Ventricular arrhythmias originating from different portions of the communicating vein of the left ventricular summit: electrocardiographic characteristics and catheter ablation.

BACKGROUND: Idiopathic ventricular arrhythmias (IVAs) arising from different portions of the communicating vein of the left ventricular summit (summit-CV) are not a rare phenomenon. Whereas its electrocardiographic (ECG) and electrophysiological characteristics are not fully investigated. OBJECTIVE: This study aimed to identify distinct ECG and electrophysiological features of IVAs originating from different portions of summit-CV. METHODS: Nineteen patients confirmed arising from summit-CV were included in this study. RESULTS: The 19 patients were divided into proximal and distal portion groups based on their target sites in summit-CV. In the proximal portion group, 100% (11/11) VAs showed dominant negative (rs or QS) waves in lead I, while in the distal portion group, 87.5% (7/8) showed dominant positive waves (R, Rs or r) (p < 0.000). In lead V1, 100% (11/11) of the proximal portion group showed dominant positive waves (R or Rs), while 62.50% (5/8) of the distal portion group showed positive and negative bidirectional or negative waves (RS or rS) (p < 0.005). RI>4mV, SI<3.5mV, RV1<13mV, SV1>3.5mV, RI/SI>0.83, and RV1/SV1< 2.6 indicated a distal portion of summit-CV with the predictive value of 0.909, 1.000, 0.653, 0.972, 0.903, 0.966, respectively. A more positive wave in lead I and a more negative wave in lead V1 indicated more distal origin in summit-CV. Target sites in proximal and distal summit-CV groups showed similar electrophysiological characteristics during mapping. CONCLUSIONS: There were significant differences in ECG characteristics of VAs at different portions of summit-CV, which could aid pre-procedure planning and facilitate radiofrequency catheter ablation (RFCA) procedures.

Left bundle branch pacing guide by uninterrupted recording of intrinsic filtered and unfiltered intracardiac electrograms.

BACKGROUND: Currently, the interrupted recording technique is commonly used to perform left bundle branch (LBB) pacing (LBBP). However, this method requires repeated testing to confirm that the LBB is captured and perforations are avoided. An automated solution may make this repetitive work easier. CASE SUMMARY: LBBP was performed using an uninterrupted recording technique in an 86-year-old woman. Lead position and LBB capture was confirmed by the characteristics of the intrinsic filtered and unfiltered intracardiac electrograms. CONCLUSION: Continuous mapping and recording technique may help achieve more accurate positioning of LBBP lead in the ventricular septum.

Artificial intelligence to detect noise events in remote monitoring data.

Background: Remote monitoring (RM) of cardiac implantable electrical devices (CIEDs) can detect various events early. However, the diagnostic ability of CIEDs has not been sufficient, especially for lead failure. The first notification of lead failure was almost noise events, which were detected as arrhythmia by the CIED. A human must analyze the intracardiac electrogram to accurately detect lead failure. However, the number of arrhythmic events is too large for human analysis. Artificial intelligence (AI) seems to be helpful in the early and accurate detection of lead failure before human analysis. Objective: To test whether a neural network can be trained to precisely identify noise events in the intracardiac electrogram of RM data. Methods: We analyzed 21 918 RM data consisting of 12 925 and 1884 Medtronic and Boston Scientific data, respectively. Among these, 153 and 52 Medtronic and Boston Scientific data, respectively, were diagnosed as noise events by human analysis. In Medtronic, 306 events, including 153 noise events and randomly selected 153 out of 12 692 nonnoise events, were analyzed in a five-fold cross-validation with a convolutional neural network. The Boston Scientific data were analyzed similarly. Results: The precision rate, recall rate, F1 score, accuracy rate, and the area under the curve were 85.8 ± 4.0%, 91.6 ± 6.7%, 88.4 ± 2.0%, 88.0 ± 2.0%, and 0.958 ± 0.021 in Medtronic and 88.4 ± 12.8%, 81.0 ± 9.3%, 84.1 ± 8.3%, 84.2 ± 8.3% and 0.928 ± 0.041 in Boston Scientific. Five-fold cross-validation with a weighted loss function could increase the recall rate. Conclusions: AI can accurately detect noise events. AI analysis may be helpful for detecting lead failure events early and accurately.

Automatic measurement of short-term variability of repolarization to indicate ventricular arrhythmias in a porcine model of cardiac ischaemia.

