Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction. TRACE Investigators. TRAndolapril Cardiac Evaluation

A number of new methods have been recently developed to quantify complex heart rate (HR) dynamics based on nonlinear and fractal analysis, but their value in risk stratification has not been evaluated. This study was designed to determine whether selected new dynamic analysis methods of HR variability predict mortality in patients with depressed left ventricular (LV) function after acute myocardial infarction (AMI). Traditional time- and frequency-domain HR variability indexes along with short-term fractal-like correlation properties of RR intervals (exponent alpha) and power-law scaling (exponent beta) were studied in 159 patients with depressed LV function (ejection fraction <35%) after an AMI. By the end of 4-year follow-up, 72 patients (45%) had died and 87 (55%) were still alive. Short-term scaling exponent alpha (1.07 +/- 0.26 vs 0.90 +/- 0.26, p <0.001) and power-law slope beta (-1.35 +/- 0.23 vs -1.44 +/- 0.25, p <0.05) differed between survivors and those who died, but none of the traditional HR variability measures differed between these groups. Among all analyzed variables, reduced scaling exponent alpha (<0.85) was the best univariable predictor of mortality (relative risk 3.17, 95% confidence interval 1.96 to 5.15, p <0.0001), with positive and negative predictive accuracies of 65% and 86%, respectively. In the multivariable Cox proportional hazards analysis, mortality was independently predicted by the reduced exponent alpha (p <0.001) after adjustment for several clinical variables and LV function. A short-term fractal-like scaling exponent was the most powerful HR variability index in predicting mortality in patients with depressed LV function. Reduction in fractal correlation properties implies more random short-term HR dynamics in patients with increased risk of death after AMI.     

Fractal analysis and time- and frequency-domain measures of heart rate variability as predictors of mortality in patients with heart failure

Time-domain measures of heart rate (HR) variability provide prognostic information among patients with congestive heart failure (CHF). The prognostic power of spectral and fractal analytic methods of HR variability has not been studied in the patients with chronic CHF. The aim of this study was to assess whether traditional and fractal analytic methods of HR variability predict mortality among a population of patients with CHF. The standard deviation of RR intervals, HR variability index, frequency-domain indexes, and the short-term fractal scaling exponent of RR intervals were studied from 24-hour Holter recordings in 499 patients with CHF and left ventricular ejection fraction < or =35%. During a mean follow-up of 665 +/- 374 days, 210 deaths (42%) occurred in this population. Conventional and fractal HR variability indexes predicted mortality by univariate analysis. For example, a short-term fractal scaling exponent <0.90 had a risk ratio (RR) of 1.9 (95% confidence interval [CI] 1.4 to 2.5) and the SD of all RR intervals <80 ms had an RR of 1.7 (95% CI 1.2 to 2.1). After adjusting for age, functional class, medication, and left ventricular ejection fraction in the multivariate proportional-hazards analysis, the reduced short-term fractal exponent remained the independent predictor of mortality, RR 1.4 (95% CI 1.0 to 1.9; p <0.05). All HR variability indexes were more significant univariate predictors of mortality in functional class II than in class III or IV. Among patients with moderate heart failure, HR variability measurements provide prognostic information, but all HR variability indexes fail to provide independent prognostic information in patients with the most severe functional impairment.     

Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction

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 alpha(1) and alpha(2)) and power-law scaling of the power spectra (exponent beta), were assessed from 24-hour Holter recordings in 446 survivors of acute myocardial infarction with a depressed left ventricular function (ejection fraction 相似文献   

Fractal analysis of heart rate variability and mortality after an acute myocardial infarction

The recently developed fractal analysis of heart rate (HR) variability has been suggested to provide prognostic information about patients with heart failure. This prospective multicenter study was designed to assess the prognostic significance of fractal and traditional HR variability parameters in a large, consecutive series of survivors of an acute myocardial infarction (AMI). A consecutive series of 697 patients were recruited to participate 2 to 7 days after an AMI in 3 Nordic university hospitals. The conventional time-domain and spectral parameters and the newer fractal scaling indexes of HR variability were analyzed from 24-hour RR interval recordings. During the mean follow-up of 18.4 +/- 6.5 months, 49 patients (7.0%) died. Of all the risk variables, a reduced short-term fractal scaling exponent (alpha(1) <0.65), measured by detrended fluctuation analysis, was the most powerful predictor of mortality (univariate relative risk 5.05, 95% confidence intervals [CI] 2.87 to 8.89, p <0.001). A low scaling exponent alpha(1) predicted death in the patients with and without depressed left ventricular function (p <0.001 and p <0.01, respectively). Several other HR variability parameters also predicted mortality in univariate analyses, but in a multivariate analysis after adjustments for clinical variables and left ventricular ejection fraction, alpha(1) was the most significant independent HR variability index that predicted subsequent mortality (relative risk 3.90, 95% CI 2.03 to 7.49, p <0.001). Short-term fractal scaling analysis of HR variability is a powerful predictor of mortality among patients surviving an acute myocardial infarction.     

