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

Volatile anaesthetics reduce neutrophil inflammatory response by interfering with CXC receptor-2 signalling

Background Growing evidence suggests a protective effect of volatile anaesthetics in ischaemia-reperfusion (I/R)-injury, and the accumulation of neutrophils is a crucial event. Pro-inflammatory cytokines carrying the C-X-C-motif including interleukin-8 (IL-8) and CXC-ligand 1 (CXCL1) activate CXC receptor-1 (CXCR1; stimulated by IL-8), CXC receptor-2 (CXCR2; stimulated by IL-8 and CXCL1), or both to induce CD11b-dependent neutrophil transmigration. Inhibition of CXCR1, CXCR2, or both reduces I/R-injury by preventing neutrophil accumulation. We hypothesized that interference with CXCR1/CXCR2 signalling contributes to the well-established beneficial effect of volatile anaesthetics in I/R-injury. Methods Isolated human neutrophils were stimulated with IL-8 or CXCL1 and exposed to volatile anaesthetics (sevoflurane/desflurane). Neutrophil migration was assessed using an adapted Boyden chamber. Expression of CD11b, CXCR1, and CXCR2 was measured by flow cytometry. Blocking antibodies against CXCR1/CXCR2/CD11b and phorbol myristate acetate were used to investigate specific pathways. Results Volatile anaesthetics reduced CD11b-dependent neutrophil transmigration induced by IL-8 by >30% and CD11b expression by 18 and 27% with sevoflurane/desflurane, respectively. This effect was independent of CXCR1/CXCR2 expression and CXCR1/CXCR2 endocytosis. Inhibition of CXCR1 signalling did not affect downregulation of CD11b with volatile anaesthetics. Blocking of CXCR2-signalling neutralized effects by volatile anaesthetics on CD11b expression. Specific stimulation of CXCR2 with CXCL1 was sufficient to induce upregulation of CD11b, which was impaired with volatile anaesthetics. No effect of volatile anaesthetics was observed with direct stimulation of protein kinase C located downstream of CXCR1/CXCR2. Conclusion Volatile anaesthetics attenuate neutrophil inflammatory responses elicited by CXC cytokines through interference with CXCR2 signalling. This might contribute to the beneficial effect of volatile anaesthetics in I/R-injur

Specific Electrogram Characteristics Identify the Extra-Pulmonary Vein Arrhythmogenic Sources of Persistent Atrial Fibrillation – Characterization of the Arrhythmogenic Electrogram Patterns During Atrial Fibrillation and Sinus Rhythm

Identification of atrial sites that perpetuate atrial fibrillation (AF), and ablation thereof terminates AF, is challenging. We hypothesized that specific electrogram (EGM) characteristics identify AF-termination sites (AFTS). Twenty-one patients in whom low-voltage-guided ablation after pulmonary vein isolation terminated clinical persistent AF were included. Patients were included if short RF-delivery for <8sec at a given atrial site was associated with acute termination of clinical persistent AF. EGM-characteristics at 21 AFTS, 105 targeted sites without termination and 105 non-targeted control sites were analyzed. Alteration of EGM-characteristics by local fibrosis was evaluated in a three-dimensional high resolution (100 µm)-computational AF model. AFTS demonstrated lower EGM-voltage, higher EGM-cycle-length-coverage, shorter AF-cycle-length and higher pattern consistency than control sites (0.49 ± 0.39 mV vs. 0.83 ± 0.76 mV, p < 0.0001; 79 ± 16% vs. 59 ± 22%, p = 0.0022; 173 ± 49 ms vs. 198 ± 34 ms, p = 0.047; 80% vs. 30%, p < 0.01). Among targeted sites, AFTS had higher EGM-cycle-length coverage, shorter local AF-cycle-length and higher pattern consistency than targeted sites without AF-termination (79 ± 16% vs. 63 ± 23%, p = 0.02; 173 ± 49 ms vs. 210 ± 44 ms, p = 0.002; 80% vs. 40%, p = 0.01). Low voltage (0.52 ± 0.3 mV) fractionated EGMs (79 ± 24 ms) with delayed components in sinus rhythm (‘atrial late potentials’, respectively ‘ALP’) were observed at 71% of AFTS. EGMs recorded from fibrotic areas in computational models demonstrated comparable EGM-characteristics both in simulated AF and sinus rhythm. AFTS may therefore be identified by locally consistent, fractionated low-voltage EGMs with high cycle-length-coverage and rapid activity in AF, with low-voltage, fractionated EGMs with delayed components/ ‘atrial late potentials’ (ALP) persisting in sinus rhythm

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

Volatile anaesthetics reduce neutrophil inflammatory response by interfering with CXC receptor-2 signalling

BACKGROUND Growing evidence suggests a protective effect of volatile anaesthetics in ischaemia-reperfusion (I/R)-injury, and the accumulation of neutrophils is a crucial event. Pro-inflammatory cytokines carrying the C-X-C-motif including interleukin-8 (IL-8) and CXC-ligand 1 (CXCL1) activate CXC receptor-1 (CXCR1; stimulated by IL-8), CXC receptor-2 (CXCR2; stimulated by IL-8 and CXCL1), or both to induce CD11b-dependent neutrophil transmigration. Inhibition of CXCR1, CXCR2, or both reduces I/R-injury by preventing neutrophil accumulation. We hypothesized that interference with CXCR1/CXCR2 signalling contributes to the well-established beneficial effect of volatile anaesthetics in I/R-injury. METHODS Isolated human neutrophils were stimulated with IL-8 or CXCL1 and exposed to volatile anaesthetics (sevoflurane/desflurane). Neutrophil migration was assessed using an adapted Boyden chamber. Expression of CD11b, CXCR1, and CXCR2 was measured by flow cytometry. Blocking antibodies against CXCR1/CXCR2/CD11b and phorbol myristate acetate were used to investigate specific pathways. RESULTS Volatile anaesthetics reduced CD11b-dependent neutrophil transmigration induced by IL-8 by >30% and CD11b expression by 18 and 27% with sevoflurane/desflurane, respectively. This effect was independent of CXCR1/CXCR2 expression and CXCR1/CXCR2 endocytosis. Inhibition of CXCR1 signalling did not affect downregulation of CD11b with volatile anaesthetics. Blocking of CXCR2-signalling neutralized effects by volatile anaesthetics on CD11b expression. Specific stimulation of CXCR2 with CXCL1 was sufficient to induce upregulation of CD11b, which was impaired with volatile anaesthetics. No effect of volatile anaesthetics was observed with direct stimulation of protein kinase C located downstream of CXCR1/CXCR2. CONCLUSION Volatile anaesthetics attenuate neutrophil inflammatory responses elicited by CXC cytokines through interference with CXCR2 signalling. This might contribute to the beneficial effect of volatile anaesthetics in I/R-injury

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

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