Cardiac Device Algorithms for Optimal Outcomes in Patients with Sick Sinus Syndrome

Sick sinus syndrome (SSS) is a relatively common chronic condition affecting the electrical conduction system of the heart. In the absence of any reversible cause of SSS, the most effective treatment is implantation of a permanent pacemaker to maintain the heart rate and conduction sequence within the normal physiological parameters. Although atrial pacing may be sufficient, in practice most patients in the UK receive dual chamber pacing. There is considerable clinical evidence that long term ventricular pacing can have a deleterious effect on the heart function and can increase the risk of heart failure hospitalisation, especially in patients with pre-existing cardiac dysfunction. A link between the degree of right ventricular pacing (RVP) and the development of atrial fibrillation (AF) has also been demonstrated. Pacemaker manufacturers have therefore developed a variety of programmable modes, features and algorithms that can be utilised to reduce unnecessary RVP. These include prolongation of atrioventricular delay (AVD), with or without “search”, and pacemakers that offer minimal ventricular pacing. Although there are national UK guidelines on the use of appropriate pacemaker modes at implant, there is no such guidance on appropriate programming strategies. This research project sought to identify the implanting and programming practice in a large pacemaker service in County Durham and Darlington (CD&D) between 2006 and 2011. The records of 349 patients who were paced for SSS were studied, with a follow-up of up to 5 years. Pacemaker implantation practice was compared to national audit data. The association between programming strategies and degree of RVP was then explored. The results from this project showed there is a lack of consistency in the historical approach to reducing right ventricular pacing by the use of particular devices and algorithms. Devices with a minimal ventricular pace algorithm reduce the degree of RVP to as little as <4% per year, whereas algorithms that altered AVD were significantly less effective, reducing RVP to between 17% and 27% per year (years 1-3). The data were less robust in years 4 and 5 due to relative small data sizes in each algorithm grouping. There was no observable correlation between algorithm and the amount of AF, but a significant correlation was present between the degree of RVP and AF (p