AIMS: An automated method for determination of short-term variability (STV) of repolarization on intracardiac electrograms (STV-ARIauto) has previously been developed for arrhythmic risk monitoring by cardiac implantable devices, and has proved effective in predicting ventricular arrhythmias (VA) and guiding preventive high-rate pacing (HRP) in a canine model. Current study aimed to assess (i) STV-ARIauto in relation to VA occurrence and secondarily (ii-a) to confirm the predictive capacity of STV from the QT interval and (ii-b) explore the effect of HRP on arrhythmic outcomes in a porcine model of acute myocardial infarction (MI). METHODS AND RESULTS: Myocardial infarction was induced in 15 pigs. In 7/15 pigs, STV-QT was assessed at baseline, occlusion, 1 min before VA, and just before VA. Eight of the 15 pigs were additionally monitored with an electrogram catheter in the right ventricle, underwent echocardiography at baseline and reperfusion, and were randomized to paced or control group. Paced group received atrial pacing at 20 beats per min faster than sinus rhythm 1 min after occlusion. Short-term variability increased prior to VA in both STV modalities. The percentage change in STV from baseline to successive timepoints correlated well between STV-QT and STV-ARIauto. High-rate pacing did not improve arrhythmic outcomes and was accompanied by a stronger decrease in ejection fraction. CONCLUSION: STV-ARIauto values increase before VA onset, alike STV-QT in a porcine model of MI, indicating imminent arrhythmias. This highlights the potential of automatic monitoring of arrhythmic risk by cardiac devices through STV-ARIauto and subsequently initiates preventive strategies. Continuous HRP during onset of acute MI did not improve arrhythmic outcomes.

Cardiac Resynchronization Therapy beyond Nominal Settings: An IEGM-Based Approach for Paced and Sensed Atrioventricular Delay Offset Optimization in Daily Clinical Practice.

Optimization of the atrioventricular (AV) delay has been performed in several landmark trials in cardiac resynchronization therapy (CRT), although it is often not performed in daily practice. Our aim was to study optimal AV delays and investigate a simple intracardiac electrogram (IEGM)-based optimization approach. 328 CRT patients with paired IEGM and echocardiography optimization data were included in our single-center observational study. Sensed (sAV) and paced (pAV) AV delays were optimized using an iterative echocardiography method. The offset between sAV and pAV delays was calculated using the IEGM method. The mean age of the patients was 69 ± 12 years; 64% were men, 48% had ischemic etiology of heart failure. During echocardiographic optimization, an offset of 73 ± 18 ms was found, differing from nominal AV settings (p < 0.001). Based on the IEGM method, the optimal offset was 75 ± 25 ms. The echocardiographic and IEGM-generated AV offset delays showed good correlation (R2 = 0.62, p < 0.001) and good agreement according to Bland-Altman plot analysis. CRT responders had a near zero offset difference between IEGM and echo optimization (-0.2 ± 17 ms), while non-responders had an offset difference of 6 ± 17 ms, p = 0.006. In conclusion, optimal AV delays are patient-specific and differ from nominal settings. pAV delay can easily be calculated from IEGM after sAV delay optimization.

Case report: Left bundle branch pacing guided by real-time monitoring of current of injury and electrocardiography.

Background: Left bundle branch (LBB) pacing (LBBP) has recently emerged as a physiological pacing mode. Current of injury (COI) can be used as the basis for electrode fixation position and detection of perforation. However, because the intermittent pacing method cannot monitor the changes in COI in real time, it cannot obtain information about the entire COI change process during implantation. Case summary: Left bundle branch pacing was achieved for treatment of atrioventricular block in a 76-year-old female. Uninterrupted electrocardiogram and electrogram were recorded on an electrophysiology system. In contrast to the interrupted pacing method, this continuous pacing and recording technique enables real-time monitoring of the change in ventricular COI and the paced QRS complex as the lead advances into the interventricular septum. During the entire screw-in process, the COI amplitude increased and then decreased gradually after reaching the peak, followed by a small but significant, rather than dramatic, decrease. Conclusion: This case report aims to demonstrate the clinical significance of changes in COI and QRS morphology for LBBP using real-time electrocardiographic monitoring and filtered and unfiltered electrograms when the lead is deployed using a continuous pacing technique. The technique could be used to confirm LBB capture and avoid perforation.

