Critical analysis of dual-chamber implantable cardioverter-defibrillator arrhythmia detection : results and technical considerations.

BACKGROUND: One of the perceived benefits of dual-chamber implantable cardioverter-defibrillators (ICDs) is the reduction in inappropriate therapy due to new detection algorithms. It was the purpose of the present investigation to propose methods to minimize bias during such comparisons and to report the arrhythmia detection clinical results of the PR Logic dual-chamber detection algorithm in the GEM DR ICD in the context of these methods. METHODS AND RESULTS: Between November 1997 and October 1998, 933 patients received the GEM DR ICD in this prospective multicenter study. A total of 4856 sustained arrhythmia episodes (n=311) with stored electrogram and marker channel were classified by the investigators; 3488 episodes (n=232) were ventricular tachycardia (VT)/ventricular fibrillation (VF), and 1368 episodes (n=149) were supraventricular tachycardia (SVT). The overall detection results were corrected for multiple episodes within a patient with the generalized estimating equations (GEE) method with an exchangeable correlation structure between episodes. The relative sensitivity for detection of sustained VT and/or VF was 100.0% (3488 of 3488, n=232; 95% CI 98.3% to 100%), the VT/VF positive predictivity was 88.4% uncorrected (3488 of 3945, n=278) and 78.1% corrected (95% CI 73.3% to 82.3%) with the GEE method, and the SVT positive predictivity was 100.0% (911 of 911, n=101; 95% CI 96% to 100%). CONCLUSIONS: A structured approach to analysis limits the bias inherent in the evaluation of tachycardia discrimination algorithms through the use of relative VT/VF sensitivity, VT/VF positive predictivity, and SVT positive predictivity along with corrections for multiple tachycardia episodes in a single patient.

An empirical Bayes formulation of cohort models in cancer epidemiology.

This paper concerns the incidence rates of malignant skin melanoma for several age-sex groups and time periods in three geographic regions, uses a method of cohort analysis and employs a two-stage random effects model. The first stage entails the assumption that the within-region variation in the frequency of disease incidence for a fixed age-sex-cohort group has a Poisson distribution with mean proportional to the population at risk. The second stage, after adjusting for age and sex, entails the assumption that the between-region geographic variation in the logarithm of the true incidence rate has a prior distribution with parameters estimated by the method of maximum likelihood. After adjusting for age effects, we estimate random geographic-specific cohort effects for each sex with use of an empirical Bayes method and compare the results with the usual multiplicative Poisson model that assumes fixed geographic-specific cohort effects for each sex. This comparison shows that the method presented here provides more stable estimates of geographic-specific cohort effects, and in addition the random effects model describes these data more adequately.