Abstract
In industrialized countries with aging populations, heart failure affects 0.3–2% of the general population. The investigation of 24 h-ECG recordings revealed the potential of nonlinear indices of heart rate variability (HRV) for enhanced risk stratification in patients with ischemic heart failure (IHF). However, long-term analyses are time-consuming, expensive, and delay the initial diagnosis. The objective of this study was to investigate whether 30 min short-term HRV analysis is sufficient for comparable risk stratification in IHF in comparison to 24 h-HRV analysis. From 256 IHF patients [221 at low risk (IHFLR) and 35 at high risk (IHFHR)] (a) 24 h beat-to-beat time series (b) the first 30 min segment (c) the 30 min most stationary day segment and (d) the 30 min most stationary night segment were investigated. We calculated linear (time and frequency domain) and nonlinear HRV analysis indices. Optimal parameter sets for risk stratification in IHF were determined for 24 h and for each 30 min segment by applying discriminant analysis on significant clinical and non-clinical indices. Long- and short-term HRV indices from frequency domain and particularly from nonlinear dynamics revealed high univariate significances (p < 0.01) discriminating between IHFLR and IHFHR. For multivariate risk stratification, optimal mixed parameter sets consisting of 5 indices (clinical and nonlinear) achieved 80.4% AUC (area under the curve of receiver operating characteristics) from 24 h HRV analysis, 84.3% AUC from first 30 min, 82.2 % AUC from daytime 30 min and 81.7% AUC from nighttime 30 min. The optimal parameter set obtained from the first 30 min showed nearly the same classification power when compared to the optimal 24 h-parameter set. As results from stationary daytime and nighttime, 30 min segments indicate that short-term analyses of 30 min may provide at least a comparable risk stratification power in IHF in comparison to a 24 h analysis period.
Highlights
Heart failure (HF) is a major escalating public health problem worldwide, in industrialized countries with aging populations, being associated with both high morbidity and mortality (McMurray and Stewart, 2000; Felker, 2012)
UNIVARIATE ANALYSIS According to the univariate discrimination between ischemic heart failure (IHF) patients at low risk and high risk respectively, both clinical and nonclinical indices could prove their ability to differentiate between these two patient groups
The IHFHR group was characterized by a decreased LVEF and an increased LVSD when compared to the IHF patients [221 at low risk (IHFLR) group
Summary
Heart failure (HF) is a major escalating public health problem worldwide, in industrialized countries with aging populations, being associated with both high morbidity and mortality (McMurray and Stewart, 2000; Felker, 2012). HF is a final manifestation of most cardiac diseases and is clinically recognized by a multitude/complexity of signs and symptoms caused by complex circulatory and neurohormonal responses to structural and/or functional cardiac dysfunction (Guindo et al, 1997; Rohini et al, 2012). As one of the most common cardiovascular diseases, clinically identified HF affects 0.3–2% of the general population. Even today there are still no generally accepted indications identifying HF patients with an increased risk of sudden cardiac death (SCD). The identification of HF patients at risk still remains an important key issue in clinical decision making (Goldberger et al, 2008; Cygankiewicz et al, 2009; Saha and Goldberger, 2012). Several risk stratification studies investigated the usefulness of primarily univariate analysis of www.frontiersin.org
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