Electrophysiological characteristics and possible mechanism of bipolar pacing in left bundle branch pacing.

BACKGROUND: Left bundle branch pacing is a physiological pacing modality with a low and stable threshold. The electrophysiological characteristics and mechanisms of bipolar pacing remain unclear. OBJECTIVES: This study aimed to assess the electrophysiological characteristics of bipolar pacing of left bundle branch pacing and to infer the mechanisms underlying each electrocardiogram and electrogram waveform morphology. METHODS: A total of 65 patients who strictly met the criteria for left bundle branch capture were enrolled. The changes in the morphology of the electrocardiogram and electrogram during the threshold testing with different outputs on unipolar and bipolar pacing were recorded. The electrophysiological characteristics were then analyzed. RESULTS: Four distinct morphologies and 3 different types of transitions during bipolar pacing threshold testing were identified; we labeled the 4 types of morphologies as nonselective (NS)-bipolar-left bundle (LB), NS-cathodal-LB, selective (S)-cathodal-LB, and left ventricular septal-cathodal. Except left ventricular septal-cathodal, the other 3 types (NS-bipolar-LB, NS-cathodal-LB, and S-cathodal-L) had a short and constant V6 R-wave peak time (RWPT) (64.8 ± 7.7 ms vs 65.7 ± 7.8 ms vs 65.7 ± 7.3 ms). The paced QRS (P-QRS) complex was the narrowest in NS-bipolar-LB rather than in NS-cathodal-LB (118.2 ± 14.2 ms vs 133.8 ± 15.8 ms; P < .001). NS-bipolar-LB had a higher threshold than did NS-cathodal-LB (2.5 ± 1.2 V vs 0.8 ± 0.4 V; P < .001). CONCLUSION: With a higher output on bipolar pacing, NS-bipolar-LB capture had the shortest V6 RWPT, V1 RWPT, and P-QRS. S-cathodal-LB capture had the longest V1 RWPT and P-QRS complex.

Characteristics of intracardiac electrogram of the interventricular septum in the left bundle branch pacing.

BACKGROUND: Left bundle branch pacing (LBBP) has become a hot topic in the field of physiological pacing. However, only a few studies have described the characteristics of the intrinsic intracardiac electrogram (EGM) while placing the left bundle branch (LBB) lead. CASE PRESENTATION: Herein, we reported a case with atrial premature contractions to the ventricle during the LBBP procedure. Paced and intrinsic (supraventricular) EGMs were recorded and analyzed. CONCLUSIONS: The myocardium of the interventricular septum could be divided into four regions based on electrophysiology: the right septal area, the left septal area, the endocardium of the left ventricular septum, and the LBB area. This might guide the electrophysiological localization of the LBB lead in the septum.

Current of injury is an indicator of lead depth and performance during left bundle branch pacing lead implantation.

BACKGROUND: Monitoring of lead depth is crucial to achieve left bundle branch pacing (LBBP) with a low capture threshold and avoid septal perforation, but lacks informative approach. OBJECTIVE: We aimed to prospectively assess the predictive value of current of injury on the occurrence of inadequate left bundle branch (LBB) capture threshold and acute septal perforation. METHODS: Consecutive patients who received LBBP were enrolled. ST-segment elevation ≥ 25% of intrinsic R-wave amplitude on the unipolar intracardiac electrogram was defined as a sign of distinct current of injury. An LBB capture threshold of <1.5 V/0.5 ms was considered acceptable. RESULTS: LBBP was attempted 513 times in 212 patients. The LBB capture threshold was more likely to improve to an acceptable level after 10 minutes in leads with initial (33 of 47 vs 0 of 8, with vs without) and residual (29 of 33 vs 4 of 14, with vs without) current of injury recorded on the tip electrode (P < .0001). Lead perforation during the procedure has occurred in 11 patients who had no current of injury noted on the tip electrode. The ratio of current of injury recorded on the tip electrode to that on the ring electrode was correlated to the lead depth determined by sheath angiography (Spearman correlation coefficient -0.624; P < .0001), and microperforation is highly possible when the ratio is decreased to <1 (sensitivity 100%; specificity 96.6%). CONCLUSION: Current of injury is a useful tool in forecasting LBBP lead depth and septal perforation, and it could facilitate the decision-making process when the initial LBB capture threshold is undesirable.

