Prevention of cerebral thromboembolism by oral anticoagulation with dabigatran after pulmonary vein isolation for atrial fibrillation: the ODIn-AF trial.

BACKGROUND AND OBJECTIVES: Long-term oral anticoagulation (OAC) following successful catheter ablation of atrial fibrillation (AF) remains controversial. Prospective data are missing. The ODIn-AF study aimed to evaluate the effect of OAC on the incidence of silent cerebral embolic events and clinically relevant cardioembolic events in patients at intermediate to high risk for embolic events, free from AF after pulmonary vein isolation (PVI). METHODS: This prospective, randomized, multicenter, open-label, blinded endpoint interventional trial enrolled patients who were scheduled for PVI to treat paroxysmal or persistent AF. Six months after PVI, AF-free patients were randomized to receive either continued OAC with dabigatran or no OAC. The primary endpoint was the incidence of new silent micro- and macro-embolic lesions detected on brain MRI at 12 months of follow-up compared to baseline. Safety analysis included bleedings, clinically evident cardioembolic, and serious adverse events (SAE). RESULTS: Between 2015 and 2021, 200 patients were randomized into 2 study arms (on OAC: n = 99, off OAC: n = 101). There was no significant difference in the occurrence of new cerebral microlesions between the on OAC and off OAC arm [2 (2%) versus 0 (0%); P = 0.1517] after 12 months. MRI showed no new macro-embolic lesion, no clinical apparent strokes were present in both groups. SAE were more frequent in the OAC arm [on OAC n = 34 (31.8%), off OAC n = 18 (19.4%); P = 0.0460]; bleedings did not differ. CONCLUSION: Discontinuation of OAC after successful PVI was not found to be associated with an elevated risk of cerebral embolic events compared with continued OAC after a follow-up of 12 months.

Recommender system for ablation lines to treat complex atrial tachycardia.

BACKGROUND AND OBJECTIVE: Planning the optimal ablation strategy for the treatment of complex atrial tachycardia (CAT) is a time consuming task and is error-prone. Recently, directed network mapping, a technology based on graph theory, proved to efficiently identify CAT based solely on data of clinical interventions. Briefly, a directed network was used to model the atrial electrical propagation and reentrant activities were identified by looking for closed-loop paths in the network. In this study, we propose a recommender system, built as an optimization problem, able to suggest the optimal ablation strategy for the treatment of CAT. METHODS: The optimization problem modeled the optimal ablation strategy as that one interrupting all reentrant mechanisms while minimizing the ablated atrial surface. The problem was designed on top of directed network mapping. Considering the exponential complexity of finding the optimal solution of the problem, we introduced a heuristic algorithm with polynomial complexity. The proposed algorithm was applied to the data of i) 6 simulated scenarios including both left and right atrial flutter; and ii) 10 subjects that underwent a clinical routine. RESULTS: The recommender system suggested the optimal strategy in 4 out of 6 simulated scenarios. On clinical data, the recommended ablation lines were found satisfactory on 67% of the cases according to the clinician's opinion, while they were correctly located in 89%. The algorithm made use of only data collected during mapping and was able to process them nearly real-time. CONCLUSIONS: The first recommender system for the identification of the optimal ablation lines for CAT, based solely on the data collected during the intervention, is presented. The study may open up interesting scenarios for the application of graph theory for the treatment of CAT.

Acute procedural efficacy and safety of a novel cryoballoon for the treatment of paroxysmal atrial fibrillation: Results from the POLAR ICE study.

