Blunted cyclic variation of heart rate predicts mortality risk in post-myocardial infarction, end-stage renal disease, and chronic heart failure patients

Figures

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  Figure 1. Assessment of the amplitude of CVHR (ACV) by a signal averaging method. CVHR appears as characteristic cyclic dips in R-R intervals of night-time ECG recordings (A). To calculate the ACV, all dips attributable to CVHR are detected, and temporal positions of the nadir point of dips (d1, d2, d3,…, dn) are determined using automated algorithms, such as ACAT.9,10 Second, segments of interval data (length, 120 s) around the nadir points (s1, s2, s3,…, sn), where segments that surround adjacent nadir points can overlap, are collected. Third, all segments are aligned at the nadir points and averaged (B). ACV is then measured as the dip depth of the signal-averaged interval curve, i.e. the vertical distance from the nadir of central dip to the line connecting the local maxima on both sides of the dip.

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  Figure 2. Representative samples of signal averaging assessments of ACV in a 74-year-old male patient who survived a MI more than 25 months prior (A) and in a 75-year-old female patient who died 2 months after an index MI (B). (A) ACV = 5.1 ln (ms) and FCV = 44.8 cph; (B) ACV = 2.9 ln (ms) and FCV = 10.7 cph.

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  Figure 3. Kaplan–Meier curves of mortality for patients in four cohorts stratified by ACV. Mortality probabilities in four cohorts of patients stratified by the same criteria of ACV cut-off values (4.0 and 3.0) that were optimized for the post-MI 1 cohort: low-risk patients (ACV >4.0), intermediate-risk patients (3.0–4.0), and high-risk patients (≤3.0). The mortality probabilities differed significantly among the post-MI 1 (log-rank χ2 = 105.8, P < 0.001); post-MI 2 (38.9, P < 0.001); ESRD (28.7, P < 0.001); and CHF (9.9, P = 0.007) cohorts.