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  • Title: SCD-HeFT: Use of R-R interval statistics for long-term risk stratification for arrhythmic sudden cardiac death.
    Author: Au-Yeung WT, Reinhall PG, Poole JE, Anderson J, Johnson G, Fletcher RD, Moore HJ, Mark DB, Lee KL, Bardy GH.
    Journal: Heart Rhythm; 2015 Oct; 12(10):2058-66. PubMed ID: 26096609.
    Abstract:
    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.
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