INTRODUCTION: Pulmonary vein isolation (PVI) is well established as a primary treatment for atrial fibrillation (AF). The POLAR ICE study was designed to collect prospective real world data on the safety and effectiveness of the POLARxTM cryoballoon for PVI to treat paroxysmal AF. METHODS: POLAR ICE, a prospective, non-randomized, multicenter (international) registry (NCT04250714), enrolled 399 patients across 19 European centers. Procedural characteristics, such as time to isolation, cryoablations per pulmonary vein (PV), balloon nadir temperature, and occlusion grade were recorded. PVI was confirmed with entrance block testing. RESULTS: Data on 372 de novo PVI procedures (n = 2190 ablations) were collected. Complete PVI was achieved in 96.8% of PVs. Procedure and fluoroscopy times were 68.2 ± 24.6 and 15.6 ± 9.6 min, respectively. Left atrial dwell time was 46.6 ± 18.3 min. Grade 3 or 4 occlusion was achieved in 98.2% of PVs reported and 71.2% of PVs isolation required only a single cryoablation. Of 2190 cryoapplications, 83% had a duration of at least 120 s; nadir temperature of these ablations averaged -56.3 ± 6.5°C. There were 6 phrenic nerve palsy events, 2 of which resolved within 3 months of the procedure. CONCLUSION: This real-world usage data on a novel cryoballoon suggests this device is effective, safe, and relatively fast in centers with cryoballoon experience. These data are comparable to prior POLARx reports and in keeping with reported data on other cryoballoons. Future studies should examine the long-term outcomes and the relationship between biophysical parameters and outcomes for this novel cryoballoon.

In Silico Study of Local Electrical Impedance Measurements in the Atria - Towards Understanding and Quantifying Dependencies in Human.

BACKGROUND: Electrical impedance measurements have become an accepted tool for monitoring intracardiac radio frequency ablation. Recently, the long-established generator impedance was joined by novel local impedance measurement capabilities with all electrical circuit terminals being accommodated within the catheter. OBJECTIVE: This work aims at in silico quantification of distinct influencing factors that have remained challenges due to the lack of ground truth knowledge and the superposition of effects in clinical settings. METHODS: We introduced a highly detailed in silico model of two local impedance enabled catheters, namely IntellaNav MiFi OI and IntellaNav Stablepoint, embedded in a series of clinically relevant environments. Assigning material and frequency specific conductivities and subsequently calculating the spread of the electrical field with the finite element method yielded in silico local impedances. The in silico model was validated by comparison to in vitro measurements of standardized sodium chloride solutions. We then investigated the effect of the withdrawal of the catheter into the transseptal sheath, catheter-tissue interaction, insertion of the catheter into pulmonary veins, and catheter irrigation. RESULTS: All simulated setups were in line with in vitro experiments and in human measurements and gave detailed insight into determinants of local impedance changes as well as the relation between values measured with two different devices. CONCLUSION: The in silico environment proved to be capable of resembling clinical scenarios and quantifying local impedance changes. SIGNIFICANCE: The tool can assists the interpretation of measurements in humans and has the potential to support future catheter development.

Local Impedance Drop Predicts Durable Conduction Block in Patients With Paroxysmal Atrial Fibrillation.

OBJECTIVES: This analysis was performed to evaluate the transition of local impedance (LI) drop during pulmonary vein isolation (PVI) to durable block and mature lesion formation based on 3-month mapping procedures. BACKGROUND: A radiofrequency catheter measuring LI has been shown to be effective for performing PVI in patients with paroxysmal atrial fibrillation. Previous analysis has demonstrated LI drop to be predictive of pulmonary vein segment conduction block during an atrial fibrillation ablation procedure. METHODS: Fifty-eight patients who had undergone LI-blinded de novo PVI returned for a 3-month mapping procedure. PVI ablation circles were divided into 16 anatomic segments for classification (durable block or gap), and the median LI drop within segments with an interlesion distance of ≤6 mm was compared. A total of 51 data sets met the criteria for segmental analysis of LI performance. RESULTS: At the 3-month procedure, PV connection was confirmed in at least 1 PV segment in 35 of the included patients. LI drop outperformed generator impedance drop as a predictor of durable conduction block (area under the receiver-operating characteristic curve: 0.79 vs 0.68; P = 0.003). Optimal LI drops were identified by left atrial region (anterior/superior: 16.9 Ω [sensitivity: 69.1%; specificity: 85.0%; positive predictive value for durable conduction block: 97.7%]; posterior/inferior:14.2 Ω [sensitivity: 73.8%; specificity: 78.3%; positive predictive value: 96.9%]). Starting LI before radiofrequency (RF) application was significantly different among healthy, gap, and mature scar tissue and was also a contributing factor to achieving an optimal LI drop (85.2% of RF applications with a starting LI of ≥110 Ω achieved the optimal regional drop or greater). CONCLUSIONS: LI drop is predictive of durable PV segment isolation. Preablation starting LI is associated with the magnitude of LI drop. These findings suggest that a regional approach to RF ablation guided by LI combined with careful interlesion distance control may be beneficial in patients with paroxysmal atrial fibrillation (Electrical Coupling Information From the Rhythmia HDx System and DirectSense Technology in Subjects With Paroxysmal Atrial Fibrillation [LOCALIZE]; NCT03232645).

Ablation of Left Atrial Tachycardia following Catheter Ablation of Atrial Fibrillation: 12-Month Success Rates.

The treatment of atrial tachycardia following catheter ablation of atrial fibrillation is often challenging. Electrophysiological studies using high-resolution 3D mapping systems have contributed significantly to their understanding, and new ablation approaches have shown high rates of acute terminations with low recurrences for the clinical AT. However, patient populations are very heterogeneous, and long-term data of the freedom from any atrial tachycardia or any arrhythmia are still sparse. To evaluate long-term success, a unified patient population and predefined ablation strategies are preferred. In this study, we present 12-month success and mean 30 month follow-up data of catheter ablation of left atrial tachycardia. All 35 patients had a history of pulmonary vein isolation (PVI), 71% of which had a previous substrate modification. A total of 54 ATs, with a mean cycle length 297 ± 86 ms, 31 macro-reentries, and 4 localized reentries, were targeted. The ablation strategy to be used was given by the study protocol, depending on the type of reentry and the number of critical isthmuses. All available ablation strategies were included: standard (anatomical) lines, individual lines, critical isthmuses, and focal ablation. All ATs were terminated by ablation. A total of 91% terminated upon the first ablation strategy. Freedom from any AT after 12 months was 82%, and from any arrhythmia, it was 77%. The multi-procedure success after 30 months was 65% for any AT and 55% for any arrhythmia. In conclusion, individual ablation strategies based on the reentry mechanism and the number of critical isthmuses seems promising and demonstrates a high long-term clinical success. Tachycardia comprising a single critical isthmus can be ablated by critical isthmus ablation only. These patients present with the highest 12-month and long-term success rates.

Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram.

AIMS: Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG). METHODS AND RESULTS: Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients-three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%. CONCLUSION: Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments.

Telemedical cardiac risk assessment by implantable cardiac monitors in patients after myocardial infarction with autonomic dysfunction (SMART-MI-DZHK9): a prospective investigator-initiated, randomised, multicentre, open-label, diagnostic trial.

BACKGROUND: Cardiac autonomic dysfunction after myocardial infarction identifies patients at high risk despite only moderately reduced left ventricular ejection fraction. We aimed to show that telemedical monitoring with implantable cardiac monitors in these patients can improve early detection of subclinical but prognostically relevant arrhythmic events. METHODS: We did a prospective investigator-initiated, randomised, multicentre, open-label, diagnostic trial at 33 centres in Germany and Austria. Survivors of acute myocardial infarction with left ventricular ejection fraction of 36-50% had biosignal analysis for assessment of cardiac autonomic function. Patients with abnormal periodic repolarisation dynamics (≥5·75 deg2) or abnormal deceleration capacity (≤2·5 ms) were randomly assigned (1:1) to telemedical monitoring with implantable cardiac monitors or conventional follow-up. Primary endpoint was time to detection of serious arrhythmic events defined by atrial fibrillation 6 min or longer, atrioventricular block class IIb or higher and fast non-sustained (>187 beats per min; ≥40 beats) or sustained ventricular tachycardia or fibrillation. This study is registered with ClinicalTrials.gov, NCT02594488. FINDINGS: Between May 12, 2016, and July 20, 2020, 1305 individuals were screened and 400 patients at high risk were randomly assigned (median age 64 years [IQR 57-73]); left ventricular ejection fraction 45% [40-48]) to telemedical monitoring with implantable cardiac monitors (implantable cardiac monitor group; n=201) or conventional follow-up (control group; n=199). During median follow-up of 21 months, serious arrhythmic events were detected in 60 (30%) patients of the implantable cardiac monitor group and 12 (6%) patients of the control group (hazard ratio 6·33 [IQR 3·40-11·78]; p<0·001). An improved detection rate by implantable cardiac monitors was observed for all types of serious arrhythmic events: atrial fibrillation 6 min or longer (47 [23%] patients vs 11 [6%] patients; p<0·001), atrioventricular block class IIb or higher (14 [7%] vs 0; p<0·001) and ventricular tachycardia or ventricular fibrillation (nine [4%] patients vs two [1%] patients; p=0·054). INTERPRETATION: In patients at high risk after myocardial infarction and cardiac autonomic dysfunction but only moderately reduced left ventricular ejection fraction, telemedical monitoring with implantable cardiac monitors was highly effective in early detection of subclinical, prognostically relevant serious arrhythmic events. FUNDING: German Centre for Cardiovascular Research (DZHK) and Medtronic Bakken Research Center.

