Automated pacemaker function

Automatic atrial capture device control in real-life practice: A multicenter experience

Background

Device-based fully automatic pacing capture detection is useful in clinical practice and important in the era of remote care management.

The main objective of this study was to verify the effectiveness of the new ACAP Confirm® algorithm in managing atrial capture in the medium term in comparison with early post-implantation testing.

Methods

Data were collected from 318 patients (66% male; mean age, 73±10 years); 237 of these patients underwent device implantation and 81 box changes in 31 Italian hospitals. Atrial threshold measurements were taken manually and automatically at different pulse widths before discharge and during follow-up (7±2 months) examination.

Results

The algorithm worked as expected in 73% of cases, considering all performed tests. The success rate was 65% and 88% pre-discharge and during follow-up examination (p<0.001), respectively, in patients who had undergone implantation. We did not detect any difference in the performance of the algorithm as a result of the type of atrial lead used. The success rate was 70% during pre-discharge testing in patients undergoing device replacement.

Considering all examination types, manual and automatic measurements yielded threshold values of 1.07±0.47 V and 1.03±0.47 V at 0.2-ms pulse duration (p=0.37); 0.66±0.37 V and 0.67±0.36 V at 0.4 ms (p=0.42); and 0.5±0.28 V and 0.5±0.29 V at 1 ms (p=0.32).

Conclusions

The results show that the algorithm works before discharge, and its reliability increases over the medium term. The algorithm also proved accurate in detecting the atrial threshold automatically. The possibility of activating it does not seem to be influenced by the lead type used, but by the time from implantation.

In silico Models of Alcohol Dependence and Treatment

In this paper we view alcohol dependence and the response to treatment as a recurrent bio-behavioral process developing in time and propose formal models of this process combining behavior and biology in silico. The behavioral components of alcohol dependence and treatment are formally described by a stochastic process of human behavior, which serves as an event generator challenging the metabolic system. The biological component is driven by the biochemistry of alcohol intoxication described by deterministic models of ethanol pharmacodynamics and pharmacokinetics to enable simulation of drinking addiction in humans. Derived from the known physiology of ethanol and the literature of both ethanol intoxication and ethanol absorption, the different models are distilled into a minimal model (as simple as the complexity of the data allows) that can represent any specific patient. We use these modeling and simulation techniques to explain responses to placebo and ondansetron treatment observed in clinical studies. Specifically, the response to placebo was explained by a reduction of the probability of environmental reinforcement, while the effect of ondansetron was explained by a gradual decline in the degree of ethanol-induced neuromodulation. Further, we use in silico experiments to study critical transitions in blood alcohol levels after specific average number of drinks per day, and propose the existence of two critical thresholds in the human - one at 5 and another at 11 drinks/day - at which the system shifts from stable to critical and to super critical state indicating a state of alcohol addiction. The advantages of such a model-based investigation are that (1) the process of instigation of alcohol dependence and its treatment can be deconstructed into meaningful steps, which allow for individualized treatment tailoring, and (2) physiology and behavior can be quantified in different (animal or human) studies and then the results can be integrated in silico.

Pacing features that mimic malfunction: a review of current programmable and automated device functions that cause confusion in the clinical setting

Modern implantable devices capable of pacing are armed with a multitude of programmable and automated features. While some features represent important advances in device safety and performance, many can also mimic device malfunction. This article discusses these features in terms of the confusion they may cause and highlights important clinical clues that aid in their recognition.

Clinical Evaluation of Two Different Evoked Response Sensing Methods for Automatic Capture Detection in the Left Ventricle

BACKGROUND

This acute feasibility study compared two different automatic capture detection methodologies, the reduced coupling capacitor (RCC) and the independent pace/sense (IPS) methods, for the left ventricle (LV).

