His-Purkinje conduction system pacing: state of the art in 2020

Conduction system pacing involves directly stimulating the specialised His–Purkinje cardiac conduction system with the aim of activating the ventricles physiologically, in contrast to the dyssynchronous activation produced by conventional myocardial pacing. Since the first report of permanent His bundle pacing (HBP) in 2000, the stylet-driven technique of its earliest incarnation has been superseded by a more successful stylet-less approach. Widespread uptake has led to a much greater evidence base. Single-centre observational studies have now been supported by large multicentre, international registries, mechanistic studies and the first randomised controlled trials. New evidence has elucidated mechanisms of HBP and illustrated the nature and magnitude of its potential benefits for preventing pacing-induced cardiomyopathy and correcting bundle branch block. Left bundle branch pacing (LBBP) is a newer technique in which the lead is fixed deep into the left side of the intraventricular septum to allow capture of the left bundle, distal to the His bundle. LBBP holds promise as a method for physiological pacing that overcomes some of the fixation, threshold and sensing challenges of HBP. In this state-of-the-art review of His–Purkinje conduction system pacing, the authors assess recent evidence and current practice and explore emerging and future directions in this rapidly evolving field

Boson stars in massive dilatonic gravity

We study equilibrium configurations of boson stars in the framework of a class scalar-tensor theories of gravity with massive gravitational scalar (dilaton). In particular we investigate the influence of the mass of the dilaton on the boson star structure. We find that the masses of the boson stars in presence of dilaton are close to those in general relativity and they are sensitive to the ratio of the boson mass to the dilaton mass within a typical few percent. It turns out also that the boson star structure is mainly sensitive to the mass term of the dilaton potential rather to the exact form of the potential.Comment: 9 pages, latex, 9 figures, one figure dropped, new comments added, new references added, typos correcte

Cardiac rhythm device identification using neural networks

Background Medical staff often need to determine the model of a pacemaker or defibrillator (cardiac rhythm devices) quickly and accurately. Current approaches involve comparing a device’s X-ray appearance with a manual flow chart. We aimed to see whether a neural network could be trained to perform this task more accurately. Methods and Results We extracted X-ray images of 1676 devices, comprising 45 models from 5 manufacturers. We developed a convolutional neural network to classify the images, using a training set of 1451 images. The testing set was a further 225 images, consisting of 5 examples of each model. We compared the network’s ability to identify the manufacturer of a device with those of cardiologists using a published flow-chart. The neural network was 99.6% (95% CI 97.5 to 100) accurate in identifying the manufacturer of a device from an X-ray, and 96.4% (95% CI 93.1 to 98.5) accurate in identifying the model group. Amongst 5 cardiologists using the flow-chart, median manufacturer accuracy was 72.0% (range 62.2% to 88.9%), and model group identification was not possible. The network was significantly superior to all of the cardiologists in identifying the manufacturer (p < 0.0001 against the median human; p < 0.0001 against the best human). Conclusions A neural network can accurately identify the manufacturer and even model group of a cardiac rhythm device from an X-ray, and exceeds human performance. This system may speed up the diagnosis and treatment of patients with cardiac rhythm devices and it is publicly accessible online

His bundle pacing, learning curve, procedure characteristics, safety, and feasibility: Insights from a large international observational study

Background His‐bundle pacing (HBP) provides physiological ventricular activation. Observational studies have demonstrated the techniques feasibility however, data has come from a limited number of centres. Objectives We set out to explore contemporary global practise in HBP focusing on learning curve, procedural characteristics and outcomes. Methods This is a retrospective, multi‐centre observational study of patients undergoing attempted HBP at seven centres. Pacing indication, fluoroscopy time, HBP thresholds and lead re‐intervention and deactivation rates were recorded. Where centres had systematically recorded implant success rates from the outset, these were collated. Results 529 patients underwent attempted HBP during the study period (2014‐19) with mean follow‐up of 217±303 days. Most implants were for bradycardia indications. In the three centres with systematic collation of all attempts, overall implant success rate was 81% which improved to 87% after completion of 40 cases. All seven centres reported data on successful implants. Mean fluoroscopy time was 11.7±12.0 minutes, His‐bundle capture threshold at implant was 1.4±0.9V at 0.8±0.3 ms and was 1.3±1.2V at 0.9±0.2ms at last device check. HBP lead re‐intervention or deactivation (for lead displacement or rise in threshold) occurred in 7.5% of successful implants. There was evidence of a learning curve: fluoroscopy time and HBP capture threshold reduced with greater experience, plateauing after ~30‐50 cases. Conclusion We found that it is feasible to establish a successful HBP program, using the currently available implantation tools. For physicians who are experienced at pacemaker implantation the steepest part of the learning curve appears to be over the first 30‐50 cases

Quantification of Electromechanical Coupling to Prevent Inappropriate Implantable Cardioverter-Defibrillator Shocks