Recording an isoelectric interval as an endpoint of left bundle branch pacing with continuous paced intracardiac electrogram monitoring.

BACKGROUND: The present study aimed to evaluate the feasibility and safety of the novel left bundle branch pacing (LBBP) procedure that uses isoelectric interval as an endpoint for lead implantation. METHODS: A total of 41 patients with indications for pacing were enrolled. All patients underwent a novel LBBP procedure guided by recording an isoelectric interval as an endpoint for lead implantation. The procedural details and electrophysiological characteristics were then analyzed. RESULTS: A total of 38/41 (92.7%) cases were confirmed of left bundle branch (LBB) capture. An isoelectric interval was observed in 36/41 cases (87.8%). A total of 36/41 (87.8%) cases with LBB potential were observed. The mean unipolar LBBP threshold at the implant was 0.5 ± 0.2 V. The mean sensed amplitude of the R wave and the pacing impedance at the implant were 12.9 ± 5.0 mV and 723.5 ± 117.1 Ω. During the final threshold testing, a transition from non-selective to selective LBBP (S-LBBP) was demonstrated in 26 patients. A transition from non-selective LBBP (NS-LBBP) to left ventricular septal myocardial capture was observed in 12 patients. CONCLUSION: Using an isoelectric interval as an endpoint to guide the LBBP was feasible in a high proportion of captured LBB cases.

Differentiating left bundle branch pacing and left ventricular septal pacing: An algorithm based on intracardiac electrophysiology.

BACKGROUND: Left bundle branch pacing (LBBP) is a new near-physiological pacing modality. Distinguishing left ventricular septal only pacing (LVSP) from nonselective LBBP still needs clarification. This prospective study sought to establish a differentiation algorithm to confirm LBBP. METHODS AND RESULTS: LBBP was attempted in consecutive patients. If direct LBB capture (LBBP) could not be confirmed, LVSP was considered to have been achieved. Intracardiac left ventricular (LV) activation sequence and activation time were analyzed using coronary sinus (CS) electrogram mapping. Electrophysiological parameters including S-CSmax, S-CSmin, LV lateral wall activation time, ΔLV, and LBB potential were compared between LBBP and LVSP. Stimulated LV activation time (S-LVAT) and stimulated QRS duration (S-QRSd) were also compared between the two groups. Multivariate logistic regression analysis was used to develop a prediction algorithm for LBBP. Of the 43 prospectively enrolled patients, 27 underwent LBBP and 16 underwent LVSP. All LBBP patients showed identical LV activation sequences to their intrinsic rhythm while no LVSP patients maintained their intrinsic sequence. S-CSmax, ΔLV, LV lateral wall activation time, and S-LVAT during LBBP were significantly shorter than those during LVSP. Combining LBB potential with S-LVAT had the largest area under the curve (AUC) of 0.985 for confirming LBBP with a sensitivity of 95.2% and a specificity of 93.7%. CONCLUSIONS: Compared with LVSP, LBBP preserves a normal LV activation sequence and better electrical synchrony. A combination of LBB potential with S-LVAT can be an effective and practical model to distinguish LBBP from LVSP during implantation in patients with normal LBB activation.

Dynamics of Intraprocedural Dominant Frequency Identifies Ablation Outcome in Persistent Atrial Fibrillation.

Background: The role of dominant frequency (DF) in tracking the efficiency of a stepwise catheter ablation (step-CA) in persistent atrial fibrillation (peAF) remains poorly studied. We hypothesized that the DF time-course during step-CA displays divergent patterns between patients in whom a step-CA successfully restores long-term sinus rhythm (SR) and those with recurrence. Methods: This study involved 40 consecutive patients who underwent a step-CA for peAF (sustained duration 19 ± 11 months). Dominant frequency was computed on electrograms recorded from the right and left atrial appendages (RAA; LAA) and the coronary sinus before and during the step-CA synchronously to the 12-lead ECG. Dominant frequency was defined as the highest peak within the power spectrum. Results: Persistent atrial fibrillation was terminated by a step-CA in 28 patients [left-terminated (LT)], whereas 12 patients remaining in AF after ablation [not left-terminated (NLT)] were cardioverted. Over a mean follow-up of 34 ± 14 months, all NLT patients had a recurrence. Among the 28 LT patients, 20 had a recurrence, while 8 remained in SR throughout follow-up. The RAA and V1 DF had the best predictive values of the procedural failure to terminate AF (area under the curve; AUC 0.84, p < 0.05). A decision tree model including a decrease in LAA DF ≥ 6.61% during the first 20 min following pulmonary vein isolation (PVI) and a baseline RAA DF <5.6 Hz predicted long-term SR restoration with a sensitivity of 83% and a specificity of 93% (p < 0.05). Conclusion: This study found that high baseline DF values are predictive of unfavorable ablation outcomes. The reduction of the LAA DF at early ablation steps following PVI is associated with procedural AF termination and long-term SR maintenance.

