How smart should pacemakers Be?

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.

Polymorphisms associated with ventricular tachyarrhythmias: rationale, design, and endpoints of the 'diagnostic data influence on disease management and relation of genomics to ventricular tachyarrhythmias in implantable cardioverter/defibrillator patients (DISCOVERY)' study

Implantable cardioverter-defibrillator (ICD) therapy is effective in primary and secondary prevention for patients who are at high risk of sudden cardiac death. However, the current risk stratification of patients who may benefit from this therapy is unsatisfactory. Single nucleotide polymorphisms (SNPs) are DNA sequence variations occurring when a single nucleotide in the genome differs among members of a species. A novel concept has emerged being that these common genetic variations might modify the susceptibility of a certain population to specific diseases. Thus, genetic factors may also modulate the risk for arrhythmias and sudden cardiac death, and identification of common variants could help to better identify patients at risk. The DISCOVERY study is an interventional, longitudinal, prospective, multi-centre diagnostic study that will enrol 1287 patients in approximately 80 European centres. In the genetic part of the DISCOVERY study, candidate gene polymorphisms involved in coding of the G-protein subunits will be correlated with the occurrence of ventricular arrhythmias in patients receiving an ICD for primary prevention. Furthermore, in order to search for additional sequence variants contributing to ventricular arrhythmias, a genome-wide association study will be conducted if sufficient a priori evidence can be gathered. In the second part of the study, associations of SNPs with ventricular arrhythmias will be sought and a search for potential new biological arrhythmic pathways will be investigated. As it is a diagnostic study, DISCOVERY will also investigate the impact of long-term device diagnostic data on the management of patients suffering from chronic cardiac disease as well as medical decisions made regarding their treatment.

Reduction of RV Pacing by Continuous Optimization of the AV Interval

BACKGROUND

In patients requiring permanent pacing, preservation of intrinsic ventricular activation is preferred whenever possible. The Search AV+ (SAV+) algorithm in Medtronic EnPulsetrade mark dual-chamber pacemakers can increase atrioventricular (AV) intervals to 320 ms in patients with intact or intermittent AV conduction. This prospective, multicenter study compared the percentage of ventricular pacing with and without AV interval extension.

METHODS

Among 197 patients enrolled in the study, the percentage of ventricular-paced beats was evaluated via device diagnostics at the 1-month follow-up. Patient cohorts were defined by clinician assessment of conduction via a 1:1 AV conduction test at the 2-week follow-up. The observed percentage of ventricular pacing with SAV + ON and the predicted percentage of ventricular pacing with SAV + OFF were determined from the SAV + histogram data for the period between the 2-week and 1-month follow-up visits.

RESULTS

Of 197 patients, 110 (55.8%) had intact 1:1 AV conduction, of which 109 had 1-month data. SAV + remained ON in 99/109 patients; 10 patients had intrinsic A-V conduction intervals beyond SAV + nominal and therefore SAV + disabled. The mean percentage of ventricular pacing in the 109 patients was SAV+ ON = 23.1% (median 3.7%) versus SAV + OFF = 97.2% (median 99.7%). In 87 patients without 1:1 AV conduction, SAV + was programmed OFF in 6, automatically disabled in 52, and remained ON in 29. In 8 of these patients, 80-100% reduction in ventricular pacing was observed with SAV + ON.

CONCLUSION

The Search AV+ algorithm in the EnPulse pacemaker effectively promotes intrinsic ventricular activation and substantially reduces unnecessary ventricular pacing.

Automatic sensor algorithms expedite pacemaker follow-ups

OBJECTIVE

Automatic algorithms can be used to optimize settings and reduce the duration of pacemaker (PM) clinical follow-up.

METHODS

This study prospectively evaluated 87 patients (74.2 +/- 10.7 years old, 52% men) who received PM with the Autoslope algorithm. Patients randomized to the manual group (group M, n = 43) performed a walk test and used sensor-indicated rate histograms to adjust the sensor, while in the automatic group (group A, n = 44) the sensor was automatically adjusted by the Autoslope. The patients were followed for 6 months. Follow-up time required for device interrogation and optimal sensor set-up, and the number of sensor parameters reprogramming were recorded. Changes in the patients' activity level were also evaluated.

RESULTS

Group A required significantly less follow-up time than group M (9.4 +/- 5.7 min vs 13.5 +/- 8.5 min, P = 0.0002). The average number of sensor parameters reprogrammed during visits was significantly lower in group A than M (0.6 +/- 0.9 vs 0.9 +/- 1.3, P = 0.048). Threshold was adjusted 34.4% of the time in the sensor evaluations in group M versus 12.9% in group A (P = 0.0004). Although more patients in group A reported being more active, the changes in patients' activity level did not lead to increasing sensor setup time or number of parameter reprogramming in either group.

CONCLUSIONS

Auto sensor adjustment required less time during routine PM clinical follow-up by reducing steps needed for manual sensor threshold adjustment.

Automated pacemaker function

Pacemakers have used automated functions since the introduction of the inhibited mode more than 30 years ago. Currently, virtually all aspects of pacemaker function are subject to automated control, including automated threshold tracking and sensitivity adjustment. These features are designed to enhance patient safety and quality of life, extend battery life, and simplify pacemaker programming and follow-up for health care providers. Many of these automated algorithms are still in evolution and the clinical benefits are not clearly demonstrated for all functions. Although pacemaker function will become increasingly automated, these features should not be accepted uncritically and without demonstrating benefit to the pacemaker patient.