Drivers in AF colocate to sites of electrogram organization and rapidity: Potential synergy between spectral analysis and STAR mapping approaches in prioritizing drivers for ablation

Coronary sinus electrogram characteristics predict termination of AF with ablation and long-term clinical outcome

INTRODUCTION

Markers predicting atrial fibrillation (AF) termination and freedom from AF/atrial tachycardia (AT) has been proposed. This study aimed to evaluate the role of novel coronary sinus (CS) electrogram characteristics in predicting the acute ablation response and freedom from AF/AT during follow-up.

METHODS

Patients undergoing ablation for persistent AF as part of the Stochastic Trajectory Analysis of Ranked signals mapping study were included. Novel CS electrogram characteristics including CS cycle length variability (CLV) and CS activation pattern stability (APS) and proportion of low voltage zones (LVZs) were reviewed as potential predictors for AF termination on ablation and freedom from AF/AT during follow-up. The relationship between localized driver characteristics and CS electrogram characteristics was also assessed.

RESULTS

Sixty-five patients were included. AF termination was achieved in 51 patients and 80% of patients were free from AF/AT during a follow-up of 29.5 ± 3.7 months. CS CLV of <30 ms, CS APS of ≥30% and proportion of LVZ < 30% showed high diagnostic accuracy in predicting AF termination on ablation and freedom from AF/AT during follow-up (CS CLV odds ratio [OR] 25.6, area under the curve [AUC] 0.91; CS APS OR 15.9, AUC 0.94; proportion of LVZs OR 21.4, AUC 0.88). These markers were independent predictors of AF termination on ablation and AF/AT recurrence during follow-up. Ablation of a smaller number of drivers that demonstrate greater dominance strongly correlate with greater CS organization.

CONCLUSION

Novel CS electrogram characteristics were independent predictors of AF termination and AF/AT recurrence during follow-up. These markers can potentially aid in predicting outcomes and guide ablation and follow-up strategies.

Cycle Length Evaluation in Persistent Atrial Fibrillation Using Kernel Density Estimation to Identify Transient and Stable Rapid Atrial Activity

Purpose

Left atrial (LA) rapid AF activity has been shown to co-localise with areas of successful atrial fibrillation termination by catheter ablation. We describe a technique that identifies rapid and regular activity.

Methods

Eight-second AF electrograms were recorded from LA regions during ablation for psAF. Local activation was annotated manually on bipolar signals and where these were of poor quality, we inspected unipolar signals. Dominant cycle length (DCL) was calculated from annotation pairs representing a single activation interval, using a probability density function (PDF) with kernel density estimation. Cumulative annotation duration compared to total segment length defined electrogram quality. DCL results were compared to dominant frequency (DF) and averaging.

Results

In total 507 8 s AF segments were analysed from 7 patients. Spearman’s correlation coefficient was 0.758 between independent annotators (P < 0.001), 0.837–0.94 between 8 s and ≥ 4 s segments (P < 0.001), 0.541 between DCL and DF (P < 0.001), and 0.79 between DCL and averaging (P < 0.001). Poorer segment organization gave greater errors between DCL and DF.

Conclusion

DCL identifies rapid atrial activity that may represent psAF drivers. This study uses DCL as a tool to evaluate the dynamic, patient specific properties of psAF by identifying rapid and regular activity. If automated, this technique could rapidly identify areas for ablation in psAF.

Unsupervised Classification of Atrial Electrograms for Electroanatomic Mapping of Human Persistent Atrial Fibrillation

OBJECTIVE

Ablation treatment for persistent atrial fibrillation (persAF) remains challenging due to the absence of a 'ground truth' for atrial substrate characterization and the presence of multiple mechanisms driving the arrhythmia. We implemented an unsupervised classification to identify clusters of atrial electrograms (AEGs) with similar patterns, which were then validated by AEG-derived markers.

METHODS

956 bipolar AEGs were collected from 11 persAF patients. CARTO variables (Biosense Webster; ICL, ACI and SCI) were used to create a 3D space, and subsequently used to perform an unsupervised classification with k-means. The characteristics of the identified groups were investigated using nine AEG-derived markers: sample entropy (SampEn), dominant frequency, organization index (OI), determinism, laminarity, recurrence rate (RR), peak-to-peak (PP) amplitude, cycle length (CL), and wave similarity (WS).

RESULTS

Five AEG classes with distinct characteristics were identified (F = 582, P<0.0001). The presence of fractionation increased from class 1 to 5, as reflected by the nine markers. Class 1 (25%) included organized AEGs with high WS, determinism, laminarity, and RR, and low SampEn. Class 5 (20%) comprised fractionated AEGs with in low WS, OI, determinism, laminarity, and RR, and in high SampEn. Classes 2 (12%), 3 (13%) and 4 (30%) suggested different degrees of AEG organization.

CONCLUSIONS

Our results expand and reinterpret the criteria used for automated AEG classification. The nine markers highlighted electrophysiological differences among the five classes found by the k-means, which could provide a more complete characterization of persAF substrate during ablation target identification in future clinical studies.

An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis

Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance.