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Aims Repetitive conduction patterns in atrial fibrillation (AF) may reflect anatomical structures harbouring preferential conduction paths and indicate the presence of stationary sources for AF. Recently, we demonstrated a novel technique to detect repetitive patterns in high-density contact mapping of AF. As a first step towards repetitive pattern mapping to guide AF ablation, we determined the incidence, prevalence, and trajectories of repetitive conduction patterns in epicardial contact mapping of paroxysmal and persistent AF patients. Methods and results A 256-channel mapping array was used to record epicardial left and right AF electrograms in persistent AF (persAF, n = 9) and paroxysmal AF (pAF, n = 11) patients. Intervals containing repetitive conduction patterns were detected using recurrence plots. Activation movies, preferential conduction direction, and average activation sequence were used to characterize and classify conduction patterns. Repetitive patterns were identified in 33/40 recordings. Repetitive patterns were more prevalent in pAF compared with persAF [pAF: median 59%, inter-quartile range (41–72) vs. persAF: 39% (0–51), P Conclusion High-density epicardial contact mapping in AF patients reveals frequent repetitive conduction patterns. In persistent AF patients, repetitive patterns were less frequent, smaller, and more variable than in paroxysmal AF patients. Future research should elucidate whether these patterns can help in finding AF ablation targets.

Background Adenosine can be used to reveal dormant pulmonary vein (PV) conduction after PV isolation (PVI). This study presents a subanalysis of real-world 1-year follow-up data from the ESC-EHRA EORP Atrial Fibrillation (AF) Ablation Long-Term registry to analyze the usage of adenosine during PVI treatment in terms of rhythm outcome and safety. Methods The registry consists of 104 participating centers in 27 countries within the European Society of Cardiology. The registry data was split into an adenosine group (AG) and no-adenosine group (NAG). Procedure characteristics and patient outcome were compared. Results Adenosine was administered in 10.8% of the 3591 PVI patients included in the registry. Spain, the Netherlands, and Italy included the majority of adenosine cases (48.8%). Adenosine was applied more often in combination with open irrigation radiofrequency (RF) energy (74.7%) and less often in combination with nonirrigated RF energy (1.6%). After 1 year, a higher percentage of the AG was free from AF compared with the NAG (68.9% vs 59.1%, p p p = 0.991). Conclusions The use of adenosine was associated with a better rhythm outcome after 1 year follow-up and seems more useful in patients treated with RF energy compared with patients treated with cryo energy. Given the improved rhythm outcome at 1-year follow-up, it seems reasonable to encourage the use of adenosine during RF AF ablation.

Aims We aimed to examine whether routine pulmonary vein isolation (PVI) induces significant ventricular repolarization changes as suggested earlier. Methods and results Five-minute electrocardiograms were recorded at hospital’s admission (T−1d), 1 day after the PVI-procedure (T+1d) and at 3 months post-procedure (T+3m) from a registry of consecutive atrial fibrillation (AF) patients scheduled for routine PVI with different PVI modalities (radiofrequency, cryo-ablation, and hybrid). Only patients who were in sinus rhythm at all three recordings (n = 117) were included. QT-intervals and QT-dispersion were evaluated with custom-made software and QTc was calculated using Bazett’s, Fridericia’s, Framingham’s, and Hodges’ formulas. Both QT- and RR-intervals were significantly shorter at T+1d (399 ± 37 and 870 ± 141 ms) and T+3m (407 ± 36 and 950 ± 140 ms) compared with baseline (417 ± 36 and 1025 ± 164 ms). There was no statistically significant within-subject difference in QTc Fridericia (T−1d 416 ± 28 ms, T+1d 419 ± 33 ms, and T+3m 414 ± 25 ms) and QT-dispersion (T−1d 18 ± 12 ms, T+1d 21 ± 19 ms, and T+3m 17 ± 12 ms) between the recordings. A multiple linear regression model with age, sex, AF type, ablation technique, first/re-do ablation, and AF recurrence to predict the change in QTc at T+3m with respect to QTc at T−1d did not reach significance which indicates that the change in QTc does not differ between all subgroups (age, sex, AF type, ablation technique, first/re-do ablation, and AF recurrence). Conclusion Based on our data a routine PVI does not result in a prolongation of QTc in a real-world population. These findings, therefore, suggest that there is no need to intensify post-PVI QT-interval monitoring.

Propagation of Atrial Activity during atrial fibrillation (AF) is a complex phenomenon characterized by a certain degree of recurrence (periodic repetition). In this study, we investigated the possibility to detect recurrence noninvasively from body surface potential map recordings in patients affected by persistent AF, and localize this recurrence both in time and space. Results showed that clusters of recurrence can be identified from body surface recordings in these patients. Moreover, the number of clusters detected and their location on the top-right of the back of the torso were significantly associated with AF recurrence 4 to 6 weeks after electrical cardioversion. This suggests that noninvasive quantification of recurrence in persistent AF patients is possible, and may contribute to improve patient stratification.

BackgroundThe course of the disease in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in mechanically ventilated patients is unknown. To unravel the clinical heterogeneity of the SARS-CoV-2 infection in these patients, we designed the prospective observational Maastricht Intensive Care COVID cohort;MaastrICCht. We incorporated serial measurements that harbour aetiological, diagnostic and predictive information. The study aims to investigate the heterogeneity of the natural course of critically ill patients with SARS-CoV-2 infection.Study populationMechanically ventilated patients admitted to the Intensive Care with SARS- CoV-2 infection.Main messageWe will collect clinical variables, vital parameters, laboratory variables, mechanical ventilator settings, chest electrical impedance tomography, electrocardiograms, echocardiography as well as other imaging modalities to assess heterogeneity of the natural course of SARS-CoV-2 infection in critically ill patients. TheMaastrICChtcohort is, also designed to foster various other studies and registries and intends to create an open-source database for investigators. Therefore, a major part of the data collection is aligned with an existing national Intensive Care data registry and two international COVID-19 data collection initiatives. Additionally, we create a flexible design, so that additional measures can be added during the ongoing study based on new knowledge obtained from the rapidly growing body of evidence.ConclusionThe spread of the COVID-19 pandemic requires the swift implementation of observational research to unravel heterogeneity of the natural course of the disease of SARS- CoV-2 infection in mechanically ventilated patients. Our design is expected to enhance aetiological, diagnostic and prognostic understanding of the disease. This paper describes the design of theMaastrICChtcohort.Strengths and limitations of this studySerial measurements that characterize the disease course of SARS-CoV-2 infection in mechanically ventilated patientsData collection and analysis according to a predefined protocolFlexible, evolving design enabling the study of multiple aspects of SARS-CoV-2 infection in mechanically ventilated patientsSingle centre, including only ICU patients

High frequency oscillations (HFOs, 80–500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting., Highlights • Cross-validation signal space separation and beamformer increase the SNR in MEG. • Automatic detection of MEG ripples in the time domain is feasible. • Our method identifies ripples with minimal user effort and is clinically applicable. • Automatically detected ripples are concordant with MEG spikes in 14/16 patients. • Automatically detected ripples are concordant with resection area in 6/8 patients.