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. 2012 Sep;7(9):1454-60.
doi: 10.2215/CJN.09430911. Epub 2012 Jun 21.

Nonlinear measures of heart rate variability and mortality risk in hemodialysis patients

Mari Suzuki  1 , Takahashi Hiroshi, Toru Aoyama, Miho Tanaka, Hideki Ishii, Masaya Kisohara, Narushi Iizuka, Toyoaki Murohara, Junichiro Hayano
Affiliations

Nonlinear measures of heart rate variability and mortality risk in hemodialysis patients

Mari Suzuki et al. Clin J Am Soc Nephrol. 2012 Sep.

Abstract

Background and objectives: Nonlinear measures of heart rate variability (HRV) have gained recent interest as powerful risk predictors in various clinical settings. This study examined whether they improve risk stratification in hemodialysis patients.

Design, setting, participants, & measurements: To assess heart rate turbulence, deceleration capacity, fractal scaling exponent (α(1)), and other conventional HRV measures, 281 hemodialysis patients underwent 24-hour electrocardiography between January 2002 and May 2004 and were subsequently followed up.

Results: During a median 87-month follow-up, 77 patients (27%) died. Age, left ventricular ejection fraction, serum albumin, C-reactive protein, and calcium × phosphate independently predicted mortality. Whereas all nonlinear HRV measures predicted mortality, only decreased scaling exponent α(1) remained significant after adjusting for clinical risk factors (hazard ratio per a 0.25 decrement, 1.46; 95% confidence interval [95% CI], 1.16-1.85). The inclusion of α(1) into a prediction model composed of clinical risk factors increased the C statistic from 0.84 to 0.87 (P=0.03), with 50.8% (95% CI, 20.2-83.7) continuous net reclassification improvement for 5-year mortality. The predictive power of α(1) showed an interaction with age (P=0.02) and was particularly strong in patients aged <70 years (n=208; hazard ratio, 1.87; 95% CI, 1.38-2.53), among whom α(1) increased the C statistic from 0.85 to 0.89 (P=0.01), with a 93.1% (95% CI, 59.3-142.0) continuous net reclassification improvement.

Conclusions: Scaling exponent α(1) that reflects fractal organization of short-term HRV improves risk stratification for mortality when added to the prediction model by conventional risk factors in hemodialysis patients, particularly those aged <70 years.

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Figures

Figure 1.
Figure 1.
Decreased scaling exponent α1 predicts increased risk for mortality in hemodialysis patients, particularly those <70 years of age. Kaplan–Meier curves for mortality in (A) all 281 hemodialysis patients and (B) 201 patients aged <70 years. In both panels, patients were stratified by scaling exponent α1 <1.1 and ≥1.1. Log-rank test chi-squared statistics are 15.46 (P<0.001) for A and 23.7 (P<0.001) for B.
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