LA voltage consistency in patients with AF. Our main findings were: 1) there were significant differences in global and regional voltage distribution when we compared different areas during the same rhythm or different rhythms in the same area; 2) despite a difference in voltage between rhythms, it was possible to establish new cutoffs for AF and AFL with acceptable validity in predicting a sinus voltage <0.5 mV; and 3) multielectrode fast automated electroanatomic bipolar mapping with closely spaced (2 mm) small electrodes (1 mm) in AF seems to be reliable and reproducible when classifying low voltage zones.
These observations had clinical implications for LA voltage distribution, particularly in procedures in which scar distribution was used to guide PV isolation and/or re-isolation.
Atrial fibrosis and AF
Atrial fibrosis has been observed in greater frequency in patients with AF (5,6). At the same time, evidence for extensive fibrosis is also associated with a longer standing history of the arrhythmia as well as a lower success rate for PVI (3). These findings suggest a possible mechanistic relationship between atrial fibrosis and/or scarring and AF. Although reparative fibrosis has been suggested to replace electrically active conductive tissue, thus causing anisotropy and enhancing re-entry, interstitial fibrosis may be more electrically inert (28). However, substrate manipulation in areas of fibrosis has been suggested to help improve outcomes for selected patients (29). Wang et al. (17) performed a randomized controlled trial of substrate modification versus a more traditional stepwise ablation approach for patients with long-standing persistent AF. The authors found improved effectiveness after a first procedure in the substrate modification groups, a lower atrial tachycardia recurrence rate, and a shorter procedure time, but the benefits faded after repeat procedures. Lin et al. (9) showed that rotors involved in AF maintenance exhibited lower voltage than sites without rotors (0.68 ± 0.44 vs. 0.71 ± 0.63). A meta-analysis of similar studies showed a benefit of adding substrate modification targeting areas of scar identified by low voltage compared with traditional PVI only (15,30). It seems that absence of atrial low voltage may identify patients in whom PVI alone is likely to be sufficient, whereas the presence of atrial low voltage may indicate scar tissue and potential alternative sources of the arrhythmia related to a slow conducting substrate (29).
Identifying fibrotic tissue by means of abnormal electrocardiogram
Electrophysiologically, atrial fibrosis produces low-amplitude electrograms (10), electrogram fractionation, conduction heterogeneity, and manifests as abnormal signals that can be identified using electroanatomic mapping during SR (31,32). A 3-dimensional map may thus show areas of fibrosis to help guide diagnosis or ablation procedures. Several authors already showed that LVAs identified in SR on such electroanatomic maps of the atria correlated well with late gadolinium enhancement seen on cardiac magnetic resonance imaging (8,10,11,33,34). Nevertheless, some limitations to those studies should be highlighted. First, the use of current magnetic resonance imaging technology to diagnose atrial scarring has several limitations and is not well validated against histological evidence (30). Second, a low voltage on bipolar electrodes may be related to several factors independent of fibrosis, including direction of conduction vector, amount of contact, and electrode size and interelectrode distance characteristics (28).
Voltage differences during different rhythms
Our finding of higher voltages in SR than AF is in agreement with previous studies in the literature. Yagashita et al. (11) found a linear voltage correlation between SR and AF using an ablation catheter (3.5-mm tip) (11), whereas Masuda et al. (21) found that the correlation was only present if electrograms did not become fractionated during AF. There was a possible mechanistic explanation for the observation of progressively lower voltages in AFL and AF compared with SR. During arrhythmias with shorter cycle lengths, a substantial amount of tissue might not depolarize when it is still refractory, or small pieces of underlying or neighboring tissues might depolarize nonsimultaneously and in opposite directions, which results in low bipolar voltage amplitudes (21). In addition, bipolar voltage was dependent on the direction of wavefront propagation. In AFL, the more organized activation seen in macro−re-entries compared with micro−re-entries in AF could give rise to less beat-to-beat variation and a more steady peak-to-peak deflection.
