Patients with chronic atrial fibrillation (AF) and symptomatic bradycardia often receive ventricular-based pacemakers. However, many of these patients continue to have symptoms of palpitations, which may be due to ventricular rate variability. It has previously been shown that continuous ventricular pacing during AF has a stabilizing effect on the ventricular rate. Hence, a study was initiated to determine whether a patient-specific optimal ventricular standby rate that reduces the ventricular rate variability, without over-pacing, could be predicted. A ventricular rate stabilization (VRS) pacing algorithm that increases the pacing rate until instability is reduced below a threshold was developed. The VRS algorithm was utilized to determine a patient-specific standby rate in 15 patients with chronic AF, intact AV nodal conduction, and implanted pacemakers. The computer algorithm controlled a pacemaker programmer to automatically change the pacemaker's ventricular pacing rate via telemetry. Patients were studied for 15 minutes with VRS and for 15 minutes with 50 ppm fixed rate pacing (control). The results were as follows: (1) VRS versus control = P < 0.05; (2) mean ventricular pacing rate (ppm): 77 +/- 13 versus 50 +/- 0; (3) mean ventricular rate (beats/min): 82 +/- 13 versus 79 +/- 12; (4) ventricular rate coefficient of variation (%): 11 +/- 1 versus 22 +/- 5; (5) percent pacing: 75 +/- 8 versus 6 +/- 8; (6) percent of RR intervals less than minimum pacing interval eliminated: 58 +/- 12; (8) regression analysis: mean VRS pacing rate (beats/min) = 0.96 x mean control ventricular rate + 2.3, r2 = 0.85. We concluded that: (1) a moderate increase in the ventricular pacing rate was required to substantially stabilize the ventricular rate; (2) the resulting mean ventricular rate increased marginally; (3) a majority of RR cycles less than each patient's minimum pacing interval were eliminated; and (4) there was a linear relationship between the mean ventricular rate during control and the optimal ventricular pacing rate. Thus, a ventricular pacing rate close to the mean ventricular rate during control consistently reduced the ventricular variability. Although pacing at an increased ventricular standby rate reduces variability at rest, the optimal solution would likely be an adaptive rate algorithm that changes the ventricular standby rate as the mean intrinsic rate varies.

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The electrical interaction between the heart and an artificial pacemaker is often complex. Because of the sophistication and diversity of dual-chamber device algorithms, even experienced cardiologists can have difficulty interpreting paced electrocardiograms (ECG's). In order to study heart-pacemaker interaction (HPI), a computer model of the cardiac conduction system has been developed which includes the effects of artificial pacemaker function and failure. The stochastic network model of cardiac conduction consists of five vertices, each representing a functional electrophysiologic element. Electrophysiologic multidimensional conditional probability functions determine the depolarization status of each vertex. The atrioventricular (AV) node is emulated using a mathematical model which includes the influence of past cycle lengths on AV nodal conduction time. Twenty-three classes of arrhythmias may be simulated and, for pacing simulation, one of 12 antibradycardia pacing modes may be chosen. Random effects of pacemaker malfunction including oversensing, undersensing, or failure-to-capture may be simulated through the use of probability distribution functions. This model should prove useful in the development of pacemaker algorithms, determining patient-specific pacemaker therapy, and predicting causes for apparent pacemaker malfunction. The model has been used in the development of an expert system to analyze paced ECG's for pacemaker function and malfunction.

Implantable antitachycardia devices suffer a high false-positive rate of delivery of therapy because current detection schemes based upon ventricular rate and rate variations are excessively sensitive at the cost of specificity. Several methods have been proposed for providing complementary information derived from morphologic analysis of intraventricular electrograms in order to increase specificity. The majority of these techniques have utilized bipolar electrogram analysis to detect changes in ventricular activation indicative of ventricular tachycardia. Whether bipolar or unipolar intracardiac electrogram analysis might be preferred for discriminating ventricular tachycardia from sinus rhythm has not been determined. In this study, a previously demonstrated method for identification of ventricular tachycardia using intracardiac electrograms, correlation waveform analysis, was used to analyze both unipolar and bipolar signals during sinus rhythm and ventricular tachycardia recorded during electrophysiology studies of 15 patients with inducible sustained monomorphic ventricular tachycardia. Correlation waveform analysis consistently discriminated between all depolarizations during ventricular tachycardia in 14/15 patients (93%) using either electrogram configuration; 13 of the 14 patients were common to both groups. Of these patients, 8/15 (53%) had greater separation between sinus rhythm and ventricular waveforms with bipolar electrogram analysis while 7/15 (47%) had greater separation with unipolar electrogram analysis. We conclude that morphologic analysis of unipolar and bipolar electrograms may be equally effective in distinguishing ventricular tachycardia from sinus rhythm. For individual patients, either a unipolar or bipolar ventricular configuration may be preferable, and should be chosen on a patient-specific basis during electrophysiology study prior to antitachycardia device implantation.

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