Timo H. Mäkikallio

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 </=35%). During a mean+/-SD follow-up period of 685+/-360 days, 114 patients died (25.6%), with 75 deaths classified as arrhythmic (17.0%) and 28 as nonarrhythmic (6.3%) cardiac deaths. Several traditional and fractal measures of R-R interval variability were significant univariate predictors of all-cause mortality. Reduced short-term scaling exponent alpha(1) was the most powerful R-R interval variability measure as a predictor of all-cause mortality (alpha(1) <0.75, relative risk 3.0, 95% confidence interval 2.5 to 4.2, P<0.001). It remained an independent predictor of death (P<0.001) after adjustment for other postinfarction risk markers, such as age, ejection fraction, NYHA class, and medication. Reduced alpha(1) predicted both arrhythmic death (P<0.001) and nonarrhythmic cardiac death (P<0.001). CONCLUSIONS Analysis of the fractal characteristics of short-term R-R interval dynamics yields more powerful prognostic information than the traditional measures of HR variability among patients with depressed left ventricular function after an acute myocardial infarction.

Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study

BACKGROUND Decreased vagal activity after myocardial infarction results in reduced heart-rate variability and increased risk of death. To distinguish between vagal and sympathetic factors that affect heart-rate variability, we used a signal-processing algorithm to separately characterise deceleration and acceleration of heart rate. We postulated that diminished deceleration-related modulation of heart rate is an important prognostic marker. Our prospective hypotheses were that deceleration capacity is a better predictor of risk than left-ventricular ejection fraction (LVEF) and standard deviation of normal-to-normal intervals (SDNN). METHODS We quantified heart rate deceleration capacity by assessing 24-h Holter recordings from a post-infarction cohort in Munich (n=1455). We blindly validated the prognostic power of deceleration capacity in post-infarction populations in London, UK (n=656), and Oulu, Finland (n=600). We tested our hypotheses by assessment of the area under the receiver-operator characteristics curve (AUC). FINDINGS During a median follow-up of 24 months, 70 people died in the Munich cohort and 66 in the London cohort. The Oulu cohort was followed-up for 38 months and 77 people died. In the London cohort, mean AUC of deceleration capacity was 0.80 (SD 0.03) compared with 0.67 (0.04) for LVEF and 0.69 (0.04) for SDNN. In the Oulu cohort, mean AUC of deceleration capacity was 0.74 (0.03) compared with 0.60 (0.04) for LVEF and 0.64 (0.03) for SDNN (p<0.0001 for all comparisons). Stratification by dichotomised deceleration capacity was especially powerful in patients with preserved LVEF (p<0.0001 in all cohorts). INTERPRETATION Impaired heart rate deceleration capacity is a powerful predictor of mortality after myocardial infarction and is more accurate than LVEF and the conventional measures of heart-rate variability.

Vagal modulation of heart rate during exercise: effects of age and physical fitness

This study was designed to assess the effects of age and physical fitness on vagal modulation of heart rate (HR) during exercise by analyzing the instantaneous R-R interval variability from Poincaré plots (SD1) at rest and at different phases of a bicycle exercise test in a population of healthy males. SD1 normalized for the average R-R interval (SD1n), a measure of vagal activity, was compared at rest and during exercise among subjects of ages 24-34 (young, n = 25), 35-46 (middle-aged, n = 30), and 47-64 yr (old, n = 25) matched for peak O2 consumption (V˙o 2 peak) and among subjects withV˙o 2 peak of 28-37 (poor, n = 25), 38-45 (average, n = 36), and 46-60 ml ⋅ kg-1 ⋅ min-1(good, n = 25) matched for age. SD1n was higher at rest in the young subjects than in the middle-aged or old subjects (39 ± 14, 27 ± 16, and 21 ± 8, respectively; P < 0.001), but the age-related differences in SD1n were smaller during exercise [e.g., 11 ± 5, 9 ± 5, and 8 ± 4 at the level of 100 W; P = not significant (NS)]. The age-matched subjects with good, average, and poor V˙o 2 peakshowed no difference in SD1n at rest (32 ± 17, 28 ± 13, and 26 ± 11, respectively; P = NS), but SD1n differed significantly among the groups from a low to a moderate exercise intensity level (e.g., 13 ± 6, 10 ± 5, and 6 ± 3 for good, average, and poor fitness groups, respectively; P < 0.001, 100 W). These data show that poor physical fitness is associated with an impairment of cardiac vagal function during exercise, whereas aging itself results in more evident impairment of vagal function at rest.

Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction

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.

Prediction of sudden cardiac death after myocardial infarction in the beta-blocking era☆

OBJECTIVES This study assessed the predictive power of arrhythmia risk markers after an acute myocardial infarction (AMI). BACKGROUND Several risk variables have been suggested to predict the occurrence of sudden cardiac death (SCD), but the utility of these variables has not been well established among patients using medical therapy according to contemporary guidelines. METHODS A consecutive series of 700 patients with AMI was studied. The end points were total mortality, SCD, and nonsudden cardiac death (non-SCD). Nonsustained ventricular tachycardia (nsVT), ejection fraction (EF), heart rate variability, baroreflex sensitivity, signal-averaged electrocardiogram (SAECG), QT dispersion, and QRS duration were analyzed (n = 675). Beta-blocking therapy was used by 97% of the patients at discharge and by 95% at one and two years after AMI. RESULTS During a mean (+/-SD) follow-up of 43 +/- 15 months, 37 non-SCDs (5.5%) and 22 SCDs (3.2%) occurred. All arrhythmia risk variables differed between the survivors and those with non-SCD (e.g., the standard deviation of N-N intervals was 98 +/- 32 vs. 74 +/- 21 ms [p < 0.001] and the QRS duration was 103 +/- 22 vs.89 +/- 16 ms [p < 0.001]). Sudden cardiac death was weakly predicted only by reduced EF (<0.40; p < 0.05), nsVT (p < 0.05), and abnormal SAECG (p < 0.05), but not by autonomic markers or standard ECG variables. The positive predictive accuracy of EF, nsVT, and abnormal SAECG as predictors of SCD was relatively low (8%, 12%, and 13%, respectively). CONCLUSIONS The common arrhythmia risk variables, particularly the autonomic and standard ECG markers, have limited predictive power in identifying patients at risk of SCD after AMI in the beta-blocking era.