Objective To test specialised processing of laser Doppler signals for discriminating ventricular fibrillation(VF) from common causes of inappropriate therapies. Background Inappropriate ICD therapies remain a clinically important problem associated with morbidity and mortality. Tissue perfusion biomarkers, to assist automated diagnosis of VF, suffer the vulnerability of sometimes mistaking artefact and random noise for perfusion, which could lead to shocks being inappropriately withheld. Methods We developed a novel processing algorithm that combines electrogram data and laser Doppler perfusion monitoring, as a method for assessing circulatory status. We recruited 50 patients undergoing VF induction during ICD implantation. We recorded non-invasive laser Doppler and continuous electrograms, during both sinus-rhythm and VF. For each patient we simulated two additional scenarios that may lead to inappropriate shocks: ventricular-lead fracture and T-wave oversensing. We analysed the laser Doppler using three methods for reducing noise: (i)Running Mean, (ii)Oscillatory Height, (iii)a novel quantification of Electro-Mechanical coupling which gates laser Doppler against electrograms. We additionally tested the algorithm during exercise induced sinus tachycardia. Results Only the Electro-mechanical coupling algorithm found a clear perfusion cut-off between sinus rhythm and VF (sensitivity and specificity 100%). Sensitivity and specificity remained 100% during simulated lead fracture and electrogram oversensing. (AUC: Running Mean 0.91, Oscillatory Height 0.86, Electro-Mechanical Coupling 1.00). Sinus tachycardia did not cause false positives. Conclusions Quantifying the coupling between electrical and perfusion signals increases reliability of discrimination between VF and artefacts that ICDs may interpret as VF. Incorporating such methods into future ICDs may safely permit reductions of inappropriate shocks

Optimizing atrio-ventricular delay in pacemakers using potentially implantable physiological biomarkers

Background Hemodynamically optimal atrioventricular (AV) delay can be derived by echocardiography or beat-by-beat blood pressure (BP) measurements, but analysis is labor intensive. Laser Doppler perfusion monitoring measures blood flow and can be incorporated into future implantable cardiac devices. We assess whether laser Doppler can be used instead of BP to optimize AV delay. Methods Fifty eight patients underwent 94 AV delay optimizations with biventricular or His-bundle pacing using laser Doppler and simultaneous noninvasive beat-by-beat BP. Optimal AV delay was defined using a curve of hemodynamic response to switching from AAI (reference state) to DDD (test state) at several AV delays (40–320 ms), with automatic quality control checking precision of the optimum. Five subsequent patients underwent an extended protocol to test the impact of greater numbers of alternations on optimization quality. Results 55/94 optimizations passed quality control resulting in an optimal AV delay on laser Doppler similar to that derived by BP (median absolute deviation 12 ms). An extended protocol with increasing number of replicates consistently improved quality and reduced disagreement between laser Doppler and BP optima. With only five replicates, no optimization passed quality control, and the median absolute deviation would be 29 ms. These improved progressively until at 50 replicates, all optimizations passed quality control and the median absolute deviation was only 13 ms. Conclusions Laser Doppler perfusion produces hemodynamic optima equivalent to BP. Quality control can be automatic. Adding more replicates, consistently improves quality. Future implantable devices could use such methods to dynamically and reliably optimize AV delays

Shortening of atrioventricular delay at increased atrial paced heart rates improves diastolic filling and functional class in patients with biventricular pacing

<p>Abstract</p> <p>Background</p> <p>Use of rate adaptive atrioventricular (AV) delay remains controversial in patients with biventricular (Biv) pacing. We hypothesized that a shortened AV delay would provide optimal diastolic filling by allowing separation of early and late diastolic filling at increased heart rate (HR) in these patients.</p> <p>Methods</p> <p>34 patients (75 ± 11 yrs, 24 M, LVEF 34 ± 12%) with Biv and atrial pacing had optimal AV delay determined at baseline HR by Doppler echocardiography. Atrial pacing rate was then increased in 10 bpm increments to a maximum of 90 bpm. At each atrial pacing HR, optimal AV delay was determined by changing AV delay until best E and A wave separation was seen on mitral inflow pulsed wave (PW) Doppler (defined as increased atrial duration from baseline or prior pacemaker setting with minimal atrial truncation). Left ventricular (LV) systolic ejection time and velocity time integral (VTI) at fixed and optimal AV delay was also tested in 13 patients. Rate adaptive AV delay was then programmed according to the optimal AV delay at the highest HR tested and patients were followed for 1 month to assess change in NYHA class and Quality of Life Score as assessed by Minnesota Living with Heart Failure Questionnaire.</p> <p>Results</p> <p>81 AV delays were evaluated at different atrial pacing rates. Optimal AV delay decreased as atrial paced HR increased (201 ms at 60 bpm, 187 ms at 70 bpm, 146 ms at 80 bpm and 123 ms at 90 bpm (ANOVA F-statistic = 15, p = 0.0010). Diastolic filling time (P < 0.001 vs. fixed AV delay), mitral inflow VTI (p < 0.05 vs fixed AV delay) and systolic ejection time (p < 0.02 vs. fixed AV delay) improved by 14%, 5% and 4% respectively at optimal versus fixed AV delay at the same HR. NYHA improved from 2.6 ± 0.7 at baseline to 1.7 ± 0.8 (p < 0.01) 1 month post optimization. Physical component of Quality of Life Score improved from 32 ± 17 at baseline to 25 ± 12 (p < 0.05) at follow up.</p> <p>Conclusions</p> <p>Increased heart rate by atrial pacing in patients with Biv pacing causes compromise in diastolic filling time which can be improved by AV delay shortening. Aggressive AV delay shortening was required at heart rates in physiologic range to achieve optimal diastolic filling and was associated with an increase in LV ejection time during optimization. Functional class improved at 1 month post optimization using aggressive AV delay shortening algorithm derived from echo-guidance at the time of Biv pacemaker optimization.</p