Linear and nonlinear heart rate variability risk stratification in heart failure patients

Abstract

Chronic heart failure (CHF) is a major and growing public health concern (~23 million people worldwide) with five-year survival rates of 25% in men and 38% in women. Objective of this study was to investigate whether linear and nonlinear heart rate variability (HRV) indices enhance risk prediction in patients with CHF. To discriminate between low risk (stable condition, N = 459) and high risk (cardiac death, N = 50) CHF patient groups, nonlinear indices from compression entropy (CE), detrended fluctuation analysis (DFA), symbolic dynamics (SD) and standard linear HRV analysis were calculated from 24 h Holter ECG recordings. Indices from nonlinear dynamics (CE, DFA, SD: p < 0.001) contribute together with clinical parameters NYHA and LVEF to an enhanced risk stratification in CHF patients.