Why the study?
Does the intermediate-term fractal scaling exponent (DFAα2) predict sinus node dysfunction in patients with sinus bradycardia?
Does the intermediate-term fractal scaling exponent (DFAα2) predict sinus node dysfunction in patients with sinus bradycardia?
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Inappropriate reduction of the intermediate-term fractal scaling exponent (DFAα2) is a robust non-invasive measure that could improve diagnostic performance in detecting sinus node dysfunction.
BACKGROUND: The aim of the present study was to characterize the heart rate dynamics of sinus bradycardia (SB) from sinus node dysfunction (SND) using non-linear dynamical system analysis. No data are yet available on how the dynamics change in the presence of SND. METHODS AND RESULTS: Conventional time and frequency domain analysis, the short- (DFAα(1)) and intermediate-term fractal scaling exponent (DFAα(2)), approximate entropy (ApEn) and sample entropy (SampEn) were calculated in 60-min sinus RR interval data of SB from 24-h ambulatory electrocardiograms of 110 patients: 44 SND patients, 44 age-matched controls, and 22 younger controls. All of the time and frequency domain parameters, ApEn and SampEn, were significantly reduced in the age-matched control group, compared with the young control group. DFAα(1) and DFAα(2) increased with aging. Both the DFAα(1) and DFAα(2) of SND patients were paradoxically reduced, which was not appropriate for their age. Only the percentage of consecutive RR intervals with absolute differences >50ms (pNN(50)), low-frequency power, and DFAα(2) made a significant contribution to prediction of SND on logistic regression analysis. Among them, DFAα(2) was the most significant variable for prediction of SND (odds ratio, 0.927; 95% confidence interval: 0.888-0.969, P=0.001). DFAα(2) remained as a significant variable for prediction of SND, when compared with overall control patients, combining the 2 control groups. CONCLUSIONS: Inappropriate reduction of DFAα(2) is a robust measure and could be an adjunctive tool for improvement of diagnostic performance in detection of SND.
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Shin et al. (2011) studied this question.
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