(2Z)-N-(4-Meth­oxy­phen­yl)-2-(4-meth­oxy­phenyl­imino)-2H-1,4-benzoxazin-3-amine

In the crystal structure of the title compound, C22H19N3O3, inter­molecular C—H⋯O hydrogen bonds link the mol­ecules into a zigzag chain parallel to the face diagonal of the ac plane. The meth­oxy phenyl rings make a dihdral angle of 32.38 (7)° and form dihedral angles of 0.66 (8) and 24.17 (7)° with the fused benzooxazine ring system

Discrepancy Between LGE-MRI and Electro-Anatomical Mapping for Regional Detection of Pathological Atrial Substrate

Atrial fibrillation (AF) is the most common sustained arrhythmia posing a significant burden to patients and leading to an increased risk of stroke and heart failure. Additional ablation of areas of arrhythmogenic substrate in the atrial body detected by either late gadolinium enhancement magnetic resonance imaging (LGE-MRI) or electroanatomical mapping (EAM) may increase the success rate of restoring and maintaining sinus rhythm compared to the standard treatment procedure of pulmonary vein isolation (PVI). To evaluate if LGE-MRI and EAM identify equivalent substrate as potential ablation targets, we divided the left atrium (LA) into six clinically important regions in ten patients. Then, we computed the correlation between both modalities by analyzing the regional extents of identified pathological tissue. In this regional analysis, we observed no correlation between late gadolinium enhancement (LGE) and low voltage areas (LVA), neither in any region nor with regard to the entire atrial surface (-0.3<r<0.3). Instead, the regional extents identified as pathological tissue varied significantly between both modalities. An increased extent of LVA compared to LGE was observed in the septal wall of the LA ($\tilde{a}_{sept}.,_{LVA}$= 19.63% and $\tilde{a}_{sept.,LGE}$= 3.94%, with = median of the extent of pathological tissue in the corresponding region). In contrast, in the inferior and lateral wall, the extent of LGE was higher than the extent of LVA for most geometries ($\tilde{a}_{inf.,LGE}$= 27.22% and $\tilde{a}_{lat.,LGE}$= 32.70% compared to $\tilde{a}_{inf.,LVA}$= 9.21% and $\tilde{a}_{lat.,LVA}$= 6.69%). Since both modalities provided discrepant results regarding the detection of arrhythmogenic substrate using clinically established thresholds, further investigations regarding their constraints need to be performed in order to use these modalities for patient stratification and treatment planning

Comparison of Unipolar and Bipolar Voltage Mapping for Localization of Left Atrial Arrhythmogenic Substrate in Patients With Atrial Fibrillation

Background: Presence of left atrial low voltage substrate in bipolar voltage mapping is associated with increased arrhythmia recurrences following pulmonary vein isolation for atrial fibrillation (AF). Besides local myocardial fibrosis, bipolar voltage amplitudes may be influenced by inter-electrode spacing and bipole-to-wavefront-angle. It is unclear to what extent these impact low voltage areas (LVA) in the clinical setting. Alternatively, unipolar electrogram voltage is not affected by these factors but requires advanced filtering. Objectives: To assess the relationship between bipolar and unipolar voltage mapping in sinus rhythm (SR) and AF and identify if the electrogram recording mode affects the quantification and localization of LVA. Methods: Patients (n = 28, 66±7 years, 46% male, 82% persistent AF, 32% redo-procedures) underwent high-density (>1,200 sites, 20 ± 10 sites/cm2, using a 20-pole 2-6-2 mm-spaced Lasso) voltage mapping in SR and AF. Bipolar LVA were defined using four different thresholds described in literature: <0.5 and <1 mV in SR, <0.35 and <0.5 mV in AF. The optimal unipolar voltage threshold resulting in the highest agreement in both unipolar and bipolar mapping modes was determined. The impact of the inter-electrode distance (2 vs. 6 mm) on the correlation was assessed. Regional analysis was performed using an 11-segment left atrial model. Results: Patients had relevant bipolar LVA (23 ± 23 cm$^{2}$ at <0.5 mV in SR and 42 ± 26 cm2 at <0.5 mV in AF). 90 ± 5% (in SR) and 85 ± 5% (AF) of mapped sites were concordantly classified as high or low voltage in both mapping modes. Discordant mapping sites located to the border zone of LVA. Bipolar voltage mapping using 2 vs. 6 mm inter-electrode distances increased the portion of matched mapping points by 4%. The unipolar thresholds (y) which resulted in a high spatial concordance can be calculated from the bipolar threshold (x) using following linear equations: y = 1.06x + 0.26mV (r = 0.994) for SR and y = 1.22x + 0.12mV (r = 0.998) for AF. Conclusion: Bipolar and unipolar voltage maps are highly correlated, in SR and AF. While bipole orientation and inter-electrode spacing are theoretical confounders, their impact is unlikely to be of clinical importance for localization of LVA, when mapping is performed at high density with a 20-polar Lasso catheter

Personalized ablation vs. conventional ablation strategies to terminate atrial fibrillation and prevent recurrence