Bipolar ablation of therapy-refractory ventricular arrhythmias: application of a dedicated approach.

AIMS: Bipolar radiofrequency ablation (B-RFA) has been reported as a bail-out strategy for the treatment of therapy refractory ventricular arrhythmias (VA). Currently, existing setups have not been standardized for B-RFA, while the impact of conventional B-RFA approaches on lesion formation remains unclear. METHODS AND RESULTS: (i) In a multicentre observational study, patients undergoing B-RFA for previously therapy-refractory VA using a dedicated B-RFA setup were retrospectively analysed. (ii) Additionally, in an ex vivo model lesion formation during B-RFA was evaluated using porcine hearts. In a total of 26 procedures (24 patients), acute success was achieved in all 14 ventricular tachycardia (VT) procedures and 7/12 procedures with premature ventricular contractions (PVC), with major complications occurring in 1 procedure (atrioventricular block). During a median follow-up of 211 days in 21 patients, 6/11 patients (VT) and 5/10 patients (PVC) remained arrhythmia-free. Lesion formation in the ex vivo model during energy titration from 30 to 50 W led to similar lesion volumes compared with initial high-power 50 W B-RFA. Lesion size significantly increased when combining sequential unipolar and B-RFA (1429 mm3 vs. titration 501 mm3 vs. B-RFA 50 W 423 mm3, P < 0.001), an approach used in overall 58% of procedures and more frequently applied in procedures without VA recurrence (92% vs. 36%, P = 0.009). Adipose tissue severely limited lesion formation during B-RFA. CONCLUSION: Using a dedicated device for B-RFA for therapy-refractory VA appears feasible and safe. While some patients need repeat ablation, success rates were encouraging. Sequential unipolar and B-RFA may be favourable for lesion formation.

Atrial Flutter Mechanism Detection Using Directed Network Mapping.

Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of in-silico simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and in-silico simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios.

Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset.

In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.

CVAR-Seg: An Automated Signal Segmentation Pipeline for Conduction Velocity and Amplitude Restitution.

BACKGROUND: Rate-varying S1S2 stimulation protocols can be used for restitution studies to characterize atrial substrate, ionic remodeling, and atrial fibrillation risk. Clinical restitution studies with numerous patients create large amounts of these data. Thus, an automated pipeline to evaluate clinically acquired S1S2 stimulation protocol data necessitates consistent, robust, reproducible, and precise evaluation of local activation times, electrogram amplitude, and conduction velocity. Here, we present the CVAR-Seg pipeline, developed focusing on three challenges: (i) No previous knowledge of the stimulation parameters is available, thus, arbitrary protocols are supported. (ii) The pipeline remains robust under different noise conditions. (iii) The pipeline supports segmentation of atrial activities in close temporal proximity to the stimulation artifact, which is challenging due to larger amplitude and slope of the stimulus compared to the atrial activity. METHODS AND RESULTS: The S1 basic cycle length was estimated by time interval detection. Stimulation time windows were segmented by detecting synchronous peaks in different channels surpassing an amplitude threshold and identifying time intervals between detected stimuli. Elimination of the stimulation artifact by a matched filter allowed detection of local activation times in temporal proximity. A non-linear signal energy operator was used to segment periods of atrial activity. Geodesic and Euclidean inter electrode distances allowed approximation of conduction velocity. The automatic segmentation performance of the CVAR-Seg pipeline was evaluated on 37 synthetic datasets with decreasing signal-to-noise ratios. Noise was modeled by reconstructing the frequency spectrum of clinical noise. The pipeline retained a median local activation time error below a single sample (1 ms) for signal-to-noise ratios as low as 0 dB representing a high clinical noise level. As a proof of concept, the pipeline was tested on a CARTO case of a paroxysmal atrial fibrillation patient and yielded plausible restitution curves for conduction speed and amplitude. CONCLUSION: The proposed openly available CVAR-Seg pipeline promises fast, fully automated, robust, and accurate evaluations of atrial signals even with low signal-to-noise ratios. This is achieved by solving the proximity problem of stimulation and atrial activity to enable standardized evaluation without introducing human bias for large data sets.

