Uncovering the dynamics of cardiac systems using stochastic pacing and frequency domain analyses

Alternans of cardiac action potential duration (APD) is a well-known arrhythmogenic mechanism which results from dynamical instabilities. The propensity to alternans is classically investigated by examining APD restitution and by deriving APD restitution slopes as predictive markers. However, experiments have shown that such markers are not always accurate for the prediction of alternans. Using a mathematical ventricular cell model known to exhibit unstable dynamics of both membrane potential and Ca2+ cycling, we demonstrate that an accurate marker can be obtained by pacing at cycle lengths (CLs) varying randomly around a basic CL (BCL) and by evaluating the transfer function between the time series of CLs and APDs using an autoregressive-moving-average (ARMA) model. The first pole of this transfer function corresponds to the eigenvalue (λalt) of the dominant eigenmode of the cardiac system, which predicts that alternans occurs when λalt≤−1. For different BCLs, control values of λalt were obtained using eigenmode analysis and compared to the first pole of the transfer function estimated using ARMA model fitting in simulations of random pacing protocols. In all versions of the cell model, this pole provided an accurate estimation of λalt. Furthermore, during slow ramp decreases of BCL or simulated drug application, this approach predicted the onset of alternans by extrapolating the time course of the estimated λalt. In conclusion, stochastic pacing and ARMA model identification represents a novel approach to predict alternans without making any assumptions about its ionic mechanisms. It should therefore be applicable experimentally for any type of myocardial cell.

Electrogram analysis and pacing are complimentary for recognition of abnormal conduction and far-field potentials during substrate mapping of infarct-related ventricular tachycardia.

BACKGROUND Mapping to identify scar-related ventricular tachycardia re-entry circuits during sinus rhythm focuses on sites with abnormal electrograms or pace-mapping findings of QRS morphology and long stimulus to QRS intervals. We hypothesized that (1) these methods do not necessarily identify the same sites and (2) some electrograms are far-field potentials that can be recognized by pacing. METHODS AND RESULTS From 12 patients with coronary disease and recurrent ventricular tachycardia undergoing catheter ablation, we retrospectively analyzed electrograms and pacing at 546 separate low bipolar voltage (<1.5 mV) sites. Electrograms were characterized as showing evidence of slow conduction if late potentials (56%) or fractionated potentials (76%) were present. Neither was present at (13%) sites. Pacing from the ablation catheter captured 70% of all electrograms. Higher bipolar voltage and fractionation were independent predictors for pace capture. There was a linear correlation between the stimulus to QRS duration during pacing and the lateness of a capturing electrogram (P<0.001), but electrogram and pacing markers of slow conduction were discordant at 40% of sites. Sites with far-field potentials, defined as those that remained visible and not captured by pacing stimuli, were identified at 48% of all pacing sites, especially in areas of low bipolar voltage and late potentials. Initial radiofrequency energy application rendered 74% of targeted sites electrically unexcitable. CONCLUSIONS Far-field potentials are common in scar areas. Combining analysis of electrogram characteristics and assessment of pace capture may refine identification of substrate targets for radiofrequency ablation.