Pacemaker programming and device interrogation

Author(s):  
Timothy Betts ◽  
Julian Ormerod

This chapter covers all areas of pacemaker programming and choices related to pacing. Methods of choosing rate limits, and related programming features are described for the lower rate interval, the upper rate interval, the atrioventricular interval, and refractory and blanking periods are described in detail. The anatomical and signal changes for each period are defined, and all descriptions are illustrated with example ECGs to show different programme outcomes. Rate response, programming rate response, and mode switching are covered. Automated functions and stored information are described, and algorithms to prevent atrial arrhythmias are explained. Finally, pacemaker-mediated tachycardia is outlined, including detection and methods of terminating the pacemaker-mediated tachycardia (PMT).

EP Europace ◽  
2020 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
C Garweg ◽  
S K Khelae ◽  
J Y S Chan ◽  
L Chinitz ◽  
P Ritter ◽  
...  

Abstract Funding Acknowledgements Medtronic, Inc. Background/Introduction Accelerometer (ACC)-based AV synchronous pacing by tracking atrial activity is feasible using a leadless ventricular pacemaker. Patients may experience variable AV conduction (AVC) and/or atrial arrhythmias during the lifetime of their device. ACC-based AV synchronous pacing should facilitate AVC and pace appropriately in those two common rhythms. Purpose To characterize the behavior of ACC-based AV synchronous pacing algorithms during paroxysmal AV block (AVB) and atrial arrhythmias. Methods The MARVEL2 (Micra Atrial tRacking using a Ventricular accELerometer) was a 5-hour acute study to assess the efficacy of atrial tracking with a temporarily downloaded algorithm into a Micra leadless pacemaker. Patients with a history of AVB were eligible for inclusion. The MARVEL2 algorithm included a mode-switching algorithm that switched between VDD and VVI-40 depending upon AVC status. The AVC algorithm requires 2 ventricular paces (VP) at 40 bpm out of 4 pacing cycles to switch to VDD. Results Overall, 75 patients (age 77.5 ± 11.8 years, 40% female, median time from Micra implant 9.7 months) from 12 centers worldwide were enrolled. During study procedures, 40 patients (53%) had normal sinus rhythm with complete AVB, 18 (24%) had 1:1 AVC, 5 (7%) had varying AVC status, 8 (11%) had atrial arrhythmias, and 2 other rhythms.  Two patients with complete AVB had the AVC mode switch feature disabled due to an idioventricular rate >40 bpm.  Among the 40 subjects with a predominant 3rd degree AVB and normal sinus function the median %VP was 99.9% compared to 0.2% among those with 1:1 AVC (Figure). In the patients with 1:1 AVC, there were 64 opportunities to AVC mode switch with 48 switching to VDI-40. In the other 16 cases (2 patients) the mode remained VDD due to sinus bradycardia varying between 40-45 bpm. High %VP was observed in 2 patients with 1:1 AVC and sinus bradycardia <40 bpm. The AVC mode switch minimized %VP (<1%) in patients with PR intervals > 300 ms (N = 2). Among patients with varying AVC, the algorithm appropriately switched to VDD when the ventricular rate was paced at 40 bpm. During infrequent AVB or AF with ventricular response >40 bpm, VVI-40 mode was maintained. In patients with AF, the ACC signal was of low amplitude and there was infrequent sensing, resulting in VP at the lower rate (50 bpm). In the one patient with atrial flutter, the ACC was intermittently detected, resulting in VP at 67 bpm (IQR 66-67 bpm). Conclusion(s) The mode switching algorithm in the MARVEL2 reduced %VP in patients with 1:1 AVC and appropriately switched to VDD during complete AVB.  If greater AV synchrony or rate support is required, disabling the AVC algorithm may be appropriate for low grade AVB or idioventricular rhythms. In the presence of atrial arrhythmias, the algorithm paced near the lower rate. Abstract Figure. Distribution of VP% by heart rhythm


2020 ◽  
Author(s):  
Luca Rade

Emulators are internal models, first evolved for prediction in perception to shorten the feedback on motor action. However, the selective pressure on perception is to improve the fitness of decision-making, driving the evolution of emulators towards context-dependent payoff representation and integration of action planning, not enhanced prediction as is generally assumed. The result is integrated perceptual, memory, representational, and imaginative capacities processing external input and stored internal input for decision-making, while simultaneously updating stored information. Perception, recall, imagination, theory of mind, and dreaming are the same process with different inputs. Learning proceeds via scaffolding on existing conceptual infrastructure, a weak form of embodied cognition. Discrete concepts are emergent from continuous dynamics and are in a perceptual, not representational, format. Language is also in perceptual format and enables precise abstract thought. In sum, what was initially a primitive system for short-term prediction in perception has evolved to perform abstract thought, store and retrieve memory, understand others, hold embedded action plans, build stable narratives, simulate scenarios, and integrate context dependence into perception. Crucially, emulators co-evolved with the emergence of societies, producing a mind-society system in which emulators are dysfunctional unless integrated into a society, which enables their complexity. The Target Emulator System, evolved initially for honest signaling, produces the emergent dynamics of the mind-society system and spreads variation-testing of behavior and thought patterns across a population. The human brain is the most dysfunctional in isolation, but the most effective given its context.


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