Altered Complexity and Correlation Properties of R-R Interval Dynamics Before the Spontaneous Onset of Paroxysmal Atrial Fibrillation

BACKGROUND Trigger mechanisms for the onset of paroxysmal atrial fibrillation (AF) in patients without structural heart disease are not well established. New analysis methods of heart rate (HR) variability based on nonlinear system theory may reveal features and abnormalities in R-R interval behavior that are not detectable by traditional analysis methods. The purpose of this study was to reveal possible alterations in the dynamics of R-R intervals before the spontaneous onset of paroxysmal AF. METHODS AND RESULTS Traditional time and frequency domain HR variability indices, along with the short-term scaling exponent alpha(1) and approximate entropy (ApEn), were analyzed in 20-minute intervals before 92 episodes of spontaneous, paroxysmal AF in 22 patients without structural heart disease. Traditional HR variability measures showed no significant changes before the onset of AF. A progressive decrease occurred both in ApEn (1.09+/-0.26 120 to 100 minutes before AF; 0.88+/-0.24 20 to 0 minutes before AF; P<0.001) and in alpha(1) (1.01+/-0.28 120 to 100 minutes before AF, 0.89+/-0.28 20 to 0 minutes before AF; P<0.05) before the AF episodes. Both ApEn (0. 89+/-0.27 versus 1.02+/-0.30; P<0.05) and alpha(1) (0.91+/-0.28 versus 1.27+/-0.21; P<0.001) were also lower before the onset of AF compared with values obtained from matched healthy control subjects. CONCLUSIONS A decrease in the complexity of R-R intervals and altered fractal properties in short-term R-R interval dynamics precede the spontaneous onset of AF in patients with no structural heart disease. Further studies are needed to determine the physiological correlates of these new, nonlinear HR variability measures.

Effects of intracoronary injection of mononuclear bone marrow cells on left ventricular function, arrhythmia risk profile, and restenosis after thrombolytic therapy of acute myocardial infarction

AIMS To assess the efficacy and safety of bone marrow cell (BMC) therapy after thrombolytic therapy of an acute ST-elevation myocardial infarction (STEMI). METHODS AND RESULTS Patients with STEMI treated with thrombolysis followed by percutaneous coronary intervention (PCI) 2-6 days after STEMI were randomly assigned to receive intracoronary BMCs (n = 40) or placebo medium (n = 40), collected and prepared 3-6 h prior PCI and injected into the infarct artery immediately after stenting. Efficacy was assessed by the measurement of global left ventricular ejection fraction (LVEF) by left ventricular angiography and 2-D echocardiography, and safety by measuring arrhythmia risk variables and restenosis of the stented vessel by intravascular ultrasound. At 6 months, BMC group had a greater absolute increase of global LVEF than placebo group, measured either by angiography (mean +/- SD increase 7.1 +/- 12.3 vs. 1.2 +/- 11.5%, P = 0.05) or by 2-D echocardiography (mean +/- SD increase 4.0 +/- 11.2 vs. -1.4 +/- 10.2%, P = 0.03). No differences were observed between the groups in the adverse clinical events, arrhythmia risk variables, or the minimal lumen diameter of the stented coronary lesion. CONCLUSION Intracoronary BMC therapy is associated with an improvement of global LVEF and neutral effects on arrhythmia risk profile and restenosis of the stented coronary lesions in patients after thrombolytic therapy of STEMI.

Measurement of heart rate variability: a clinical tool or a research toy?

The objectives of this review are to discuss the diversity of mechanisms that may explain the association between heart rate (HR) variability and mortality, to appraise the clinical applicability of traditional and new measures of HR variability and to propose future directions in this field of research. There is a large body of data demonstrating that abnormal HR variability measured over a 24-h period provides information on the risk of subsequent death in subjects with and without structural heart disease. However, the mechanisms responsible for this association are not completely established. Therefore, no specific therapy is currently available to improve the prognosis for patients with abnormal HR variability. Reduced HR variability has been most commonly associated with a risk of arrhythmic death, but recent data suggest that abnormal variability also predicts vascular causes of death, progression of coronary atherosclerosis and death due to heart failure. A consensus is also lacking on the best HR variability measure for clinical purposes. Time and frequency domain measures of HR variability have been most commonly used, but recent studies show that new analysis methods based on nonlinear dynamics may be more powerful in terms of risk stratification. Before the measurement of HR variability can be applied to clinical practice and used to direct therapy, more precise insight into the pathophysiological link between HR variability and mortality are needed. Further studies should also address the issue of which of the HR variability indexes, including the new nonlinear measures, is best for clinical purposes in various patient populations.

Dynamic analysis of heart rate may predict subsequent ventricular tachycardia after myocardial infarction.

Dynamics analysis of RR interval behavior and traditional measures of heart rate variability were compared between postinfarction patients with and without vulnerability to ventricular tachyarrhythmias in a case-control study. Short-term fractal correlation of heart rate dynamics was better than traditional measures of heart rate variability in differentiating patients with and without life-threatening arrhythmias.

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.