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG.

BACKGROUND: Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. OBJECTIVES: To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. METHODS: AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). RESULTS: The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. CONCLUSION: Machine learning-based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.

Cycle length statistics during human atrial fibrillation reveal refractory properties of the underlying substrate: a combined in silico and clinical test of concept study.

AIMS: The treatment of atrial fibrillation beyond pulmonary vein isolation has remained an unsolved challenge. Targeting regions identified by different substrate mapping approaches for ablation resulted in ambiguous outcomes. With the effective refractory period being a fundamental prerequisite for the maintenance of fibrillatory conduction, this study aims at estimating the effective refractory period with clinically available measurements. METHODS AND RESULTS: A set of 240 simulations in a spherical model of the left atrium with varying model initialization, combination of cellular refractory properties, and size of a region of lowered effective refractory period was implemented to analyse the capabilities and limitations of cycle length mapping. The minimum observed cycle length and the 25% quantile were compared to the underlying effective refractory period. The density of phase singularities was used as a measure for the complexity of the excitation pattern. Finally, we employed the method in a clinical test of concept including five patients. Areas of lowered effective refractory period could be distinguished from their surroundings in simulated scenarios with successfully induced multi-wavelet re-entry. Larger areas and higher gradients in effective refractory period as well as complex activation patterns favour the method. The 25% quantile of cycle lengths in patients with persistent atrial fibrillation was found to range from 85 to 190 ms. CONCLUSION: Cycle length mapping is capable of highlighting regions of pathologic refractory properties. In combination with complementary substrate mapping approaches, the method fosters confidence to enhance the treatment of atrial fibrillation beyond pulmonary vein isolation particularly in patients with complex activation patterns.

Local catheter impedance drop during pulmonary vein isolation predicts acute conduction block in patients with paroxysmal atrial fibrillation: initial results of the LOCALIZE clinical trial.

AIMS: Radiofrequency ablation creates irreversible cardiac damage through resistive heating and this temperature change results in a generator impedance drop. Evaluation of a novel local impedance (LI) technology measured exclusively at the tip of the ablation catheter found that larger LI drops were indicative of more effective lesion formation. We aimed to evaluate whether LI drop is associated with conduction block in patients with paroxysmal atrial fibrillation (AF) undergoing pulmonary vein isolation (PVI). METHODS AND RESULTS: Sixty patients underwent LI-blinded de novo PVI using a point-by-point ablation workflow. Pulmonary vein rings were divided into 16 anatomical segments. After a 20-min waiting period, gaps were identified on electroanatomic maps. Median LI drop within segments with inter-lesion distance ≤6 mm was calculated offline. The diagnostic accuracy of LI drop for predicting segment block was assessed using receiver operating characteristic analysis. For segments with inter-lesion distance ≤6 mm, acutely blocked segments had a significantly larger LI drop [19.8 (14.1-27.1) Ω] compared with segments with gaps [10.6 (7.8-14.7) Ω, P < 0.001). In view of left atrial wall thickness differences, the association between LI drop and block was further evaluated for anterior/roof and posterior/inferior segments. The optimal LI cut-off value for anterior/roof segments was 16.1 Ω (positive predictive value for block: 96.3%) and for posterior/inferior segments was 12.3 Ω (positive predictive value for block: 98.1%) where inter-lesion distances were ≤6 mm. CONCLUSION: The magnitude of LI drop was predictive of acute PVI segment conduction block in patients with paroxysmal AF. The thinner posterior wall required smaller LI drops for block compared with the thicker anterior wall.

