RF Ablation of VT in Chronic Cocaine Users Background Cocaine use is a known but rare cause of cardiac arrhythmias. Ventricular arrhythmias related to cocaine may not respond to antiarrhythmic drugs and may need treatment with radiofrequency ablation. Objectives We describe the clinical and electrophysiological characteristics of cocaine-related ventricular tachycardia (VT) from a multicenter registry. Methods Subjects presenting with VT related to cocaine use and being considered for radiofrequency ablation have been included in the study. Patients who were refractory to maximal medical therapy underwent radiofrequency ablation of the VT. Clinical, procedural variables, efficacy, and safety outcomes were assessed. Results A total of 14 subjects met study criteria (age 44 ± 13, range 18- to 68-year-old with 79% male, 71% Caucasian). MRI showed evidence of scar only in 43% of patients (6/14). The mechanism of VT was focal in 50% (n = 7) and scar related reentry in 50% (n = 7) based on 3D mapping. The mean VT cycle length was 429 ± 96 milliseconds. The site of origin was epicardial in 16% (3/18) of VTs. Most clinical VTs were hemodynamically stable (75%). Mean ejection fraction at the time of admission was 44 ± 14%. Duration of procedure was 289 ± 50 minutes. One subject developed pericardial tamponade requiring drainage. At 18 ± 11 months follow-up, freedom from arrhythmia was seen in 86% (1 case lost to follow-up and 2 died). Conclusion Radiofrequency ablation is not only feasible but also safe and effective in patients who have drug refractory VT related to chronic cocaine use. © 2014 Wiley Periodicals, Inc.

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.