Temporal changes and prognostic significance of measures of heart rate dynamics after acute myocardial infarction in the beta-blocking era

Previous studies have shown that indexes describing heart rate (HR) dynamics may predict subsequent deaths of patients after an acute myocardial infarction (AMI). Because beta-blocking (BB) drugs affect both mortality and HR dynamics, the prognostic power of measurements of HR dynamics may have changed in the current era of BB therapy. This study assessed the temporal changes and prognostic significance of time-domain, spectral, and fractal indexes of HR variability along with HR turbulence after an AMI among patients with optimized BB medication. SD of NN intervals, spectral indexes, the short-term fractal scaling exponent (alpha(1)), power-law slope (beta), and turbulence onset and slope were measured in 600 patients at 5 to 7 days after AMI and in 416 patients at 12 months after AMI. In the multivariate analysis, after adjusting for clinical variables, only reduced fractal HR indexes, alpha1 and beta (p <0.01 for both), turbulence onset, and slope (p <0.05 for both), measured at the convalescent phase after AMI, predicted subsequent cardiac death. All time-domain and spectral HR variability indexes and turbulence onset increased significantly during the 12-month period after AMI (p <0.001 for all), whereas the fractal indexes and turbulence slope remained unchanged. Late after AMI, reduced beta (p <0.05) and turbulence slope (p <0.01) were the only independent predictors of cardiac mortality. Traditional time-domain and spectral measurements of HR variability and turbulence onset improved significantly after AMI, whereas the fractal HR dynamics and turbulence slope remained stable. Fractal HR variability and HR turbulence retain their prognostic power in the BB era, when measured either at the convalescent or late phase after AMI.     

Prediction of sudden cardiac death by fractal analysis of heart rate variability in elderly subjects

OBJECTIVES: The aim of this study was to test the hypothesis that abnormal scaling characteristics of heart rate (HR) predict sudden cardiac death in a random population of elderly subjects. BACKGROUND: An abnormality in the short-term fractal scaling properties of HR has been observed to be related to a risk of life-threatening arrhythmias among patients with advanced heart diseases. The predictive power of altered short-term scaling properties of HR in general populations is unknown. METHODS: A random sample of 325 subjects, age 65 years or older, who had a comprehensive risk profiling from clinical evaluation, laboratory tests and 24-h Holter recordings were followed up for 10 years. Heart rate dynamics, including conventional and fractal scaling measures of HR variability, were analyzed. RESULTS: At 10 years of follow-up, 164 subjects had died. Seventy-one subjects had died of a cardiac cause, and 29 deaths were defined as sudden cardiac deaths. By univariate analysis, a reduced short-term fractal scaling exponent predicted the occurrence of cardiac death (relative risk [RR] 2.5, 95% confidence interval [CI], 1.9 to 3.2, p < 0.001) and provided even stronger prediction of sudden cardiac death (RR 4.1, 95% CI, 2.5 to 6.6, p < 0.001). After adjusting for other predictive variables in a multivariate analysis, reduced exponent value remained as an independent predictor of sudden cardiac death (RR 4.3, 95% CI, 2.0 to 9.2, p < 0.001). CONCLUSIONS: Altered short-term fractal scaling properties of HR indicate an increased risk for cardiac mortality, particularly sudden cardiac death, in the random population of elderly subjects.     