METHODS

LV threshold tests were performed in DDD mode, with LV-only and bi-ventricular (BiV) pacing using an external cardiac resynchronization therapy (CRT) defibrillator. Evoked response (ER) signals from LV leads were recorded using the LV(Tip) (LV(Tip)-->Can) and LV(Ring) (LV(Ring)-->Can) to empty pulse generator (Can) housing sensing vectors to evaluate the two methodologies. Pacing vector, pulse duration, atrioventricular delay, and interventricular delay were varied to assess their effects on ER. The minimum ER amplitude (ER(min)), signal-to-artifact ratio (SAR), and ER amplitude voltage dependence were evaluated. ER(min)>2 mV and SAR(min)>2 define potential automatic LV capture detection for the two methodologies.

RESULTS

Data collected from 43 patients (63.7 +/- 11.0 years) were analyzed, including unipolar and bipolar (14/29) LV leads. Neither ER sensing method was affected by changing the pacing vector. The LV(Tip)-->Can ER(min) was significantly decreased at the 1.0-ms pulse duration when compared to 0.4-ms (p < 0.05). During BiV pacing, LV(Tip)-->Can ER(min) increased at negative interventricular delays and decreased at positive interventricular delays relative to simultaneous pacing. LV(Tip)-->Can resulted in fewer patients with sufficient ER characteristics for capture detection, albeit only significantly at the extended pulse duration (79% vs 97%, p < 0.05) and at simultaneous and positive interventricular delays (81% vs 97%, p < 0.05).

CONCLUSIONS

Though LV capture detection was feasible using both investigated methods, the RCC method (LV(Tip)-->Can) sensitivity to the evaluated pacing parameters suggests the IPS method (LV(Ring)-->Can) provides a more robust performance.

Automatic measurement of atrial pacing thresholds in dual-chamber pacemakers: Clinical experience with atrial capture management

BACKGROUND

The Medtronic EnPulse pacemaker incorporates the new atrial capture management (ACM) algorithm to automatically measure atrial capture thresholds and subsequently manage atrial pacing outputs.

OBJECTIVES

The purpose of this study was to evaluate the clinical performance of ACM.

METHODS

Two hundred patients with an indication for a dual-chamber pacemaker underwent implantation. ACM thresholds and manually measured atrial pacing thresholds were assessed at follow-up visits. Clinical equivalence was defined as the ACM-measured threshold being within -0.25 V to +0.5 V of the manually measured threshold. The clinician analyzed all ACM measurements performed in-office for evidence of proarrhythmia.

RESULTS

All 200 implanted patients had a 1-month visit, and validated manual and in-office ACM threshold data were available for 123 patients. The ACM threshold was 0.595 +/- 0.252 V, and the manual threshold was 0.584 +/- 0.233 V. The mean difference was 0.010 V with a 95% confidence interval of (-0.001, 0.021). The mean difference over all visits was 0.011 V. For all patients, the individual threshold differences were within the range of clinical equivalence at all visits. No atrial arrhythmias were observed during 892 ACM tests in 193 patients.

CONCLUSION

This study demonstrated that the ACM algorithm is safe, accurate, and reliable over time. ACM was demonstrated to be clinically equivalent to the manual atrial threshold test in all patients at 1 month and over the entire follow-up period of up to 6 months. ACM ensures atrial capture, may save time during follow-up, and can be used to manage atrial pacing outputs.

Pacemaker automaticity

The automated measurement of the main electrical parameters of pacemakers, such as battery voltage, current drain, pacing impedance, sensing levels, and pacing thresholds enables a continuous monitoring of the adequate functioning of the implanted device. New technologies in device interrogation, data transfer, and patient alert functions will enhance therapy safety significantly by the immediate detection of all, including transient, device malfunctions. The introduction of this technology will result in major changes in follow-up of patients with pacemakers. Technically we are very close to the goal of a fully automated pacemaker, although clinical acceptance lags behind. Clinical studies are necessary to demonstrate the beneficial effects of automated device functions, with regard to the main rationales for pacemaker automatization: increased patient safety, improved quality of life, and an improvement in the cost-benefit ratio in pacemaker therapy.