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


METHODS
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.
Department of Medical Education, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Mari
M
Suzuki
Takahashi
T
Hiroshi
Toru
T
Aoyama
Miho
M
Tanaka
Hideki
H
Ishii
Masaya
M
Kisohara
Narushi
N
Iizuka
Toyoaki
T
Murohara
Junichiro
J
Hayano
D000367:Age Factors; D000368:Aged; D016009:Chi-Square Distribution; D015716:Electrocardiography, Ambulatory; D005260:Female; D017709:Fractals; D006339:Heart Rate; D006801:Humans; D007564:Japan / Q000453:epidemiology; D053208:Kaplan-Meier Estimate; D007676:Kidney Failure, Chronic / Q000401:mortality / Q000503:physiopathology / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D011446:Prospective Studies; D006435:Renal Dialysis / Q000009:adverse effects / Q000401:mortality*; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors