Assessment of adaptive rate response provided by accelerometer, minute ventilation and dual sensor compared with normal sinus rhythm during exercise: a self-controlled study in chronotropically competent subjects

Pacemaker-Mediated Tachycardia: A Case Report

Pacemakers respond to the physical activity of patients by increasing the heart rate to compensate for the increased demand. They sense the physical activity of the patient with the help of different types of sensors that work with different mechanisms. In this case, we are presenting a 74-year-old male who was experiencing intermittent chest pain and palpitations. Following interrogation, he was found to have atrial paced tachycardias due to a tracking of the atrial tachycardia.

Benefit of Rate Response with Closed-Loop Stimulation in Patients with Difficult Hemodialysis

Rate-responsive pacing is known to improve quality of life (QOL) in patients with sick sinus syndrome and chronotropic incompetence. However, the sensors for rate response include accelerometers, closed-loop stimulation (CLS), and minute ventilation sensors (MV sensors), each of which has a different mode of action. For this reason, it is important to select appropriate sensors that match the daily habits and behavioral patterns of the patient. For example, young and active patients are expected to have a rate increase when an accelerometer is used, while elderly patients and patients with a physical disability who are only able to move slowly often have a poor response to the accelerometer. MV sensors are therefore better suited to these patients. Furthermore, CLS is considered effective for patients who require an increase in heart rate when at rest, for example, patients undergoing maintenance dialysis.We describe a representative case, demonstrating the effectiveness of closed-loop stimulation in a patient with hypotension during dialysis.

Utility of transthoracic impedance and novel algorithm for sleep apnea screening in pacemaker patient

Background

Pacing patients was revealed with a high prevalence of sleep disorder, but mostly undiagnosed. The pacemaker with transthoracic impedance sensor and novel algorithm could identify sleep apnea (SA) event. This study aimed to evaluate accuracy of pacemaker in sleep apnea diagnosis.

Methods

This is a prospective study which enrolled patients implanted with pacemakers integrated with transthoracic impedance sensor and SA-identified algorithm (AP Scan). All patients underwent a polysomnography (PSG). The apnea and hypopnea index (AHI) of the PSG (PSG-AHI) and the respiratory disturbance index (RDI) of the pacemaker (PM-RDI) were recorded on the same night. The correlation between two methods was evaluated by the kappa coefficient, receiver operating characteristic (ROC) curves, and Bland and Altman statistics.

Results

Sixty-four patients were enrolled, who had never been diagnosed with SAS or underwent PSG exam. After PSG examination, 76.4% patients were diagnosed as combining with SA (20% severe, 18.2% moderate, and 38.2% mild). RDI calculated by PM has a strong positive correlation with PSG-AHI (r = 0.76, p < 0.001, 95% CI 0.61–0.85). The optimal cutoff value of PM-RDI for advanced SAS (PSG-AHI ≥ 15) diagnosis was 26, with AUC of 0.89 (95% CI 0.77 to 0.96, p < 0.001). The best cutoff value for severe SA (PSG-AHI ≥ 30) identification was 41, with a sensitivity of 81.6%, a specificity of 88.6%.

Conclusions

Pacemaker patients present a high prevalence of undiagnosed SA. Detection of SA by pacemaker is feasible and accurate in SA screening and monitoring.