Aims The long-term success rate of ablation therapy is still sub-optimal in patients with persistent atrial fibrillation (AF), mostly due to arrhythmia recurrence originating from arrhythmogenic sites outside the pulmonary veins. Computational modelling provides a framework to integrate and augment clinical data, potentially enabling the patient-specific identification of AF mechanisms and of the optimal ablation sites. We developed a technology to tailor ablations in anatomical and functional digital atrial twins of patients with persistent AF aiming to identify the most successful ablation strategy. Methods and results Twenty-nine patient-specific computational models integrating clinical information from tomographic imaging and electro-anatomical activation time and voltage maps were generated. Areas sustaining AF were identified by a personalized induction protocol at multiple locations. State-of-the-art anatomical and substrate ablation strategies were compared with our proposed Personalized Ablation Lines (PersonAL) plan, which consists of iteratively targeting emergent high dominant frequency (HDF) regions, to identify the optimal ablation strategy. Localized ablations were connected to the closest non-conductive barrier to prevent recurrence of AF or atrial tachycardia. The first application of the HDF strategy had a success of >98% and isolated only 5–6% of the left atrial myocardium. In contrast, conventional ablation strategies targeting anatomical or structural substrate resulted in isolation of up to 20% of left atrial myocardium. After a second iteration of the HDF strategy, no further arrhythmia episode could be induced in any of the patient-specific models. Conclusion The novel PersonAL in silico technology allows to unveil all AF-perpetuating areas and personalize ablation by leveraging atrial digital twins

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 Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure

AIMS: Atrial fibrillation (AF) and heart failure often co-exist. Early identification of AF patients at risk for AF-induced heart failure (AF-HF) is desirable to reduce both morbidity and mortality as well as health care costs. We aimed to leverage the characteristics of beat-to-beat-patterns in AF to prospectively discriminate AF patients with and without AF-HF. METHODS: A dataset of 10,234 5-min length RR-interval time series derived from 26 AF-HF patients and 26 control patients was extracted from single-lead Holter-ECGs. A total of 14 features were extracted, and the most informative features were selected. Then, a decision tree classifier with 5-fold cross-validation was trained, validated, and tested on the dataset randomly split. The derived algorithm was then tested on 2,261 5-min segments from six AF-HF and six control patients and validated for various time segments. RESULTS: The algorithm based on the spectral entropy of the RR-intervals, the mean value of the relative RR-interval, and the root mean square of successive differences of the relative RR-interval yielded an accuracy of 73.5%, specificity of 91.4%, sensitivity of 64.7%, and PPV of 87.0% to correctly stratify segments to AF-HF. Considering the majority vote of the segments of each patient, 10/12 patients (83.33%) were correctly classified. CONCLUSION: Beat-to-beat-analysis using a machine learning classifier identifies patients with AF-induced heart failure with clinically relevant diagnostic properties. Application of this algorithm in routine care may improve early identification of patients at risk for AF-induced cardiomyopathy and improve the yield of targeted clinical follow-up

Validating left atrial fractionation and low-voltage substrate during atrial fibrillation and sinus rhythm-A high-density mapping study in persistent atrial fibrillation

Altres ajuts: Deutsche Herzstiftung (German Heart Foundation).Background: Low-voltage-substrate (LVS)-guided ablation for persistent atrial fibrillation (AF) has been described either in sinus rhythm (SR) or AF. Prolonged fractionated potentials (PFPs) may represent arrhythmogenic slow conduction substrate and potentially co-localize with LVS. We assess the spatial correlation of PFP identified in AF (PFP-AF) to those mapped in SR (PFP-SR). We further report the relationship between LVS and PFPs when mapped in AF or SR. Materials and methods: Thirty-eight patients with ablation naïve persistent AF underwent left atrial (LA) high-density mapping in AF and SR prior to catheter ablation. Areas presenting PFP-AF and PFP-SR were annotated during mapping on the LA geometry. Low-voltage areas (LVA) were quantified using a bipolar threshold of 0.5 mV during both AF and SR mapping. Concordance of fractionated potentials (CFP) (defined as the presence of PFPs in both rhythms within a radius of 6 mm) was quantified. Spatial distribution and correlation of PFP and CFP with LVA were assessed. The predictors for CFP were determined. Results: PFPs displayed low voltages both during AF (median 0.30 mV (Q1-Q3: 0.20-0.50 mV) and SR (median 0.35 mV (Q1-Q3: 0.20-0.56 mV). The duration of PFP-SR was measured at 61 ms (Q1-Q3: 51-76 ms). During SR, most PFP-SRs (89.4 and 97.2%) were located within LVA (40%), followed by posterior LA (>20%) and septal LA (>15%). The extent of LVA 80%) fractionation concordance in AF and SR. Conclusion: Substrate mapping in SR vs. AF reveals smaller areas of low voltage and fewer sites with PFP. PFP-SR are located within low-voltage areas in SR. There is a high degree of spatial agreement (80%) between PFP-AF and PFP-SR in patients with moderate LVA in SR (>16% of LA surface). These findings should be considered when substrate-based ablation strategies are applied in patients with the left atrial low-voltage substrate with recurrent persistent AF