Comparison of procedural efficacy and biophysical parameters between two competing cryoballoon technologies for pulmonary vein isolation: Insights from an initial multicenter experience.

INTRODUCTION: Recently a novel cryoballoon system (POLARx, Boston Scientific) became available for the treatment of atrial fibrillation. This cryoballoon is comparable with Arctic Front Advance Pro (AFA-Pro, Medtronic), however, it maintains a constant balloon pressure. We compared the procedural efficacy and biophysical characteristics of both systems. METHODS: One hundred and ten consecutive patients who underwent first-time cryoballoon ablation (POLARx: n = 57; AFA-Pro: n = 53) were included in this prospective cohort study. RESULTS: Acute isolation was achieved in 99.8% of all pulmonary veins (POLARx: 99.5% vs. AFA-Pro: 100%, p = 1.00). Total procedure time (81 vs. 67 min, p < .001) and balloon in body time (51 vs. 35 min, p < .001) were longer with POLARx. After a learning curve, these times were similar. Cryoablation with POLARx was associated with shorter time to balloon temperature -30°C (27 vs. 31 s, p < .001) and -40°C (32 vs. 54 s, p < .001), lower balloon nadir temperature (-55°C vs. -47°C, p < .001), and longer thawing time till 0°C (16 vs. 9 s, p < .001). There were no differences in time-to-isolation (TTI; POLARx: 45 s vs. AFA-Pro 43 s, p = .441), however, POLARx was associated with a lower balloon temperature at TTI (-46°C vs. -37°C, p < .001). Factors associated with acute isolation differed between groups. The incidence of phrenic nerve palsy was comparable (POLARx: 3.5% vs. AFA-Pro: 3.7%). CONCLUSION: The novel cryoballoon is comparable to AFA-Pro and requires only a short learning curve to get used to the slightly different handling. It was associated with faster cooling rates and lower balloon temperatures but TTI was similar to AFA-Pro.

Local Electrical Impedance Mapping of the Atria: Conclusions on Substrate Properties and Confounding Factors.

The treatment of atrial fibrillation and other cardiac arrhythmias as a major cause of cardiovascular hospitalization has remained a challenge predominantly for patients with severely remodeled substrate. Individualized ablation strategies are extremely important both for pulmonary vein isolation and subsequent ablations. Current approaches to identifying arrhythmogenic regions rely on electrogram-based features such as activation time and voltage. Novel technologies now enable clinical assessment of the local impedance as tissue property. Previous studies demonstrated its use for ablation monitoring and indicated its potential to differentiate healthy substrate, scar, and pathological tissue. This study investigates the potential of local electrical impedance-based substrate mapping of the atria for human in-vivo data. The presented pipeline for impedance mapping particularly contains options for dealing with undesirable effects originating from cardiac motion, catheter motion, or proximity to other intracardiac devices. Bloodpool impedance was automatically determined as a patient-specific reference. Full-chamber, left atrial impedance maps were drawn up from interpolating the measured impedances to the atrial endocardium. Finally, the origin and magnitude of oscillations of the raw impedance recording were probed into. The most dominant reason for exclusion of impedance samples was the loss of endocardial contact. With median elevations above the bloodpool impedance between 29 and 46 Ω, the impedance within the pulmonary veins significantly exceeded the remaining atrial walls presenting median elevations above the bloodpool impedance between 16 and 20 Ω. Previous ablation lesions were distinguished from their surroundings by a significant drop in local impedance while the corresponding regions did not differ for the control group. The raw impedance was found to oscillate with median amplitudes between 6 and 17 Ω depending on the patient. Oscillations were traced back to an interplay of atrial, ventricular, and respiratory motion. In summary, local impedance measurements demonstrated their capability to distinguish pathological atrial tissue from physiological substrate. Methods to limit the influence of confounding factors that still hinder impedance mapping were presented. Measurements at different frequencies or the combination of multiple electrodes could lead to further improvement. The presented examples indicate that electrogram- and impedance-based substrate mapping have the potential to complement each other toward better patient outcomes in future.