Focal Atrial Tachycardia Originating From the Right Atrial Appendage Masquerading as Inappropriate Sinus Tachycardia.

Focal atrial tachycardia arising from the right atrial appendage (RAAT) may be misdiagnosed as sinus tachycardia. The electrocardiogram from this case demonstrates a negative notched P-wave in leads V1 and V2 during RAAT compared with a beat of sinus rhythm. RAAT was confirmed and eliminated with mapping and ablation. (Level of Difficulty: Advanced.).

A novel convolutional neural network for reconstructing surface electrocardiograms from intracardiac electrograms and vice versa.

We propose a novel convolutional neural network framework for mapping a multivariate input to a multivariate output. In particular, we implement our algorithm within the scope of 12-lead surface electrocardiogram (ECG) reconstruction from intracardiac electrograms (EGM) and vice versa. The goal of performing this task is to allow for improved point-of-care monitoring of patients with an implanted device to treat cardiac pathologies. We will achieve this goal with 12-lead ECG reconstruction and by providing a new diagnostic tool for classifying five different ECG types. The algorithm is evaluated on a dataset retroactively collected from 14 patients. Correlation coefficients calculated between the reconstructed and the actual ECG show that the proposed convolutional neural network model represents an efficient, accurate, and superior way to synthesize a 12-lead ECG when compared to previous methods. We can also achieve the same reconstruction accuracy with only one EGM lead as input. We also tested the model in a non-patient specific way and saw a reasonable correlation coefficient. The model was also executed in the reverse direction to produce EGM signals from a 12-lead ECG and found that the correlation was comparable to the forward direction. Lastly, we analyzed the features learned in the model and determined that the model learns an overcomplete basis of our 12-lead ECG space. We then use this basis of features to create a new diagnostic tool for classifying different ECG arrhythmia's on the MIT-BIH arrhythmia database with an average accuracy of 0.98.

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.

Implication of the distinctive bipolar intracardiac electrograms for ventricular arrhythmias arising from different regions of ventricular outflow tract.

AIMS: To investigate the characteristics of bipolar intracardiac electrograms (bi-EGMs) in target sites of ventricular arrhythmias (VAs) originating from different regions of ventricular outflow tract (VOT). METHODS AND RESULTS: Two hundred and seventy patients undergoing first-time ablation for VAs originated from distal great cardiac vein (DGCV), aortic sinus cusps (ASCs), or pulmonary sinus cusps (PSCs) were enrolled in present study. Local intracardiac bipolar recordings on 243 successful sites and 506 attempted but unsuccessful ablation sites were analysed. Specific potentials in bi-EGMs on successful sites were more common compared with unsuccessful sites (76.95%, 187/243 vs. 25.49%, 129/506, P < 0.05). A total of 60.00% (81/135) patients in ASCs group presented a presystolic short-duration fractionated potential, higher than 23.21% (13/56) in DGCV and 23.08% (12/52) in PSCs (all P < 0.05); 44.23% (23/52) patients in PSC group showed a presystolic high-amplitude discrete potential, while 1.79% (1/56) in DGCV and 2.22% (3/135) in ASCs (all P < 0.05); 41.07% (23/56) patients in DGCV group showed bi-EGMs of presystolic long-duration multicomponent fractionated potential, which was significantly higher than 3.85% (2/52) in PSCs and 4.44%(6/135) in ASCs (all P < 0.05). CONCLUSION: Distinctive morphology of bi-EGMs during VAs can be found in different regions of VOT, which probably due to changes in the arrangements of myocardial sleeves. Correct identification and better understanding of the distinctive features of these bi-EGMs with regards to the anatomic location was important, the presence of specific potentials may add help in successful ablation.