SCD-HeFT: Use of R-R interval statistics for long-term risk stratification for arrhythmic sudden cardiac death

Abstract

In the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT), a significant fraction of the patients with congestive heart failure ultimately did not die suddenly of arrhythmic causes. Patients with CHF will benefit from better tools to identify if implantable cardioverter-defibrillator (ICD) therapy is needed. We aimed to identify predictor variables from baseline SCD-HeFT patients' R-R intervals that correlate to arrhythmic sudden cardiac death (SCD) and mortality and to design an ICD therapy screening test. Ten predictor variables were extracted from prerandomization Holter data from 475 patients enrolled in the ICD arm of the SCD-HeFT by using novel and traditional heart rate variability methods. All variables were correlated to SCD using the Mann-Whitney-Wilcoxon test and receiver operating characteristic analysis. ICD therapy screening tests were designed by minimizing the cost of false classifications. Survival analysis, including log-rank test and Cox models, was also performed. A short-term fractal exponent, α1, and a long-term fractal exponent, α2, from detrended fluctuation analysis, the ratio of low- to high-frequency power, the number of premature ventricular contractions per hour, and the heart rate turbulence slope are all statistically significant for predicting the occurrences of SCD (P < .001) and survival (log-rank, P < .01). The most powerful multivariate predictor tool using the Cox proportional hazards regression model was α2 with a hazard ratio of 0.0465 (95% confidence interval 0.00528-0.409; P < .01). Predictor variables extracted from R-R intervals correlate to the occurrences of SCD and distinguish survival functions among patients with ICDs in SCD-HeFT. We believe that SCD prediction models should incorporate Holter-based R-R interval analysis to refine ICD patient selection, especially to exclude patients who are unlikely to benefit from ICD therapy.