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.

Dois ensaios em finanças

In the first chapter, we test some stochastic volatility models using options on the S&P 500 index. First, we demonstrate the presence of a short time-scale, on the order of days, and a long time-scale, on the order of months, in the S&P 500 volatility process using the empirical structure function, or variogram. This result is consistent with findings of previous studies. The main contribution of our paper is to estimate the two time-scales in the volatility process simultaneously by using nonlinear weighted least-squares technique. To test the statistical significance of the rates of mean-reversion, we bootstrap pairs of residuals using the circular block bootstrap of Politis and Romano (1992). We choose the block-length according to the automatic procedure of Politis and White (2004). After that, we calculate a first-order correction to the Black-Scholes prices using three different first-order corrections: (i) a fast time scale correction; (ii) a slow time scale correction; and (iii) a multiscale (fast and slow) correction. To test the ability of our model to price options, we simulate options prices using five different specifications for the rates or mean-reversion. We did not find any evidence that these asymptotic models perform better, in terms of RMSE, than the Black-Scholes model. In the second chapter, we use Brazilian data to compute monthly idiosyncratic moments (expected skewness, realized skewness, and realized volatility) for equity returns and assess whether they are informative for the cross-section of future stock returns. Since there is evidence that lagged skewness alone does not adequately forecast skewness, we estimate a cross-sectional model of expected skewness that uses additional predictive variables. Then, we sort stocks each month according to their idiosyncratic moments, forming quintile portfolios. We find a negative relationship between higher idiosyncratic moments and next-month stock returns. The trading strategy that sells stocks in the top quintile of expected skewness and buys stocks in the bottom quintile generates a significant monthly return of about 120 basis points. Our results are robust across sample periods, portfolio weightings, and to Fama and French (1993)’s risk adjustment factors. Finally, we identify a return reversal of stocks with high idiosyncratic skewness. Specifically, stocks with high idiosyncratic skewness have high contemporaneous returns. That tends to reverse, resulting in negative abnormal returns in the following month.

Bob Mann, UM Football, punt return vs Pitt, 1947

[Bob Mann, #81; Pete Elliott, #45; Wally Teninga, #42; Peter Dendrinos, #78]