Heart rate variability patterns before ventricular tachycardia onset in patients with an implantable cardioverter defibrillator. Participating Investigators of ICD-HRV Italian Study Group

Time- and frequency-domain analysis of heart rate variability (HRV) has been proven effective in describing alteration of autonomic control mechanisms and in identifying patients with increased cardiac and arrhythmic mortality. Patients with implantable cardioverter defibrillators offer the opportunity to evaluate HRV patterns before ventricular tachycardia (VT) and under control conditions. We therefore analyzed time- and frequency-domain parameters of short-term HRV and power-law behavior of RR interval time series at rest, at 15 to 30 minutes, and immediately before VT. In comparison to control conditions, lower values of mean cycle length duration and total power were observed before VT. Spectral analysis indicated that the low- to high-frequency ratio was significantly higher (5.5 +/- 0.6 vs 2.8 +/- 0.3) immediately before VT than during rest. Both findings were consistent with the shift of sympathovagal balance toward sympathetic predominance and reduced vagal tone. Before VT, a more negative value of the scaling exponent beta of the power-frequency relation (-1.57 +/- 0.04 vs -1.33 +/- 0.04) also confirmed the presence of an altered HRV pattern in comparison to controls. Thus, both abnormal autonomic modulation and dynamic patterns of HRV seem to characterize the minutes before arrhythmia onset in these patients.     

Fractal correlation properties of heart rate dynamics and adverse events in patients with implantable cardioverter-defibrillators.

The aim of this study was to determine the prognostic significance of nonlinear and standard heart rate (HR) variability parameters in predicting future adverse events (AEs) in patients with implantable cardioverter-defibrillators. In postinfarction studies, nonlinear measures of HR variability obtained from long-term electrocardiographic recordings have been suggested to be better predictors of adverse outcomes than conventional HR variability measures. Fifty-five high-risk patients with reduced left ventricular function and an implantable cardioverter-defibrillator had a 10-minute, high-resolution electrocardiographic recording after which they were followed for 25 months on average. Implantable cardioverter-defibrillator shock or death was determined as the end point. The SD of all normal-to-normal RR intervals, the square root of the mean squared differences of successive normal-to-normal RR intervals, and the proportion of interval differences of successive normal-to-normal RR intervals >50 ms, low-frequency and high-frequency powers of the power spectrum and their ratio were calculated as conventional measures of HR variability. The short-term scaling exponent (alpha(1)) and approximate entropy were determined as nonlinear measures of HR variability. AEs occurred in 23 patients (42%). Patients with AEs had significantly lower alpha(1) than event-free patients: 0.81 +/- 0.29 (mean +/- SD) versus 1.01 +/- 0.30 (p = 0.02). None of the other HR variability parameters differed significantly between patients with and without AEs. In the Cox proportional-hazards model including age, gender, ejection fraction, occurrence of ventricular tachyarrhythmia before defibrillator implantation, beta-blocker usage, and alpha(1), only alpha(1) was an independent predictor of AEs: hazard ratio 1.20 (95% confidence interval 1.03 to 1.39) for every 0.10 decrease in alpha(1) (p = 0.020). In conclusion, alpha(1) obtained from a 10-minute electrocardiographic recording yields important prognostic information about the risk of AEs in patients with implantable cardioverter-defibrillators.     

Measurement of heart rate variability by methods based on nonlinear dynamics

Heart rate (HR) variability has been conventionally analyzed with time and frequency domain methods, which measure the overall magnitude of R-R interval fluctuations around its mean value or the magnitude of fluctuations in some predetermined frequencies. Analysis of HR dynamics by methods based on chaos theory and nonlinear system theory has gained recent interest. This interest is based on observations suggesting that the mechanisms involved in cardiovascular regulation likely interact with each other in a nonlinear way. Furthermore, recent observational studies suggest that some indexes describing nonlinear HR dynamics, such as fractal scaling exponents, may provide more powerful prognostic information than the traditional HR variability indexes. In particular, short-term fractal scaling exponent measured by detrended fluctuation analysis method has been shown to predict fatal cardiovascular events in various populations. Approximate entropy, a nonlinear index of HR dynamics, which describes the complexity of R-R interval behavior, has provided information on the vulnerability to atrial fibrillation. There are many other nonlinear indexes, eg, Lyapunov exponent and correlation dimensions, which also give information on the characteristics of HR dynamics, but their clinical utility is not well established. Although concepts of chaos theory, fractal mathematics, and complexity measures of HR behavior in relation to cardiovascular physiology or various cardiovascular events are still far away from clinical medicine, they are a fruitful area for future research to expand our knowledge concerning the behavior of cardiovascular oscillations in normal healthy conditions as well as in disease states.     

