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A Clinical Challenge Overcome by His Bundle Pacing.

ABSTRACT: We highlight a diagnostic challenge in a patient with dyspnea on exertion due to radiation therapy-induced severe first-degree atrioventricular block and how permanent His bundle pacing was helpful in overcoming these symptoms. (Level of Difficulty: Intermediate.).

SUBMITTER: Zimmerman A

PROVIDER: S-EPMC8298658 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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A Clinical Challenge Overcome by His Bundle Pacing.

Zimmerman Alison A Patel Hena H Volgman Annabelle A Okwuosa Tochukwu T Sharma Parikshit S PS

JACC. Case reports 20200219 2

We highlight a diagnostic challenge in a patient with dyspnea on exertion due to radiation therapy-induced severe first-degree atrioventricular block and how permanent His bundle pacing was helpful in overcoming these symptoms. (<b>Level of Difficulty: Intermediate.</b>). ...[more]

PMID: 34317212

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His-bundle pacing is the best approach to physiological pacing.

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Pacing Characteristics of His Bundle Pacing vs. Left Bundle Branch Pacing: A Systematic Review and Meta-Analysis.

Project description:BackgroundHis bundle pacing (HBP) is a physiological pacing strategy, which aims to capture the His bundle-Purkinje system and synchronously activate the ventricles. Left bundle branch pacing (LBBP) is a newly discovered physiological pacing technique similar to HBP. We conducted this meta-analysis to compare the pacing parameters and clinical results between HBP and LBBP.MethodsWe systematically retrieved studies using the PubMed, Embase database, and Cochrane Library. Mean difference (MD) and relative risk (RR) with their 95% confidence intervals [CIs] were used to measure the outcomes. A random-effect model was used when studies were of high heterogeneity.ResultsA total of seven studies containing 867 individuals were included. Compared with HBP, LBBP was associated with higher implant success rates (RR: 1.12, 95% CI: 1.05-1.18; I 2 = 60%, P = 0.0003), lower capture threshold at implantation (V/0.5 ms) (MD: 0.63, 95% CI: 0.35-0.90, I 2 = 89%, P < 0.0001) and capture threshold at follow-up (V/0.5 ms) (MD: 0.76, 95% CI: 0.34-1.18, I 2 = 93%, P = 0.0004), and larger sensed R wave amplitude (mV) at implantation (MD: 7.23, 95% CI: 5.29-9.16, P < 0.0001) and sensed R wave amplitude (mV) at follow-up (MD: 7.53, 95% CI: 6.85-8.22, P < 0.0001). In LBBP recipients, greater QRS wave complex reduction was found in the paced QRS duration at follow-up compared with HBP recipients at follow-up (MD: 6.12, 95% CI: 1.23-11.01, I 2 = 0%, P = 0.01). No statistical differences were found in procedure duration, fluoroscopy time, native left ventricular ejection fractions (LVEF), LVEF improvement, native QRS duration, and QRS reduction from the native QRS duration vs. paced QRS duration at implantation.ConclusionCurrent evidence suggests that pacing characteristics are better in LBBP compared with HBP. Further prospective studies are needed to validate the clinical advantages of LBBP.

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Output-dependent His bundle pacing: Unexpected His-Purkinje system pathology unmasking.

Project description:The anatomy of the His-Purkinje system has been studied, yet there remains a knowledge gap regarding the impact of His bundle pacing and its electrocardiographic implications. This case report highlights the presence of His-Purkinje system pathology without apparent clues on the surface electrocardiogram (EKG). By observing identical QRS morphology with varying HV intervals resulting from different pacing outputs, we demonstrate the presence of an electrical propagation block within the His bundle.

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Long-Term Outcomes in Patients With Relatively High His-Bundle Capture Threshold After Permanent His-Bundle Pacing　- A Multicenter Clinical Study.

Project description:BackgroundOutcomes in patients with relatively high His-bundle (HB) capture thresholds at implantation are unknown. This study aimed to compare changes in the HB capture threshold and prognosis between patients with a relatively high threshold and those with a low threshold.Methods and resultsForty-nine patients who underwent permanent HB pacing (HBP) were divided into two groups: low (<1.25 V at 1.0 ms; n=35) and high (1.25-2.49 V; n=14) baseline HB capture threshold groups. The HB capture threshold was evaluated at implantation, and after 1 week, 1, 3, and 6 months, and every 6 months thereafter. HB capture threshold rise was defined as threshold rise ≥1.0 V at 1.0 ms compared with implantation measures. We compared outcomes between the groups. During a mean follow-up period of 34.6 months, the high-threshold group showed a trend toward a higher incidence of HB capture threshold of ≥2.5 V (50% vs. 14%; P=0.023), HBP abandonment (29% vs. 8.6%; P=0.091), lead revision (21% vs. 2.9%; P=0.065), and clinical events (all-cause death, heart failure hospitalization, and new-onset or progression of atrial fibrillation; 50% vs. 23%; P=0.089) than the low-threshold group. A baseline HB capture threshold of ≥1.25V was an independent predictor of clinical events.ConclusionsA relatively high HB capture threshold is associated with increased risk of HBP abandonment, lead revision, and poor clinical outcomes.

