Quantifying intracardiac organization of atrial arrhythmias using temporospatial phase of the electrocardiogram

Relationships Between Atrial Flutter and Fibrillation: The Border Zone

Atrial flutter and fibrillation have been inextricably linked in the study of electrophysiology. With astute clinical observation, advanced diagnostic equipment in the Electrophysiology Laboratory, and thoughtful study of animal models, the mechanism and inter-relationship between the 2 conditions have been elucidated and will be reviewed in this article. Though diagnosis and management of these conditions have many similarities, the mechanisms by which they develop and persist are quite unique.

Electrocardiographic spatial loops indicate organization of atrial fibrillation minutes before ablation-related transitions to atrial tachycardia

BACKGROUND

During ablation for atrial fibrillation (AF), it is challenging to anticipate transitions to organized tachycardia (AT). Defining indices of this transition may help to understand fibrillatory conduction and help track therapy.

OBJECTIVE

To determine the timescale over which atrial fibrillation (AF) organizes en route to atrial tachycardia (AT) using the ECG referenced to intracardiac electrograms.

METHODS

In 17 AF patients at ablation (58.7±9.6years; 53% persistent AF) we analyzed spatial loops of atrial activity on the ECG and intracardiac electrograms over successive timepoints. Loops were tracked at precisely 15, 10, 5, 3 and 1min prior to defined transitions of AF to AT.

RESULTS

Organizational indices reliably quantified changes from AF to AT. Spatiotemporal AF organization on the ECG was identifiable at least 15min before AT was established (p=0.02).

CONCLUSIONS

AF shows anticipatory global organization on the ECG minutes before AT is clinically evident. These results offer a foundation to establish when AF therapy is on an effective path, and for a quantitative classification separating AT from AF.

Atrial Tachycardias After Atrial Fibrillation Ablation Manifest Different Waveform Characteristics: Implications for Characterizing Tachycardias

INTRODUCTION

Atrial fibrillation (AF) ablation patients often manifest atrial tachycardias (AT) with atypical ECG morphologies that preclude accurate localization and mechanism. Diagnostic maneuvers used to define ATs during electrophysiology studies can be limited by tachycardia termination or transformation. Additional methods of characterizing post-AF ablation ATs are required.

METHODS AND RESULTS

We evaluated the utility of noninvasive ECG signal analytics in postablation AF patients for the following features: (1) Localization of ATs (i.e., right vs. left atrium), and (2) Identification of common left AT mechanisms (i.e., focal vs. macroreentrant). Atrial waveforms from the surface ECG were used to analyze (1) spectral organization, including dominant amplitude (DA) and mean spectral profile (MP), and (2) temporospatial variability, using temporospatial correlation coefficients. We studied 94 ATs in 71 patients who had undergone prior pulmonary vein isolation for AF and returned for a second ablation: (1) right atrial cavotricuspid-isthmus dependent (CTI) ATs (n = 21); (2) left atrial macroreentrant ATs (n = 41) and focal ATs (n = 32). Right CTI ATs manifested higher DAs and lower MPs than left ATs, indicative of greater stability and less complexity in the frequency spectrum. Left macroreentrant ATs possessed higher temporospatial organization than left focal ATs.

CONCLUSIONS

Noninvasively recorded atrial waveform signal analyses show that right ATs possess more stable activation properties than left ATs, and left macroreentrant ATs manifest higher temporospatial organization than left focal ATs. Further prospective analyses evaluating the role these novel ECG-derived tools can play to help localize and identify mechanisms of common ATs in AF ablation patients are warranted.

Accurate ECG diagnosis of atrial tachyarrhythmias using quantitative analysis: a prospective diagnostic and cost-effectiveness study

Quantitative ECG Analysis.

INTRODUCTION

Optimal atrial tachyarrhythmia management is facilitated by accurate electrocardiogram interpretation, yet typical atrial flutter (AFl) may present without sawtooth F-waves or RR regularity, and atrial fibrillation (AF) may be difficult to separate from atypical AFl or rapid focal atrial tachycardia (AT). We analyzed whether improved diagnostic accuracy using a validated analysis tool significantly impacts costs and patient care.