Power-law behavior of beat-rate variability in monolayer cultures of neonatal rat ventricular myocytes

It is known that extracardiac factors (nervous, humoral, and hemodynamic) participate in the power-law behavior of heart-rate variability. To assess whether intrinsic properties of cardiac tissue might also be involved, beat-rate variability was studied in spontaneously beating cell cultures devoid of extracardiac influences. Extracellular electrograms were recorded from monolayer cultures of neonatal rat ventricular myocytes under stable incubating conditions for up to 9 hours. The beat-rate time series of these recordings were examined in terms of their Fourier spectra and their Hurst scaling exponents. A non-0 Hurst exponent was found in 21 of 22 preparations (0.29+/-0.09; range, 0.11 to 0.45), indicating the presence of fractal self-similarity in the beat-rate time series. The same preparations exhibited power-law behavior of the power spectra with a power-law exponent of -1.36+/-0.24 (range, -1.04 to -1.96) in the frequency range of 0.001 to 1 Hz. Furthermore, it was found that the power-law exponent was nonstationary over time. These results indicate that the power-law behavior of heart-rate variability is determined not only by extracardiac influences but also by components intrinsic to cardiac tissue. Furthermore, the presence of power-law behavior in monolayer cultures of cardiomyocytes suggests that beat-rate variability might be determined by the complex nonlinear dynamics of processes occurring at the level of the cellular network, eg, interactions among a large number of cell oscillators or metabolic regulatory systems.     

Fractal and Complexity Measures of Heart Rate Variability

Heart rate variability has been analyzed conventionally with time and frequency domain methods, which measure the overall magnitude of RR interval fluctuations around its mean value or the magnitude of fluctuations in some predetermined frequencies. Analysis of heart rate dynamics by methods based on chaos theory and nonlinear system theory has gained recent interest. This interest is based on observations suggesting that the mechanisms involved in cardiovascular regulation likely interact with each other in a nonlinear way. Furthermore, recent observational studies suggest that some indexes describing nonlinear heart rate dynamics, such as fractal scaling exponents, may provide more powerful prognostic information than the traditional heart rate variability indexes. In particular, the short-term fractal scaling exponent measured by the detrended fluctuation analysis method has predicted fatal cardiovascular events in various populations. Approximate entropy, a nonlinear index of heart rate dynamics, that describes the complexity of RR interval behavior, has provided information on the vulnerability to atrial fibrillation. Many other nonlinear indexes, e.g., Lyapunov exponent and correlation dimensions, also give information on the characteristics of heart rate dynamics, but their clinical utility is not well established. Although concepts of chaos theory, fractal mathematics, and complexity measures of heart rate behavior in relation to cardiovascular physiology or various cardiovascular events are still far away from clinical medicine, they are a fruitful area for future research to expand our knowledge concerning the behavior of cardiovascular oscillations in normal healthy conditions as well as in disease states.     

Fractal and complexity measures of heart rate variability

Heart rate variability has been analyzed conventionally with time and frequency domain methods, which measure the overall magnitude of RR interval fluctuations around its mean value or the magnitude of fluctuations in some predetermined frequencies. Analysis of heart rate dynamics by methods based on chaos theory and nonlinear system theory has gained recent interest. This interest is based on observations suggesting that the mechanisms involved in cardiovascular regulation likely interact with each other in a nonlinear way. Furthermore, recent observational studies suggest that some indexes describing nonlinear heart rate dynamics, such as fractal scaling exponents, may provide more powerful prognostic information than the traditional heart rate variability indexes. In particular, the short-term fractal scaling exponent measured by the detrended fluctuation analysis method has predicted fatal cardiovascular events in various populations. Approximate entropy, a nonlinear index of heart rate dynamics, that describes the complexity of RR interval behavior, has provided information on the vulnerability to atrial fibrillation. Many other nonlinear indexes, e.g., Lyapunov exponent and correlation dimensions, also give information on the characteristics of heart rate dynamics, but their clinical utility is not well established. Although concepts of chaos theory, fractal mathematics, and complexity measures of heart rate behavior in relation to cardiovascular physiology or various cardiovascular events are still far away from clinical medicine, they are a fruitful area for future research to expand our knowledge concerning the behavior of cardiovascular oscillations in normal healthy conditions as well as in disease states.     