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Rapid reversal of right ventricular pacing-induced cardiomyopathy by His bundle pacing.

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Computerized tomography image correlation of His bundle/deep septal pacing location and outcomes: an analysis from the Canberra HIs bundle/deep septal Pacing Study (CHIPS).

Project description:BackgroundLocalisation of the conduction system under fluoroscopy is not easy and the ideal location of the pacing leads in physiological pacing is still being debated.ObjectiveThe primary aim was to assess the lead locations using cardiac CT scan. Secondary aims were clinical outcomes including success and safety of the procedure and lead performance.MethodsOf the 100 consecutive patients who received physiological pacing, 34 patients underwent follow-up cardiac CT scan. The four different types of pacing were identified as His bundle (HBP), para-Hisian, left bundle branch (LBBP), and deep septal pacing.ResultsMost patients had successful HBP via the right atrium (RA) (87.5%) as compared to the right ventricle (RV) (12.5%). Lower thresholds were observed when leads were placed within 2 mm of the junction of the membranous and muscular ventricular septum. Unlike HBP, LBBP was possible at a wide region of the septum and selective capture of individual fascicles was feasible. LBBP showed deeper penetration of leads into the septum, as compared to deep septal pacing (70% vs. 45%). Approximately, 80% of patients did not have an intra-ventricular portion of the membranous septum.ConclusionsThe anterior part of the atrio-ventricular (AV) septum at the junction between the membranous and muscular septum via RA appeared to be the best target to successfully pace His bundle. LBBP was possible at a wide region of the septum and selective capture of individual fascicle was feasible. Adequate depth of penetration of lead was very important to capture the left bundle.

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Outcomes of left bundle branch area pacing compared to His bundle pacing and right ventricular apical pacing in Japanese patients with bradycardia.

Project description:BackgroundHis bundle pacing (HBP) and left bundle branch area pacing (LBBAP) emerge as better alternatives to right ventricular apical pacing (RVAP) in patients with bradycardia requiring permanent cardiac pacing. We aimed to compare the clinical outcomes of LBBAP, HBP, and RVAP in Japanese patients with bradycardia.MethodsA total of 424 patients who underwent successful pacemaker implantation (HBP, n = 53; LBBAP, n = 75; and RVAP, n = 296) were retrospectively enrolled in this study. The primary study endpoint was the cumulative incidence of heart failure hospitalization (HFH) during the follow-up.ResultsThe success rate for implantation was higher in the LBBAP group than in the HBP group (94.9% and 81.5%, respectively). Capture threshold increase >1V during the follow-up occurred in the HBP and RVAP groups (9.4% and 5.1%, respectively), while it did not in the LBBAP group. The cumulative incidence of HFH was significantly lower in the LBBAP group than the RVAP (adjusted hazard ratio, 0.12 [95% confidence interval: 0.02-0.86]; p = .034); it did not differ between the HBP and RVAP groups (adjusted hazard ratio, 0.48 [95% confidence interval: 0.17-1.34]; p = .16). Advanced age, mean percent right ventricular pacing (per 10% increase), left ventricular ejection fraction <50%, and RVAP were associated with HFH.ConclusionsCompared to RVAP and HBP, LBBAP appeared more feasible and effective in patients with bradycardia requiring permanent cardiac pacing.

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His Bundle Pacing: A New Strategy for Physiological Ventricular Activation.

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Discriminating electrocardiographic responses to His-bundle pacing using machine learning.

Project description:BackgroundHis-bundle pacing (HBP) has emerged as an alternative to conventional ventricular pacing because of its ability to deliver physiological ventricular activation. Pacing at the His bundle produces different electrocardiographic (ECG) responses: selective His-bundle pacing (S-HBP), non-selective His bundle pacing (NS-HBP), and myocardium-only capture (MOC). These 3 capture types must be distinguished from each other, which can be challenging and time-consuming even for experts.ObjectiveThe purpose of this study was to use artificial intelligence (AI) in the form of supervised machine learning using a convolutional neural network (CNN) to automate HBP ECG interpretation.MethodsWe identified patients who had undergone HBP and extracted raw 12-lead ECG data during S-HBP, NS-HBP, and MOC. A CNN was trained, using 3-fold cross-validation, on 75% of the segmented QRS complexes labeled with their capture type. The remaining 25% was kept aside as a testing dataset.ResultsThe CNN was trained with 1297 QRS complexes from 59 patients. Cohen kappa for the neural network's performance on the 17-patient testing set was 0.59 (95% confidence interval 0.30 to 0.88; P <.0001), with an overall accuracy of 75%. The CNN's accuracy in the 17-patient testing set was 67% for S-HBP, 71% for NS-HBP, and 84% for MOC.ConclusionWe demonstrated proof of concept that a neural network can be trained to automate discrimination between HBP ECG responses. When a larger dataset is trained to higher accuracy, automated AI ECG analysis could facilitate HBP implantation and follow-up and prevent complications resulting from incorrect HBP ECG analysis.

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