Fractal Correlation Properties of R-R Interval Dynamics and Mortality in Patients With Depressed Left Ventricular Function After an Acute Myocardial Infarction

TLDR

Fractal analysis of R‑R interval variability may provide clinically useful information for patients with heart failure. The study compared the prognostic power of new fractal and traditional R‑R interval variability measures for predicting death after acute myocardial infarction. Time‑ and frequency‑domain heart rate variability metrics, short‑ and long‑term correlation exponents (α₁, α₂), and power‑law scaling exponent (β) were derived from 24‑hour Holter recordings of 446 survivors with left ventricular ejection fraction ≤35%. Reduced short‑term scaling exponent α₁ was the strongest independent predictor of all‑cause, arrhythmic, and nonarrhythmic cardiac death, outperforming traditional HRV measures.

Abstract

Background —Preliminary data suggest that the analysis of R-R interval variability by fractal analysis methods may provide clinically useful information on patients with heart failure. The purpose of this study was to compare the prognostic power of new fractal and traditional measures of R-R interval variability as predictors of death after acute myocardial infarction. Methods and Results —Time and frequency domain heart rate (HR) variability measures, along with short- and long-term correlation (fractal) properties of R-R intervals (exponents α 1 and α 2 ) and power-law scaling of the power spectra (exponent β), were assessed from 24-hour Holter recordings in 446 survivors of acute myocardial infarction with a depressed left ventricular function (ejection fraction ≤35%). During a mean±SD follow-up period of 685±360 days, 114 patients died (25.6%), with 75 deaths classified as arrhythmic (17.0%) and 28 as nonarrhythmic (6.3%) cardiac deaths. Several traditional and fractal measures of R-R interval variability were significant univariate predictors of all-cause mortality. Reduced short-term scaling exponent α 1 was the most powerful R-R interval variability measure as a predictor of all-cause mortality (α 1 <0.75, relative risk 3.0, 95% confidence interval 2.5 to 4.2, P <0.001). It remained an independent predictor of death ( P <0.001) after adjustment for other postinfarction risk markers, such as age, ejection fraction, NYHA class, and medication. Reduced α 1 predicted both arrhythmic death ( P <0.001) and nonarrhythmic cardiac death ( P <0.001). Conclusions —Analysis of the fractal characteristics of short-term R-R interval dynamics yields more powerful prognostic information than the traditional measures of HR variability among patients with depressed left ventricular function after an acute myocardial infarction.