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 (alpha(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 x phosphate independently predicted mortality. Whereas all nonlinear HRV measures predicted mortality, only decreased scaling exponent alpha(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 alpha(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 alpha(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 alpha(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 alpha(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.