METHODS AND RESULTS

We performed a prospective, blinded, multicenter study using a novel quantitative computerized algorithm to identify atrial tachyarrhythmia mechanism from the surface ECG in patients referred for electrophysiology study (EPS). In 122 consecutive patients (age 60 ± 12 years) referred for EPS, 91 sustained atrial tachyarrhythmias were studied. ECGs were also interpreted by 9 physicians from 3 specialties for comparison and to allow healthcare system modeling. Diagnostic accuracy was compared to the diagnosis at EPS. A Markov model was used to estimate the impact of improved arrhythmia diagnosis. We found 13% of typical AFl ECGs had neither sawtooth flutter waves nor RR regularity, and were misdiagnosed by the majority of clinicians (0/6 correctly diagnosed by consensus visual interpretation) but correctly by quantitative analysis in 83% (5/6, P = 0.03). AF diagnosis was also improved through use of the algorithm (92%) versus visual interpretation (primary care: 76%, P < 0.01). Economically, we found that these improvements in diagnostic accuracy resulted in an average cost-savings of $1,303 and 0.007 quality-adjusted-life-years per patient.

CONCLUSIONS

Typical AFl and AF are frequently misdiagnosed using visual criteria. Quantitative analysis improves diagnostic accuracy and results in improved healthcare costs and patient outcomes.

Manifestation of left atrial events and interatrial frequency gradients in the surface electrocardiogram during atrial fibrillation: contributions from posterior leads

BACKGROUND

In most patients, atrial fibrillation (AF) is initiated and maintained by pulmonary vein foci, but the relationship between left atrial (LA) events and the surface electrocardiogram (ECG) is largely unknown. We investigated whether LA events are reflected in the surface ECG and whether additional information can be obtained from recording posterior leads in patients with AF.

METHODS AND RESULTS

In 10 patients undergoing radiofrequency ablation of AF, we identified 103 5-second segments with a significant frequency gradient between right (RA) and left (LA) intraatrial electrograms, or with frequency changes from segment to segment in the same patient. QRS-T cancellation methods were used to isolate atrial activity in the surface ECG and peak frequencies were computed. Peak frequencies of different posterior leads were very similar (6.0 +/- 1.3 Hz for V10, 6.0 +/- 0.9 Hz for V9, 5.9 +/- 1.4 Hz for V8, 6.0 +/- 1.3 Hz for V7). We found a strong correlation between V1 and RA and between V9 and LA, 0.89 and 0.88, respectively, while the lowest correlation was found between lead V1 and LA, 0.62, P < 0.0001. Magnitude-squared coherence values were highest between V1 and RA and between V9 and LA.

CONCLUSION

We have demonstrated that, by recording additional surface ECG leads from posterior locations, RA and LA electrical events and interatrial frequency gradients can be monitored noninvasively.

Using electrocardiographic activation time and diastolic intervals to separate focal from macro-re-entrant atrial tachycardias

OBJECTIVES

This study was designed to separate focal from atypical macro-re-entrant atrial tachycardia (AT) on the electrocardiogram (ECG).

BACKGROUND

Focal AT often cannot be distinguished from macro-re-entrant AT until the time of electrophysiology study (EPS). We hypothesized that quantitative ECG metrics should separate focal AT, using its short activation relative to tachycardia cycle length (CL), from macro-re-entrant AT, whose activation should span the CL. We developed tools to accurately quantify CL and P- or F-wave duration even when overlying T waves, then prospectively applied them to patients during focal or macro-re-entrant AT ablation and compared them to the gold standard EPS diagnosis.

METHODS

We studied 41 patients (27 men, 14 women) age 57 +/- 17 years. In the training group (n = 20), tachycardia P or F waves overlying T waves were identified from transitions in slope (dV/dt) relative to "expected" T waves generated from scaling of the sinus-rate T-wave. Electrocardiographic P-wave duration agreed with the duration of intra-atrial activation. Autocorrelation was used to estimate ECG atrial CL (p < 0.001).

RESULTS

Compared to macro-re-entry (n = 13), focal AT (n = 7) had shorter P waves (115 +/- 31 ms vs. 227 +/- 67 ms; p < 0.001) that were smaller ratios of CL (28 +/- 7% vs. 85 +/- 21%; p < 0.001). Receiver-operating characteristic curve areas for AT were 0.92 for P(F)-wave duration and 0.99 for P(F)/CL ratio. On blinded prospective analysis (n = 21), P(F)-wave duration <160 ms identified focal (n = 7) from macro-re-entrant AT (n = 14) with 90% sensitivity and 90% specificity, and a P(F)/CL ratio <45% gave 86% sensitivity and 98% specificity.

