HRV Scaling Exponent Identifies Postinfarction Patients Who Might Benefit From Prophylactic Treatment With Amiodarone

Fractal analysis of heart rate variability as a predictor of mortality: A systematic review and meta-analysis

Previous studies have suggested benefits of applying fractal analysis to intervals between R waves in electrocardiography as an additional prognostic marker. The aim of this study was to investigate whether fractal analysis can provide an independent predictor of cardiac mortality or all-cause mortality. Prognostic cohort studies reporting fractal heart rate variability results from 24-h Holter monitor recordings were selected for comparison. Populations were subdivided into four groups-post-myocardial infarction, left ventricular dysfunction, other cardiac, and non-cardiac patients-and analysed using ANOVA, Forest plots (using pooled mean difference), and Funnel plots. The most significant mean differences were recorded in short-term fractal self-similarity (α₁) (-0.17, 95% CI [-0.21, -0.13], p < 0.00001) and the traditional measure called standard deviation of NN intervals (SDNN) (-13.31, 95% CI [-18.89, -7.73], p < 0.00001) between the deceased and survivor groups. Fractal measures of long-term fractal self-similarity (α₂), 1/f scaling (β), and traditional heart rate variability measures of high frequency to low frequency ratio show promise. This review indicated that fractal measure α₁ and traditional measure SDNN could be potential predictors of mortality, but require further assessment to determine appropriate thresholds for clinical significance and additional targeted prognostic studies to properly define their applicability as prognostic markers. Therefore, clinicians should interpret fractal and traditional measures with caution since such measures have yet to be fully described as biomarkers for clinical application.

Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society

Following the publication of the Task Force document on heart rate variability (HRV) in 1996, a number of articles have been published to describe new HRV methodologies and their application in different physiological and clinical studies. This document presents a critical review of the new methods. A particular attention has been paid to methodologies that have not been reported in the 1996 standardization document but have been more recently tested in sufficiently sized populations. The following methods were considered: Long-range correlation and fractal analysis; Short-term complexity; Entropy and regularity; and Nonlinear dynamical systems and chaotic behaviour. For each of these methods, technical aspects, clinical achievements, and suggestions for clinical application were reviewed. While the novel approaches have contributed in the technical understanding of the signal character of HRV, their success in developing new clinical tools, such as those for the identification of high-risk patients, has been rather limited. Available results obtained in selected populations of patients by specialized laboratories are nevertheless of interest but new prospective studies are needed. The investigation of new parameters, descriptive of the complex regulation mechanisms of heart rate, has to be encouraged because not all information in the HRV signal is captured by traditional methods. The new technologies thus could provide after proper validation, additional physiological, and clinical meaning. Multidisciplinary dialogue and specialized courses in the combination of clinical cardiology and complex signal processing methods seem warranted for further advances in studies of cardiac oscillations and in the understanding normal and abnormal cardiac control processes.

Prophylactic amiodarone and lidocaine improve survival in an ovine model of large size myocardial infarction

BACKGROUND

Large animal models serve as a critical link in the translation of basic science to clinical practice. However, large animal models of myocardial infarction (MI), especially large size MI, have been associated with high mortality because of arrhythmia. The prophylactic effect of amiodarone and lidocaine were retrospectively reviewed in our ovine MI model.

MATERIALS AND METHODS

A total of 114 Dorset hybrid sheep with 25%-30% MI were included in the present study. The sheep were prophylactically treated with amiodarone plus lidocaine before ligation of the four to six coronary artery branches supplying the apex of the heart (arrhythmia prevention [AP] group, n = 45) and with epinephrine (shock prevention [SP] group, n = 49), respectively. The sheep without prophylactic treatment (no prevention [NP] group, n = 20) were used as the control group. The incidence of arrhythmia requiring treatment, mortality due to arrhythmia, hemodynamics, and arterial blood gas values during surgery were analyzed in these three groups.

RESULTS

No significant difference was found in infarct size among the three groups. The incidence of arrhythmia requiring treatment was significantly decreased in the AP group compared with that in the NP or SP groups (4.4% for AP versus 35% for NP and 45% for SP groups; P < 0.05). The mortality due to lethal arrhythmia was 2.2% in the AP group, significantly lower than that in the NP group (15%) or SP group (18.4%). Other than the heart rate, no significant differences were found in the hemodynamic data between the AP and NP groups. Metabolic acidosis was not observed in any group, as indicated by the pH and lactate values.

CONCLUSIONS

Prophylactic amiodarone plus lidocaine decreased the mortality due to lethal arrhythmia after acute MI in our sheep model without significant negative effects on the hemodynamics. However, epinephrine improved the hemodynamics but also increased the mortality due to lethal arrhythmia. Thus, prophylactic amiodarone plus lidocaine is recommended to improve the stability in a large MI animal model.

The rise of hemodialysis machines: new technologies in minimizing cardiovascular complications

Hemodialysis (HD) is a life-saving treatment for more than 1,700,000 patients with chronic kidney disease (CKD) stage V. Every year the HD population becomes increasingly older (average age: 75 years) and more ill due to the associated comorbidities such as cardiovascular disease (heart failure, coronary heart disease and peripheral vascular disease), diabetes, hypertension and peripheral vascular disease. Most of the complications associated with HD involve the cardiovascular system. HD machines have been greatly improved over the last 30 years. We have moved from HD machines simply allowing extracorporeal circulation to high technological medical devices capable of very precisely controlling ultrafiltration, dialysis dose, the patient's core temperature, circulating plasma volume, plasma sodium and producing unlimited volumes of ultrapure dialysate. In this article, we will focus on some of the fundamental achievements in HD machine technology, with particular reference to monitoring tools and bioengineering approaches for biosignal analysis. We propose that along these lines of further development, HD machines in the future will enable a better online identification of patients at higher cardiovascular risk, thus allowing clinicians to select more appropriate treatment modalities and parameters.

Long-term invariant parameters obtained from 24-h Holter recordings: a comparison between different analysis techniques

Over the last two decades, a large number of different methods had been used to study the fractal-like behavior of the heart rate variability (HRV). In this paper some of the most used techniques were reviewed. In particular, the focus is set on those methods which characterize the long memory behavior of time series (in particular, periodogram, detrended fluctuation analysis, rescale range analysis, scaled window variance, Higuchi dimension, wavelet-transform modulus maxima, and generalized structure functions). The performances of the different techniques were tested on simulated self-similar noises (fBm and fGn) for values of alpha, the slope of the spectral density for very small frequency, ranging from -1 to 3 with a 0.05 step. The check was performed using the scaling relationships between the various indices. DFA and periodogram showed the smallest mean square error from the expected values in the range of interest for HRV. Building on the results obtained from these tests, the effective ability of the different methods in discriminating different populations of patients from RR series derived from Holter recordings, was assessed. To this extent, the Noltisalis database was used. It consists of a set of 30, 24-h Holter recordings collected from healthy subjects, patients suffering from congestive heart failure, and heart transplanted patients. All the methods, with the exception at most of rescale range analysis, were almost equivalent in distinguish between the three groups of patients. Finally, the scaling relationships, valid for fBm and fGn, when empirically used on HRV series, also approximately held.