BACKGROUND: Measurement of high-frequency (HF) spectral power of heart rate (HR) variability has not been able to identify the patients at risk of sudden cardiac death (SCD) despite the experimental evidence of protective role of vagal activity for fatal arrhythmias. AIM: We developed a novel respiratory sinus arrhythmia (RSA) analysis method and tested its ability to predict SCD after an acute myocardial infarction. METHOD: The RSA analysis method was developed in 13 subjects from simultaneous recordings of respiration and R-R intervals. An adaptive threshold was computed based on the zero-phase forward and reverse digital filtering in the analysis of RSA. With this method, only respiration-related R-R interval fluctuations are included. The prognostic power of RSA, analyzed from 24-hour electrocardiographic recordings, was subsequently assessed in a large postinfarction population including 1631 patients with mean follow-up of 40 +/- 17 months. RESULTS: Depressed RSA was a strong predictor of SCD (hazard ratio 7.4; 95% CI 3.6-15.1; P 0.0001) but only a weak predictor of non-SCD. The RSA index remained an independent predictor of SCD after adjustments for ejection fraction and other clinical risk variables (RR 4.7; 95% CI 2.28-9.85). CONCLUSIONS: Reduced respiratory-related HR dynamics, detected by RSA index, are a specific marker of an increased risk of SCD among postinfarction patients.     «

BACKGROUND: Measurement of high-frequency (HF) spectral power of heart rate (HR) variability has not been able to identify the patients at risk of sudden cardiac death (SCD) despite the experimental evidence of protective role of vagal activity for fatal arrhythmias. AIM: We developed a novel respiratory sinus arrhythmia (RSA) analysis method and tested its ability to predict SCD after an acute myocardial infarction. METHOD: The RSA analysis method was developed in 13 subjects from simultaneous...     »