Ideal cutoff for ablating
The bipolar voltage of <0.5 mV cutoff was originally based on baseline noise levels in early electroanatomic mapping systems and was only later validated in imaging studies (23,35). Therefore, the ideal clinical cutoff value was arguably still unknown, but our study findings suggested that such a threshold was likely to be different depending on the rhythm during mapping. Furthermore, only a few studies validated this conventional cutoff when it was used in atrial mapping during AF (7). Even more important, most clinical studies of substrate modification procedures performed mapping mostly during SR (15). Jadidi et al. (19) performed mapping in AF with different types of catheters and evaluated the effects of ablating at points with certain electrogram characteristics lying in or near LVAs after PVI. The authors successfully demonstrated improved effectiveness compared with a conventional PVI-only strategy for persistent AF. Although values changed slightly according to the statistical analysis used, we found that a 0.38-mV cutoff in AFL and 0.31-mV cutoff in AF provided a good negative predicted value, positive predicted value, specificity, sensitivity, and accuracy for predicting a voltage of at least 0.5 mV in SR. Moreover, as indicated in Table 3, these cutoffs could be even further adjusted according to the sampled region.
In the study by Yagashita et al. (11), the number of points with LVA in AF, using a bipolar voltage cutoff of 0.5 mV (4.0 ± 2.4) (using a 3.5-mm tip ablation catheter), was significantly higher than those used in SR using the same cutoff (1.9 ± 2.1; p < 0.001). An adjusted bipolar voltage of 1.5 mV in SR produced a similar area of fibrosis to that of 0.5 mV in AF (p = 0.125). In our opinion, there are several limitations to that approach for defining cutoffs. First, because a sinus voltage of 0.5 mV is the most studied, we believe it is most important to define the thresholds in AF and AFL that predict such a voltage in SR and not the inverse. Second, the total area of low voltage depends on the number of points sampled in each particular segment. Derivations based on the area of low voltage may thus be biased by the interpolation created at the time of the electroanatomical map. In our study, we derived thresholds from an analysis based on a point-by-point comparison and were able to prove great accuracy for predicting a sinus voltage <0.5 mV. Our study also derived threshold values for AFL and studied the reproducibility of LVAs in AF patients.
Reliability of mapping in AF
Bipolar voltage is measured in a single window as the maximum peak-to-peak voltage of 2 to 3 consecutive AF beats, and may be subject to temporal variation and quality of contact. For this reason, we attempted to use a widened window of 400 ms. Remarkably, despite the beat-to-beat variation seen in AF, we proved good reproducibility of the AF voltage maps and confirmed the reliability of LVA mapping during this rhythm.
Voltage mapping in the LA to guide AF ablation depends on the accuracy of each mapping technique and the underlying rhythm. This is particularly important, because recent publications that described success with substrate-based AF ablation approaches were highly dependent on accurate identification and interpretation of LA scar (14–19). Our present study suggested that there might be differences in ideal voltage thresholds according to the underlying rhythm. This must be taken into account when mapping during nonsinus clinical rhythms or when designing future studies on the subject. For example, this is critical awareness of low voltage distribution is critical for instance when conversion of AF to sinus rhythm fails or when it is not desired to perform a cardioversion in those cases when mechanism based AF mapping is attempted. We showed that mapping using an adjusted lower LVA detection cutoff might produce valid and reliable results that could identify areas with a voltage of <0.5 mV in SR.
There were several limitations for our study. Our analysis could have been affected by undetected map shifts. We attempted to minimize this by performing well-distributed electroanatomic voltage mapping using CARTO3 system (Biosense Webster, Diamond Bar, California) and strict analysis, including only adjacent points that were <1 mm apart. Furthermore, we cardioverted patients with AF at the beginning of the study, so that the first mapping procedure was obtained in SR, thus avoiding any significant map shifts during cardioversion. We had nearly identical LA volumes in SR and AF maps that we believe add credence to the reliability of the mapping obtained. However, despite the fact of having nearly identical LA volumes in SR and AF maps, as it is well known that LA volume can be different between SR and AF because volume loading is different with different rhythms. We tried to overcome this limitation by taking stables references and comparing them along the course of the procedure (coronary sinus, His location, and the PV antrum) and by excluding those cases in which a cardioversion was needed. For all these reasons, this is the reason why it should not have significantly altered the conclusions of the present study.
In some patients, we might have underestimated the voltage due to possible stunned myocardium. We used CARTO3 for electroanatomic voltage mapping and 1-mm electrodes with 2-mm interelectrode spacing; therefore, our findings might not be applicable to other mapping systems and catheters. Because we do not currently perform pre-ablation cardiac magnetic resonance routinely on patients undergoing repeat AF ablation, we were unable to make comparisons between electroanatomic mapping-derived scar versus LA scar as seen on cardiac magnetic resonance in this patient series.
