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

Background

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.

Objectives

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.

Methods

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.

Results

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).

Conclusion

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.

Introduction

Implantable cardioverter-defibrillator (ICD) therapy decreases mortality in select post–myocardial infarction and congestive heart failure (CHF) patients.1, 2 However, many deaths occur from mechanisms unamenable to ICD therapy. New tools to better identify candidates who would benefit the most from ICD therapy remains a challenge.³ The proof of this can be found in the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT), in which only 182 of the 811 CHF patients who received ICD therapy (22.4%) received ICD shocks for ventricular tachycardia or ventricular fibrillation (VF) over a median follow-up period of 45.5 months.⁴

It has been shown in the past that traditional heart rate variability (HRV) measures in the time and frequency domains have prognostic power.⁵ However, HRV measures have not undergone well-conducted ICD clinical trials, nor do they harbor significant power to serve as a practical risk predictor. More recently, methods based on nonlinear dynamics, such as detrended fluctuation analysis (DFA) and heart rate turbulence (HRT), which looks at the return to the equilibrium of heart rate after a premature ventricular contraction (PVC), may yield superior predictive results.6, 7 DFA fractal exponents of heart rate time series have been shown to correlate with autonomic tone, while HRT parameters correlate with baroreflex sensitivity.8, 9 In this article, we investigated the effectiveness of these methods, together with traditional Holter methods, in predicting which patients enrolled in SCD-HeFT would benefit or not benefit from their ICD over the duration of the study.