CONCLUSIONS

Quantitative ECG indexes of shorter atrial activation and longer diastolic interval separate focal from macro-re-entrant AT without diagnostic maneuvers.

Separating atrial flutter from atrial fibrillation with apparent electrocardiographic organization using dominant and narrow F-wave spectra

OBJECTIVES

The purpose of this study was to separate atrial flutter (AFL) with atypical F waves from fibrillation (AF) with "apparent organization."

BACKGROUND

We hypothesized that F-wave spectra should reveal a dominant and narrow peak in AFL, reflecting its single macro-re-entrant wave front, but broad spectra in AF, reflecting multiple wave fronts.

METHODS

We identified 39 patients with electrocardiograms (ECGs) of "AFL/AF" or "coarse AF" from 134 consecutive patients referred for ablation: 21 had AFL (18 atypical, 3 typical), 18 had AF, and all were successfully ablated. Filtered atrial ECGs were created by cross-correlating F waves to successive ECG time points. Dominant peaks between 3 and 10 Hz were identified from power spectra of X (lead V5), Y (aVF), and Z (V1) axes, and for each, we calculated height (relative to two adjacent spectral points) and area ratio to envelopes of bandwidth 0.625, 1.25, 2.5, 3.75, and 5 Hz (range 0 to 1, where higher ratios reflect narrower peaks).

RESULTS

Dominant peaks had greater relative height for AFL than AF (three-axis mean: 14.2 +/- 6.4 dB vs. 6.6 +/- 2.1 dB; p < 0.001). Peak area ratios were also higher for AFL than AF for all envelopes (p < 0.001). For the 2.5-Hz envelope, the separation (0.61 +/- 0.14 vs. 0.35 +/- 0.05, respectively; p < 0.001) enabled a ratio > or =0.44 to identify all cases of AFL from AF (p < 0.001). A panel of seven cardiologists blinded to clinical data provided lower diagnostic accuracy (82.1%; p < 0.01).

CONCLUSIONS

In ambiguous ECGs with atypical F waves, spectral evidence for a solitary activation cycle separates AFL from AF with "apparent organization." This approach might improve bedside ECG diagnosis and shed light on intra-atrial organization of both rhythms.

Separating non-isthmus- from isthmus-dependent atrial flutter using wavefront variability

OBJECTIVES

The aim of this study was to separate isthmus-dependent atrial flutter (IDAFL) from non-isthmus-dependent atrial flutter (NIDAFL) from the electrocardiogram (ECG) based on functional differences.

BACKGROUND

The ECG analyses of F-wave shape suboptimally separate NIDAFL from IDAFL. The authors hypothesized that anatomic and functional differences may result in greater wavefront variability in NIDAFL than IDAFL, allowing their separation. The authors tested this hypothesis in patients undergoing ablation for atrial flutter using a novel ECG algorithm to detect subtle F-wave variability, validated by intracardiac measurements.

METHODS

In 62 patients (23 NIDAFL, 39 IDAFL) ECG atrial wavefronts were represented as correlations of an F-wave template to the ECG over time. Correlations in orthogonal ECG lead-pairs were plotted at each time point to yield loops reflecting temporal and spatial regularity in each plane. The ECG analyses were compared with intracardiac standard deviations of: 1) atrial electrograms (temporal variability), and 2) bi-atrial activation time differences (spatial variability).

RESULTS

Atrial ECG temporospatial loops were reproducible in IDAFL, but varied in NIDAFL (p < 0.01) suggesting greater variability that correctly classified IDAFL (39 of 39 cases) from NIDAFL (22 of 23 cases; p < 0.001). Intra-atrial mapping confirmed greater temporal variability for NIDAFL versus IDAFL, in lateral (p < 0.01) and septal (p = 0.03) right atrium, and proximal (p = 0.02) and distal (p < 0.01) coronary sinus. Spatial variability was greater in NIDAFL than IDAFL (p = 0.02).

CONCLUSIONS

Greater cycle-to-cycle atrial wavefront variability separates NIDAFL from IDAFL and is detectable from the ECG using temporospatial analyses. These results have implications for guiding ablation and support the concept that IDAFL and NIDAFL lie along a spectrum of intracardiac organization.