Relation of heart rate dynamics to the occurrence of myocardial ischemia after coronary artery bypass grafting

Postoperative myocardial ischemia is a common finding after coronary artery bypass grafting (CABG) and is associated with an adverse short-term clinical outcome. The reasons and pathophysiologic background for the occurrence of ischemia after CABG are not well established. We tested the hypothesis that altered heart rate (HR) behavior precedes the onset of myocardial ischemic episodes in patients after CABG. Time-domain HR variability measurements, along with analysis of Poincaré plots and fractal scaling analysis were assessed in 40 CABG patients from 48-hour postoperative Holter recordings. Twenty patients experienced 195 ischemic episodes during the postoperative course. In the univariate analysis of HR variability measurements of the first postoperative day (POD), the increased ratio between the short-term (SD1) and long-term (SD2) HR variability analyzed from the Poincaré plot and the decreased short- and intermediate-term fractal scaling exponents alpha(1) and alpha(2) were significantly associated with ischemia during the study period (p <0.01, p <0.05, and p <0.05, respectively). In the multivariate model, the increased SD1/SD2 ratio of the first POD was the most powerful independent predictor of all possible confounding variables for the occurrence of postoperative ischemia (corresponding to a change of 0.15 U; odds ratio 2.2 and 95% confidence interval 1.2 to 5.7; p <0.01). Altered HR dynamics have been associated with myocardial ischemic episodes in patients after CABG, suggesting that the autonomic nervous system has an important role in the pathogenesis of myocardial ischemia in the postoperative phase of CABG.     

Reversal of deteriorated fractal behavior of heart rate variability by beta-blocker therapy in patients with advanced congestive heart failure

INTRODUCTION: The slope of the power spectrum in heart rate variability (HRV) reflects the fractal or scaling behavior in HR dynamics and recently was confirmed as an independent predictor of postmyocardial infarction survival. Whether or not the new measurement in HRV foresees the functional evolution in patients with advanced congestive heart failure treated by beta blockers is unclear. METHODS AND RESULTS: Sequential 24-hour Holter ECG recordings were obtained at baseline, and 1 and 3 months after addition of atenolol therapy for advanced congestive heart failure in 10 patients. The slope and intercept of the regression line of power-law behavior, the short- and intermediate-term of detrended fluctuated analysis (DFA), the approximate entropy (ApEn), and the standard frequency spectra of the 24-hour HRV were compared sequentially as well as with those in 12 age-matched normal controls. The results showed that the slope (-1.70 +/- 0.45 vs -1.22 +/- 0.21; P < 0.05) and the intercept (5.11 +/- 0.46 vs 5.62 +/- 0.24; P < 0.05) of the regression line of power-law behavior and the short-term DFA (for 4 to 11 beats) (0.78 +/- 0.18 vs 1.13 +/- 0.21; P < 0.05) increased after 3 months of atenolol treatment. However, the change in intermediate-term DFA (>11 beats) and ApEn was not apparent (1.24 +/- 0.21 vs 1.22 +/- 0.15 and 1.34 +/- 0.14 vs 1.36 +/- 0.11; both P > 0.05). The evolution of the slope or intercept of the regression line of the HRV power spectrum did not correlate with the echocardiographic or clinical cardiac function, or with the frequency spectral components of the HRV (P > 0.05). CONCLUSION: Additional beta-blocker therapy upregulated the fractal behavior control of the HRV in patients with advanced congestive heart failure. The improvement was independent of subjective and objective global cardiac performance.     

Differences in Heart Rate Dynamics Before the Spontaneous Onset of Long and Short Episodes of Paroxysmal Atrial Fibrillation

Background: Altered heart rate (HR) dynamics precede the spontaneous onset of atrial fibrillation (AF), but the factors related to the perpetuation and duration of paroxysmal AF episodes are not well established. This study was designed to test the hypothesis that HR dynamics preceding the onset of (AF) may influence the duration of AF. Methods: Traditional time and frequency domain HR variability indices, along with a short‐term fractal scaling exponent (α₁) and approximate entropy (ApEn), were analyzed in 20‐minute intervals before 92 episodes of spontaneous paroxysmal AF in 22 patients without structural heart disease. AF episodes were divided into two groups according to the duration of the arrhythmia episodes. Results: The high‐frequency (HF) spectral component in normalized units (nu) of heart rate variability was higher and low‐frequency (LF) component lower before long (> 200 s, n = 41) compared to short (< 200 s, n = 51) AF episodes (HF nu; 40.1 ± 14.8 vs 31.5 ± 16.4, P < 0.0001 and LF nu; 59.9 ± 14.8 vs 68.5 ± 16.4, P < 0.0001). Short‐term scaling exponent values also were lower before long compared to short AF episodes (e.g., α₁; 1.12 ± 0.21 vs 1.24 ± 0.23, P < 0.0001). Women had a larger number of long AF episodes than men, but the duration of AF was not related to any other clinical or demographic features or antiarrhythmic medication. Conclusion: Increased HF oscillations and decreased short‐term correlation properties of R‐R intervals, reflecting altered sympathovagal balance before the onset of AF, predispose to perpetuation of spontaneous arrhythmia episodes in patients with vulnerability to paroxysmal AF and without structural heart disease. A.N.E. 2001;6(2):134–142     

