Key result
Heart failure patients exhibited a significantly higher long-term scaling exponent α2 compared to healthy controls (0.932 vs 0.830, p<0.01), and the ratio of short- to long-term scaling exponents identified four distinct patient clusters.
Why the study?
Current methods of heartbeat interval analysis cannot differentiate HF patients, which could help guide treatment direction.
Does the ratio of short- and long-term scaling exponents (α1/α2) from resting ECG differentiate subgroups of heart failure patients?
Cross-Sectional (n=81)
No
Does the ratio of short- and long-term scaling exponents (α1/α2) from resting ECG differentiate subgroups of heart failure patients?
The ratio of short- and long-term scaling exponents (α1/α2) from a 20-minute resting ECG can identify distinct physiological subgroups of heart failure patients, potentially aiding in targeted therapy.
May support HF phenotyping via scaling; hypothesis-generating and requires prospective validation before any clinical use.
Heart failure (HF) is one of the most frequent heart diseases. It is usually characterized with structural and functional cardiac abnormalities followed by dysfunction of autonomic cardiac control. Current methods of heartbeat interval analysis are not capable to differentiate HF patients and some new differentiation of HF patients could be useful in the determination of the direction of their treatment. In this study we examined potential of the ratio of the short-term and long-term scaling exponents (α1 and α2) to separate HF patients with similar level of reduced cardiac autonomic nervous system control and with no significant difference in age, left ventricular ejection fraction (LVEF) and NYHA class. Thirty-five healthy control subjects and forty-six HF patients underwent 20 minutes of continuous supine resting ECG recording. The interbeat interval time series were analysed using standardized power spectrum analysis, detrended fluctuation analysis method and standard Poincaré plot (PP) analysis with measures of asymmetry of the PP. Compared with healthy control group, in HF patients linear measures of autonomic cardiac control were statistically significantly reduced (p Cdown, p <0.01), and long-term scaling exponent α2 was significantly higher. Cluster analysis of the ratio of short- and long-term scaling exponents showed capability of this parameter to separate 4 clusters of HF patients. Clusters were determined by interplay of presence of short-term and long-term correlations in interbeat intervals. Complementary measure, commonly accepted ratio of the PP descriptors, SD2/SD1, showed tendency toward statistical significance to separate HF patients in obtained clusters. Also, heart rate asymmetry was preserved only in two clusters. Finally, a multiple regression analysis showed that the ratio α1/α2 could be used as an integrated measure of cardiac dynamic with complex physiological background which, besides spectral components as measures of autonomic cardiac control, also involves breathing frequency and mechanical cardiac parameter, left ventricular ejection fraction.
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Platiša et al. (2019) conducted a cross-sectional in Heart failure (n=81). Heart failure vs. Healthy controls was evaluated on Long-term scaling exponent (α2) (p=<0.01). Heart failure patients exhibited a significantly higher long-term scaling exponent α2 compared to healthy controls (0.932 vs 0.830, p<0.01), and the ratio of short- to long-term scaling exponents identified four distinct patient clusters.
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