Electrophysiologic Determinants of Isoelectric Intervals on Surface Electrocardiograms During Atrial Tachycardia: Insights From High-Density Mapping.

BACKGROUND: Substrate abnormalities can alter atrial activation during atrial tachycardias (ATs) thereby influencing AT-wave morphology on the surface electrocardiogram. OBJECTIVES: This study sought to identify determinants of isoelectric intervals during ATs with complex atrial activation patterns. METHODS: High-density activation maps of 126 ATs were studied. To assess the impact of the activated atrial surface on the presence of isoelectric intervals, this study measured the minimum activated area throughout the AT cycle, defined as the smallest activated area within a 50-millisecond period, by using signal processing algorithms (LUMIPOINT). RESULTS: ATs with isoelectric intervals (P-wave ATs) included 23 macro-re-entrant ATs (40%), 26 localized-re-entrant ATs (46%), and 8 focal ATs (14%), whereas those without included 46 macro-re-entrant ATs (67%), 21 localized-re-entrant ATs (30%), and 2 focal ATs (3%). Multivariable regression identified smaller minimum activated area and larger very low voltage area as independent predictors of P-wave ATs (OR: 0.732; 95% CI: 0.644-0.831; P < 0.001; and OR: 1.042; 95% CI: 1.006-1.080; P = 0.023, respectively). The minimum activated area with the cutoff value of 10 cm2 provided the highest predictive accuracy for P-wave ATs with sensitivity, specificity, and positive and negative predictive values of 96%, 97%, 97%, and 95%, respectively. In re-entrant ATs, smaller minimum activated area was associated with lower minimum conduction velocity within the circuit and fewer areas of delayed conduction outside of the circuit (standardized ß: 0.524; 95% CI: 0.373-0.675; P < 0.001; and standardized ß: 0.353; 95% CI: 0.198-0.508; P < 0.001, respectively). CONCLUSIONS: Reduced atrial activation area and voltage were associated with isoelectric intervals during ATs.

Abnormal Atrial Potentials Recorded During Sinus Rhythm or Pacing Represent Substrates for Reentrant Atrial Tachycardia.

BACKGROUND: Abnormal atrial potentials (AAPs) recorded during sinus rhythm/atrial pacing may indicate areas of slow conduction capable of supporting reentrant atrial tachycardia (AT). Therefore, we sought to examine the relationship between AAPs and AT circuits. METHODS: One hundred twenty-three reentrant ATs in 104 patients were analyzed. AAPs, consisting of fragmented potentials and split potentials, were assessed using the Rhythmia LUMIPOINT algorithm. RESULTS: There was 93±13% overlap between areas with AAPs during sinus rhythm/atrial pacing and areas of slow conduction along the reentry circuit during AT. The cumulative area of AAPs was smaller in patients with localized-reentrant ATs compared with anatomic macro-reentrant ATs (20.0 [14.6-30.5] versus 28.9 [21.8-35.6] cm2; P=0.021). Patients with perimitral ATs had larger areas of AAPs on the lateral wall whereas patients with roof-dependent ATs had larger areas of AAPs on the roof and posterior wall (P≤0.018 for all comparisons). The patchy scar that was associated with localized-reentrant AT exhibited a larger area of AAPs at its periphery than the scar that did not participate in localized-reentrant AT (3.1 [2.4-4.5] versus 1.0 [0.7-1.6] cm2; P<0.001). CONCLUSIONS: AAPs recorded during sinus rhythm/atrial pacing are associated with areas of slow conduction during reentrant AT. The burden and distribution of AAPs may provide actionable insights into AT circuit features, including in cases in which ATs are difficult to map.

High-resolution mapping of reentrant atrial tachycardias: Relevance of low bipolar voltage.

