Key result
Heart rate variability features successfully classified coronary artery disease patients into left ventricular ejection fraction groups with a goodness of variance fit of more than 0.70 during late-night to early-morning and evening periods.
Why the study?
Echocardiography provides only a once-off LVEF measurement and does not capture heart function during day and night cardiac cycles.
Do heart rate variability (HRV) features accurately classify left ventricular ejection fraction (LVEF) levels in patients with coronary artery disease?
Cross-Sectional (n=92)
Do heart rate variability (HRV) features accurately classify left ventricular ejection fraction (LVEF) levels in patients with coronary artery disease?
Answers are grounded in this paper and cite their sources.
Heart rate variability features derived from 24-hour Holter ECGs can accurately classify LVEF levels in CAD patients, particularly during late-night and evening hours, offering a potential continuous monitoring alternative to echocardiography.
Abstract Analysis of heart failure is important in clinical practice to ensure coronary artery disease (CAD) patients will be provided with appropriate timely treatment. The current gold standard, echocardiography, although reliable, provides a once-off left ventricular ejection fraction (LVEF) measurement but does not provide information about heart function the during day / night cardiac cycles. The aim of this study was to investigate the ability of heart rate variability (HRV) features in classifying CAD patients into multiple LVEF groups through a new index, HRV ejection fraction (HRVEF) and its agreement to established norms. A total of 92 heart failure patients were included in this study. HRVEF groups optimized on hourly basis through Jenks natural breaks algorithm exhibited a consistent pattern with a goodness of variance fit (GVF) of more than 0.70 accuracy during the late-night to early-morning (01:00-08:00) and evening (17:00-23:00) time periods. At these hours, several HRV features were found significant in differentiating between HRVEF groups using statistical analysis of variance (ANOVA) test. These features include the successive differences between normal heartbeats (RMSSD), low and high frequency (LF, HF) power, standard deviation of normal heartbeats (SD2), short-term scaling exponent (alpha1), and percentage of normal heartbeats in alternation segments (PAS). This study paves the way towards new LVEF guidelines based on the association between LVEF and HRV features for a better demonstration of the circadian cardiac function at different LVEF levels in CAD patients.
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Alkhodari et al. (2021) conducted a cross-sectional in Coronary Artery Disease (n=92). Heart rate variability (HRV) features vs. Echocardiography-based LVEF guidelines (ACCF/AHA and ASE/EACVI) was evaluated on Classification into LVEF groups (HRVEF) using Jenks natural breaks algorithm (GVF > 0.70). Heart rate variability features successfully classified coronary artery disease patients into left ventricular ejection fraction groups with a goodness of variance fit of more than 0.70 during late-night to early-morning and evening periods.
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