Heart Rate Variability in Risk Stratification of Cardiac Patients

Heart rate (HR) variability has been extensively studied in cardiac patients, especially in patients surviving an acute myocardial infarction (AMI) and also in patients with congestive heart failure (CHF) or left ventricular (LV) dysfunction. The majority of studies have shown that patients with reduced or abnormal HR variability have an increased risk of mortality within a few years after an AMI or after a diagnosis of CHF/LV dysfunction. Various measures of HR dynamics, such as time-domain, spectral, and non-linear measures of HR variability have been used in risk stratification. The prognostic power of various measures, except of those reflecting rapid R–R interval oscillations, has been almost identical, albeit some non-linear HR variability measures, such as short-term fractal scaling exponent have provided somewhat better prognostic information than the others. Abnormal HR variability predicts both sudden and non-sudden cardiac death. Because of remodeling of the arrhythmia substrate after AMI, early measurement of HR variability to identify those at high risk should likely be repeated later in order to assess the risk of fatal arrhythmia events. Future randomized trials using HR variability/turbulence as one of the pre-defined inclusion criteria will show whether routine measurement of HR variability/turbulence will become a routine clinical tool for risk stratification of cardiac patients.     

Ventricular response to atrial fibrillation: role of atrioventricular conduction pathways

Irregularity of the ventricular rhythm is a hallmark of patients with atrial fibrillation, yet the genesis of the irregularity is not yet fully understood. The role of the atrioventricular (AV) node in determining the irregularity of the ventricular response to atrial fibrillation was investigated by comparing the frequency distributions of the atrial (AA) and the ventricular (RR) intervals. Atrial electrograms and surface electrocardiographic leads were recorded during sustained atrial fibrillation in 12 patients with conduction over the AV node. The scaling factor (mean RR interval/mean AA interval) quantified the ability of the conduction pathway to scale the atrial input to a slower ventricular response and ranged from 2.55 to 5.92 (mean +/- SD 3.77 +/- 0.92). The coefficient of variation (SD/mean) measured the relative variability of the AA and RR interval distributions. The atrial and ventricular coefficients of variation were not significantly different (0.20 +/- 0.04 versus 0.21 +/- 0.03, p greater than 0.27). Similar recordings were analyzed in six patients with conduction over a accessory AV pathway. The scaling factor ranged from 1.54 to 2.46 (2.02 +/- 0.39) and, as was the case for patients with conduction over the AV node, the atrial and ventricular coefficients of variation did not significantly differ (0.24 +/- 0.08 versus 0.27 +/- 0.10, p greater than 0.6). For both groups of patients, ventricular variability and the maximal RR intervals were predicted by the product of the scaling factor and either atrial variability or maximal AA intervals, respectively.(ABSTRACT TRUNCATED AT 250 WORDS)     

Repolarization abnormality in idiopathic ventricular fibrillation: assessment using 24-hour QT-RR and QaT-RR relationships