There are further implications in non-AF studies as well. For example, “fine tune” scar recognition and subsequent ablation is extremely important during AFL ablation. In this situation, interruption of AFL might be undesirable to avoid difficulty with inducing it afterwards. Hence, activation mapping and electroanatomical mapping, both performed during on-going AFL or AF, could help delineate the re-entry or focal site, and the substrate without interrupting the rhythm.
COMPETENCY IN MEDICAL KNOWLEDGE: Atrial fibrosis and/or scar tissue serves as an important substrate for focal and re-entrant activity. Electroanatomic mapping has been suggested to delineate areas of scar. However, there remain multiple unresolved issues, such as the voltage correlation between sinus and atrial AF or AFL and the reproducibility of voltage mapping in AF.
TRANSLATIONAL OUTLOOK: It is possible to establish new cutoffs for AF and/or AFL with acceptable validity in predicting a sinus voltage <0.5 mV. Moreover, electroanatomic mapping in AF seems to be reliable and reproducible when classifying LVAs. Investigators are requested to consider the sufficiency of this data and to determine whether it has clinical implications in those procedures in which scar distribution is used to guide AF ablation.
Dr. Valderrábano has received research support from Biosense Webster. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
All authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the JACC: Clinical Electrophysiology author instructions page.
- Abbreviations and Acronyms
- atrial fibrillation
- atrial flutter
- confidence interval
- left atrium
- low voltage area
- pulmonary vein
- pulmonary vein isolation
- sinus rhythm
- Received May 18, 2018.
- Revision received July 17, 2018.
- Accepted August 16, 2018.
- 2018 American College of Cardiology Foundation
- Verma A.,
- Wazni O.M.,
- Marrouche N.F.,
- et al.
- Platonov P.G.,
- Mitrofanova L.B.,
- Orshanskaya V.,
- Ho S.Y.
- Oakes R.S.,
- Badger T.J.,
- Kholmovski E.G.,
- et al.
- Jadidi A.S.,
- Cochet H.,
- Shah A.J.,
- et al.
- Anter E.,
- McElderry T.H.,
- Contreras-Valdes F.M.,
- et al.
- Lin Y.J.,
- Lo M.T.,
- Lin C.,
- et al.
- Anter E.,
- Tschabrunn C.M.,
- Josephson M.E.
- Yagishita A.,
- S DEO,
- Cakulev I.,
- et al.
- Squara F.,
- Frankel D.S.,
- Schaller R.,
- et al.
- Jadidi A.,
- Cochet H.,
- Miyazaki A.,
- et al.
- Nademanee K.,
- McKenzie J.,
- Kosar E.,
- et al.
- Blandino A.,
- Bianchi F.,
- Grossi S.,
- et al.
- Rolf S.,
- Kircher S.,
- Arya A.,
- et al.
- Wang X.H.,
- Li Z.,
- Mao J.L.,
- He B.
- Cutler M.J.,
- Johnson J.,
- Abozguia K.,
- et al.
- Jadidi A.S.,
- Lehrmann H.,
- Keyl C.,
- et al.
- Yamaguchi T.,
- Tsuchiya T.,
- Nakahara S.,
- et al.
- Masuda M.,
- Fujita M.,
- Iida O.,
- et al.
- Sanders P.,
- Morton J.B.,
- Kistler P.M.,
- et al.
- Wickham H.
- ↵MASH-AF1 Study. Available at: https://aurora.shinyapps.io/mashaf. Accessed September 13, 2018.
- Anter E.,
- Josephson M.E.
- Hunter R.J.,
- Diab I.,
- Tayebjee M.,
- et al.
- Yang B.,
- Jiang C.,
- Lin Y.,
- et al.
- Arentz T.,
- Muller-Edenborn B.,
- Jadidi A.
- Prabhu S.,
- Voskoboinik A.,
- McLellan A.J.A.,
- et al.
- Hwang S.H.,
- Oh Y.W.,
- Lee D.I.,
- Shim J.,
- Park S.W.,
- Kim Y.H.
- Koutalas E.,
- Rolf S.,
- Dinov B.,
- et al.