BACKGROUND: Bipolar voltage is widely used to characterize the atrial substrate but has been poorly validated, particularly during clinical tachycardias. OBJECTIVE: The purpose of this study was to evaluate the diagnostic performance of voltage thresholds for identifying regions of slow conduction during reentrant atrial tachycardias (ATs). METHODS: Thirty bipolar voltage and activation maps created during reentrant ATs were analyzed to (1) examine the relationship between voltage amplitude and conduction velocity (CV), (2) measure the diagnostic ability of voltage thresholds to predict CV, and (3) identify determinants of AT circuit dimensions. Voltage amplitude was categorized as "normal" (>0.50 mV), "abnormal" (0.05-0.50 mV), or "scar" (<0.05 mV); slow conduction was defined as <30 cm/s. RESULTS: A total of 266,457 corresponding voltage and CV data points were included for analysis. Voltage and CV were moderately correlated (r = 0.407; P < .001). Bipolar voltage predicted regions of slow conduction with an area under the receiver operating characteristic curve of 0.733 (95% confidence interval 0.731-0.735). A threshold of 0.50 mV had 91% sensitivity and 35% specificity for identifying slow conduction, whereas 0.05 mV had 36% sensitivity and 87% specificity, with an optimal voltage threshold of 0.15 mV. Analyses restricted to the AT circuits identified weaker associations between voltage and CV and an optimal voltage threshold of 0.25 mV. CONCLUSION: Widely used bipolar voltage amplitude thresholds to define "abnormal" and "scar" tissue in the atria are, respectively, sensitive and specific for identifying regions of slow conduction during reentrant ATs. However, overall, the association of voltage with CV is modest. No clinical predictors of AT circuit dimensions were identified.

Accuracy of automatic abnormal potential annotation for substrate identification in scar-related ventricular tachycardia.

INTRODUCTION: Ultrahigh-density mapping for ventricular tachycardia (VT) is increasingly used. However, manual annotation of local abnormal ventricular activities (LAVAs) is challenging in this setting. Therefore, we assessed the accuracy of the automatic annotation of LAVAs with the Lumipoint algorithm of the Rhythmia system (Boston Scientific). METHODS AND RESULTS: One hundred consecutive patients undergoing catheter ablation of scar-related VT were studied. Areas with LAVAs and ablation sites were manually annotated during the procedure and compared with automatically annotated areas using the Lumipoint features for detecting late potentials (LP), fragmented potentials (FP), and double potentials (DP). The accuracy of each automatic annotation feature was assessed by re-evaluating local potentials within automatically annotated areas. Automatically annotated areas matched with manually annotated areas in 64 cases (64%), identified an area with LAVAs missed during manual annotation in 15 cases (15%), and did not highlight areas identified with manual annotation in 18 cases (18%). Automatic FP annotation accurately detected LAVAs regardless of the cardiac rhythm or scar location; automatic LP annotation accurately detected LAVAs in sinus rhythm, but was affected by the scar location during ventricular pacing; automatic DP annotation was not affected by the mapping rhythm, but its accuracy was suboptimal when the scar was located on the right ventricle or epicardium. CONCLUSION: The Lumipoint algorithm was as/more accurate than manual annotation in 79% of patients. FP annotation detected LAVAs most accurately regardless of mapping rhythm and scar location. The accuracy of LP and DP annotations varied depending on mapping rhythm or scar location.

Local abnormal ventricular activity detection in scar-related VT: Microelectrode versus conventional bipolar electrode.

BACKGROUND: Conventional bipolar electrodes (CBE) may be suboptimal to detect local abnormal ventricular activities (LAVAs). Microelectrodes (ME) may improve the detection of LAVAs. This study sought to elucidate the detectability of LAVAs using ME compared with CBE in patients with scar-related ventricular tachycardia (VT). METHODS: We included consecutive patients with structural heart disease who underwent radiofrequency catheter ablation for scar-related VT using either of the following catheters equipped with ME: QDOTTM or IntellaTip MIFITM. Detection field of LAVA potentials were classified as three types: Type 1 (both CBE and ME detected LAVA), Type 2 (CBE did not detect LAVA while ME did), and Type 3 (CBE detected LAVA while ME did not). RESULTS: In 16 patients (68 ± 16 years; 14 males), 260 LAVAs electrograms (QDOT = 72; MIFI = 188) were analyzed. Type 1, type 2, and type 3 detections were 70.8% (QDOT, 69.4%; MIFI, 71.3%), 20.0% (QDOT, 23.6%; MIFI, 18.6%) and 9.2% (QDOT, 6.9%; MIFI, 10.1%), respectively. The LAVAs amplitudes detected by ME were higher than those detected by CBE in both catheters (QDOT: ME 0.79 ± 0.50 mV vs. CBE 0.41 ± 0.42 mV, p = .001; MIFI: ME 0.73 ± 0.64 mV vs. CBE 0.38 ± 0.36 mV, p < .001). CONCLUSIONS: ME allow to identify 20% of LAVAs missed by CBE. ME showed higher amplitude LAVAs than CBE. However, 9.2% of LAVAs can still be missed by ME.