Heart Rate Variability Density Analysis (Dyx) and Prediction of Long-Term Mortality after Acute Myocardial Infarction.

AIMS: The density HRV parameter Dyx is a new heart rate variability (HRV) measure based on multipole analysis of the Poincaré plot obtained from RR interval time series, deriving information from both the time and frequency domain. Preliminary results have suggested that the parameter may provide new predictive information on mortality in survivors of acute myocardial infarction (MI). This study compares the prognostic significance of Dyx to that of traditional linear and nonlinear measures of HRV. METHODS AND RESULTS: In the Nordic ICD pilot study, patients with an acute MI were screened with 2D echocardiography and 24-hour Holter recordings. The study was designed to assess the power of several HRV measures to predict mortality. Dyx was tested in a subset of 206 consecutive Danish patients with analysable Holter recordings. After a median follow-up of 8.5 years 70 patients had died. Of all traditional and multipole HRV parameters, reduced Dyx was the most powerful predictor of all-cause mortality (HR 2.4; CI 1.5 to 3.8; P < 0.001). After adjustment for known risk markers, such as age, diabetes, ejection fraction, previous MI and hypertension, Dyx remained an independent predictor of mortality (P = 0.02). Reduced Dyx also predicted cardiovascular death (P < 0.01) and sudden cardiovascular death (P = 0.05). In Kaplan-Meier analysis, Dyx significantly predicted mortality in patients both with and without impaired left ventricular systolic function (P < 0.0001). CONCLUSION: The new nonlinear HRV measure Dyx is a promising independent predictor of mortality in a long-term follow-up study of patients surviving a MI, irrespectively of left ventricular systolic function.

Respiratory sinus arrhythmia in Chagas disease.

We applied the respiratory sinus arrhythmia (RSA) quantification algorithm to 24-hour ECG recordings of Chagas disease (ChD) patients with (G1, n=148) and without left ventricular dysfunction (LVD) (G2, n=33), and in control subjects (G0, n=28). Both ChD groups displayed a reduced RSA index; G1=299 (144-812); G2=335 (162-667), p=0.011, which was correlated with vagal indexes of heart rate variability analysis. RSA index is a marker of vagal modulation in ChD patients.

Determinants and prognostic value of cardiovascular autonomic function in coronary artery disease patients with and without type 2 diabetes.

OBJECTIVE Cardiovascular autonomic dysfunction is a common finding among patients with coronary artery disease (CAD) and type 2 diabetes (T2D). The reasons and prognostic value of autonomic dysfunction in CAD patients with T2D are not well known. RESEARCH DESIGN AND METHODS We examined the association between heart rate recovery (HRR), 24-h heart rate (HR) variability (SD of normal R-R interval [SDNN]), and HR turbulence (HRT), and echocardiographic parameters, metabolic, inflammatory, and coronary risk variables, exercise capacity, and the presence of T2D among 1,060 patients with CAD (mean age 67 ± 8 years; 69% males; 50% patients with T2D). Second, we investigated how autonomic function predicts a composite end point of cardiovascular death, acute coronary event, stroke, and hospitalization for heart failure during a 2-year follow-up. RESULTS In multiple linear regression model, exercise capacity was a strong predictor of HRR (R = 0.34, P < 0.001), SDNN (R = 0.33, P < 0.001), and HRT (R = 0.13, P = 0.001). In univariate analyses, a composite end point was predicted by reduced HRR (hazard ratio 1.7 [95% CI 1.1-2.6]; P = 0.020), reduced SDNN (2.0 [95% CI 1.2-3.1]; P = 0.005), and blunted HRT (2.1 [1.3-3.4]; P = 0.003) only in patients with T2D. After multivariate adjustment, none of the autonomic markers predicted the end point, but high-sensitivity C-reactive protein (hs-CRP) remained an independent predictor. CONCLUSIONS Cardiovascular autonomic function in CAD patients is associated with several variables, including exercise capacity. Autonomic dysfunction predicts short-term cardiovascular events among CAD patients with T2D, but it is not as strong an independent predictor as hs-CRP.

Role of editing of R-R intervals in the analysis of heart rate variability.

This paper reviews the methods used for editing of the R-R interval time series and how this editing can influence the results of heart rate (HR) variability analyses. Measurement of HR variability from short and long-term electrocardiographic (ECG) recordings is a non-invasive method for evaluating cardiac autonomic regulation. HR variability provides information about the sympathetic-parasympathetic autonomic balance. One important clinical application is the measurement of HR variability in patients suffering from acute myocardial infarction. However, HR variability signals extracted from R-R interval time series from ambulatory ECG recordings often contain different amounts of artifact. These false beats can be either of physiological or technical origin. For instance, technical artifact may result from poorly fastened electrodes or be due to motion of the subject. Ectopic beats and atrial fibrillation are examples of physiological artifact. Since ectopic and other false beats are common in the R-R interval time series, they complicate the reliable analysis of HR variability sometimes making it impossible. In conjunction with the increased usage of HR variability analyses, several studies have confirmed the need for different approaches for handling false beats present in the R-R interval time series. The editing process for the R-R interval time series has become an integral part of these analyses. However, the published literature does not contain detailed reviews of editing methods and their impact on HR variability analyses. Several different editing and HR variability signal pre-processing methods have been introduced and tested for the artifact correction. There are several approaches available, i.e., use of methods involving deletion, interpolation or filtering systems. However, these editing methods can have different effects on HR variability measures. The effects of editing are dependent on the study setting, editing method, parameters used to assess HR variability, type of study population, and the length of R-R interval time series. The purpose of this paper is to summarize these pre-processing methods for HR variability signal, focusing especially on the editing of the R-R interval time series.

Effects of postectopic heart rate turbulence on measures of heart rate variability in patients after an acute myocardial infarction.

BACKGROUND: Heart rate (HR) turbulence lasting up to 15 beats after ventricular premature beats (VPBs) may have profound effects on HR variability measures. Aim of this study was to examine the effects of HR turbulence on HR variability measures. METHODS: We developed an algorithm, which deletes 15 consecutive RR intervals after VPBs and examined the effects of the HR turbulence removal on the HR variability measures in patients after an acute myocardial infarction (AMI). Two hundred and sixty seven patients with left ventricular ejection fraction (LVEF) ≤ 0.40 and occurrence of VPBs were included in the study. Differences (%) between original HR data and HR turbulence edited data were compared. RESULTS: HR turbulence editing had variable effects on different HR variability indexes. Ultra low (ULF) and very low frequency (VLF) spectral components were mostly affected by the HR turbulence removal. Both ULF and VLF decreased significantly both at baseline Holter recordings (ULF: P = 0.006, VLF: P = 0.031) and at 6 weeks from AMI (ULF: P < 0.001, VLF: P = 0.001). The number of VPBs had a marked influence on results, e.g., when the number of VPBs exceeded the highest decile (≈50 VPBs/hour), the ULF and VLF spectral component were >30% lower after removal of turbulence. In addition, the prediction of arrhythmic events by ULF component improved after turbulence removal (AUC: 0.69 ->0.74). CONCLUSIONS: HR turbulence affects HR variability measures, especially the ULF and VFL spectral components. Editing of the HR turbulence should be considered when HR variability is measured from Holter recordings.

Respiratory sinus arrhythmia as a predictor of sudden cardiac death after myocardial infarction.

BACKGROUND: Measurement of high-frequency (HF) spectral power of heart rate (HR) variability has not been able to identify the patients at risk of sudden cardiac death (SCD) despite the experimental evidence of protective role of vagal activity for fatal arrhythmias. AIM: We developed a novel respiratory sinus arrhythmia (RSA) analysis method and tested its ability to predict SCD after an acute myocardial infarction. METHOD: The RSA analysis method was developed in 13 subjects from simultaneous recordings of respiration and R-R intervals. An adaptive threshold was computed based on the zero-phase forward and reverse digital filtering in the analysis of RSA. With this method, only respiration-related R-R interval fluctuations are included. The prognostic power of RSA, analyzed from 24-hour electrocardiographic recordings, was subsequently assessed in a large postinfarction population including 1631 patients with mean follow-up of 40 +/- 17 months. RESULTS: Depressed RSA was a strong predictor of SCD (hazard ratio 7.4; 95% CI 3.6-15.1; P 0.0001) but only a weak predictor of non-SCD. The RSA index remained an independent predictor of SCD after adjustments for ejection fraction and other clinical risk variables (RR 4.7; 95% CI 2.28-9.85). CONCLUSIONS: Reduced respiratory-related HR dynamics, detected by RSA index, are a specific marker of an increased risk of SCD among postinfarction patients.

Effects and significance of premature beats on fractal correlation properties of R-R interval dynamics.

BACKGROUND: Premature beats (PBs) have been considered as artifacts producing a bias in the traditional analysis of heart rate (HR) variability. We assessed the effects and significance of PBs on fractal scaling exponents in healthy subjects and patients with a recent myocardial infarction (AMI). METHODS: Artificial PBs were first generated into a time series of pure sinus beats in 20 healthy subjects and 20 post-AMI patients. Thereafter, a case-control approach was used to compare the prognostic significance of edited and nonedited fractal scaling exponents in a random elderly population and in a post-AMI population. Detrended fluctuation analysis (DFA) was used to measure the short-term (alpha1) and long-term (alpha2) fractal scaling exponents. RESULTS: Artificial PBs caused a more pronounced reduction of alpha1 value among the post-AMI patients than the healthy subjects, for example, if > 0.25% of the beats were premature a > 25% decrease in the alpha1 was observed in post-AMI patients, but 4% of the premature beats were needed to cause a 25% reduction in alpha1 in healthy subjects. Both edited (1.01 +/- 0.31 vs 1.19 +/- 0.27, P < 0.01) and unedited alpha1 (0.71 +/- 0.33 vs 0.89 +/- 0.36, P < 0.05) differed between the patients who died (n = 42) and those who survived (n = 42) after an AMI. In the general population, only unedited alpha1 differed significantly between survivors and those who died (0.96 +/- 0.19 vs 0.83 +/- 0.27, P < 0.05). CONCLUSIONS: Unedited premature beats result in an increase in the randomness of short-term R-R interval dynamics, particularly in post-AMI patients. Premature beats must not necessarily be edited when fractal analysis is used for risk stratification.