INTRODUCTION: We evaluated the characteristics of QT-RR and QaT (apex of T wave)-RR relationships in patients with idiopathic ventricular fibrillation (IVF) compared with control subjects. We hypothesized that IVF patients have unique repolarization dynamics related to a reduced fast Na current and a prominent transient outward current. METHODS AND RESULTS: The study group consisted of 9 men (age 47 +/- 10 years) with IVF (6 with Brugada type and 3 with non-Brugada type) who had experienced nocturnal episodes of VF. The control group consisted of 28 healthy age-matched men (age 44 +/- 12 years). The relationships between QT and RR intervals and between QaT and RR intervals were analyzed from 24-hour Holter ECG data using an automatic measurement system. Both QT and QaT at RR intervals of 0.6, 1.0, and 1.2 seconds were determined from QT-RR and QaT-RR linear regression lines. Both QT-RR and QaT-RR slopes were lower in the IVF group than in the control group (QT-RR: 0.092 +/- 0.023 vs 0.137 +/- 0.031, P < 0.001; QaT-RR: 0.109 +/- 0.025 vs 0.153 +/- 0.028, P < 0.001). QT at an RR interval of 0.6 second did not differ between two groups, but QT at RR intervals of either 1.0 or 1.2 seconds was significantly shorter in the IVF group than in the control group (RR 1.0 s: 0.384 +/- 0.018 vs 0.399 +/- 0.017, P < 0.05; RR 1.2 s: 0.402 +/- 0.019 vs 0.426 +/- 0.020, P < 0.01). QaT at RR intervals of either 1.0 or 1.2 seconds also was shorter in the IVF group (RR 1.0 s: 0.289 +/- 0.022 vs 0.312 +/- 0.021, P < 0.01; RR 1.2 s: 0.311 +/- 0.024 vs 0.343 +/- 0.024, P < 0.01). In four patients, oral administration of disopyramide (300 mg/day) was effective in suppressing VF episodes and increased slopes of QT-RR and QaT-RR relationships. CONCLUSION: IVF patients had lower slopes of QT-RR and QaT-RR regression lines and impaired prolongation of QT and QaT at longer RR intervals compared with control subjects. These unique repolarization dynamics may be related to the frequent occurrence of VF episodes at night.     

Effects of Carvedilol on Heart Rate Dynamics in Patients with Congestive Heart Failure

Background: Patients with congestive heart failure (CHF) have alterations in the traditional and nonlinear indices of heart rate (HR) dynamics, which have been associated with an increased risk of mortality. This study was designed to test the effects of carvedilol, a nonselective beta‐blocker with alpha‐1 blocking properties, on HR dynamics in patients with CHF. Methods: We studied 15 patients with CHF secondary to ischemic or idiopathic cardiomyopathy who met the following inclusion criteria: NYHA functional class II‐III, optimal conventional medical therapy, normal sinus rhythm, left ventricular ejection fraction (LVEF) of < 40%, and resting systolic blood pressure greater than 100 mmHg. The 6‐minute corridor walk test, estimation of LVEF, and 24‐hour Holter recording were performed at baseline and after 12 weeks of therapy with carvedilol. Traditional time and frequency domain measures and short‐term fractal scaling exponent of HR dynamics were analyzed. Results: After 12 weeks of therapy with carvedilol, the mean LVEF improved significantly (from 0.27 ± 0.08 to 0.38 ± 0.08, P < 0.001). The average HR decreased significantly (from 86 ± 11 to 70 ± 8 beats/min, P < 0.001). The mean distance traveled in the 6‐minute walk test increased significantly (from 177 ± 44 to 273 ± 55 m, P < 0.01). The frequency‐domain indices (HF and LF), the time domain indices (rMSSD and PNN5), and the short‐term fractal scaling exponent increased significantly. The scaling exponent increased particularly among the patients with the lowest initial values (< 1.0), and the change in the fractal scaling exponent correlated with the change in ejection fraction (r = 0.63, P < 0.01). Conclusion: Carvedilol improves time and frequency domain indices of HR variability and corrects the altered scaling properties of HR dynamics in patients with CHF. It also improves LVEF and functional capacity. These specific changes in HR behavior caused by carvedilol treatment may reflect the normalization of impaired cardiovascular neural regulation of patients with CHF. A.N.E. 2002;7(2):133–138     

Nonlinear Analysis of Heart Rate Variability: Fractal and Complexity Measures of Heart Rate Behavior

Many new methods of analyzing heart rate (HR) variability have been developed to describe the features in HR behavior that cannot be detected by traditional time‐ and frequency‐domain methods. Some of the new methods, such as analysis of fractal correlation properties and complexity of HR dynamics, have provided clinically useful information in various patient populations. Importantly, some fractal analysis methods are better risk predictors of mortality than traditional HR variability measures, and analysis of complexity of HR dynamics has been shown to predict the spontaneous onset of atrial fibrillation. New analysis methods based on nonlinear dynamics are a promising tool for better understanding of normal and abnormal HR behavior. More work will be needed to establish the clinical applicability of traditional and new analysis methods of HR variability.