Linear and non-linear 24 h heart rate variability in chronic heart failure

Visibility Graph Analysis of Heartbeat Time Series: Comparison of Young vs. Old, Healthy vs. Diseased, Rest vs. Exercise, and Sedentary vs. Active

Using the visibility graph algorithm (VGA), a complex network can be associated with a time series, such that the properties of the time series can be obtained by studying those of the network. Any value of the time series becomes a node of the network, and the number of other nodes that it is connected to can be quantified. The degree of connectivity of a node is positively correlated with its magnitude. The slope of the regression line is denoted by k-M, and, in this work, this parameter was calculated for the cardiac interbeat time series of different contrasting groups, namely: young vs. elderly; healthy subjects vs. patients with congestive heart failure (CHF); young subjects and adults at rest vs. exercising young subjects and adults; and, finally, sedentary young subjects and adults vs. active young subjects and adults. In addition, other network parameters, including the average degree and the average path length, of these time series networks were also analyzed. Significant differences were observed in the k-M parameter, average degree, and average path length for all analyzed groups. This methodology based on the analysis of the three mentioned parameters of complex networks has the advantage that such parameters are very easy to calculate, and it is useful to classify heartbeat time series of subjects with CHF vs. healthy subjects, and also for young vs. elderly subjects and sedentary vs. active subjects.

Characterization of autonomic states by complex sympathetic and parasympathetic dynamics

Assessment of heartbeat dynamics provides a promising framework for non-invasive monitoring of cardiovascular and autonomic states. Nevertheless, the non-specificity of such measurements among clinical populations and healthy conditions associated with different autonomic states severely limits their applicability and exploitation in naturalistic conditions. This limitation arises especially when pathological or postural change-related sympathetic hyperactivity is compared to autonomic changes across age and experimental conditions. In this frame, we investigate the intrinsic irregularity and complexity of cardiac sympathetic and vagal activity series in different populations, which are associated with different cardiac autonomic dynamics. Sample entropy, fuzzy entropy, and distribution entropy are calculated on the recently proposed sympathetic and parasympathetic activity indices (SAI and PAI) series, which are derived from publicly available heartbeat series of congestive heart failure patients, elderly and young subjects watching a movie in the supine position, and healthy subjects undergoing slow postural changes. Results show statistically significant differences between pathological/old subjects and young subjects in the resting state and during slow tilt, with interesting trends in SAI- and PAI-related entropy values. Moreover, while CHF patients and healthy subjects in upright position show the higher cardiac sympathetic activity, elderly and young subjects in resting state showed higher vagal activity. We conclude that quantification of intrinsic cardiac complexity from sympathetic and vagal dynamics may provide new physiology insights and improve on the non-specificity of heartbeat-derived biomarkers.

Ischemic Stroke Risk Assessment by Multiscale Entropy Analysis of Heart Rate Variability in Patients with Persistent Atrial Fibrillation

It has been recognized that heart rate variability (HRV), defined as the fluctuation of ventricular response intervals in atrial fibrillation (AFib) patients, is not completely random, and its nonlinear characteristics, such as multiscale entropy (MSE), contain clinically significant information. We investigated the relationship between ischemic stroke risk and HRV with a large number of stroke-naïve AFib patients (628 patients), focusing on those who had never developed an ischemic/hemorrhagic stroke before the heart rate measurement. The CHA2DS2−VASc score was calculated from the baseline clinical characteristics, while the HRV analysis was made from the recording of morning, afternoon, and evening. Subsequently, we performed Kaplan–Meier method and cumulative incidence function with mortality as a competing risk to estimate the survival time function. We found that patients with sample entropy (SE(s)) ≥ 0.68 at 210 s had a significantly higher risk of an ischemic stroke occurrence in the morning recording. Meanwhile, the afternoon recording showed that those with SE(s) ≥ 0.76 at 240 s and SE(s) ≥ 0.78 at 270 s had a significantly lower risk of ischemic stroke occurrence. Therefore, SE(s) at 210 s (morning) and 240 s ≤ s ≤ 270 s (afternoon) demonstrated a statistically significant predictive value for ischemic stroke in stroke-naïve AFib patients.

Detection of subjects with ischemic heart disease by using machine learning technique based on heart rate total variability parameters

OBJECTIVE

Ischemic heart disease (IHD), in its chronic stable form, is a subtle pathology due to its silent behavior before developing in unstable angina, myocardial infarction or sudden cardiac death. The clinical assessment is based on typical symptoms and finally confirmed, invasively, by coronary angiography. Recently, heart rate variability (HRV) analysis as well as some machine learning algorithms like Artificial Neural Networks (ANNs) were used to identify cardiovascular arrhythmias and, only in few cases, to classify IHD segments in a limited number of subjects. The goal of this study was the identification of the ANN structure and the HRV parameters producing the best performance to identify IHD patients in a non-invasive way, validating the results on a large sample of subjects. Moreover, we examined the influence of a clinical non-invasive parameter, the left ventricular ejection fraction (LVEF), on the classification performance.

APPROACH

To this aim, we extracted several linear and non-linear parameters from 24h RR signal, considering both normal and ectopic beats (Heart Rate Total Variability), of 251 normal and 245 IHD subjects, matched by age and gender. ANNs using several different combinations of these parameters together with age and gender were tested. For each ANN, we varied the number of hidden neurons from 2 to 7 and simulated 100 times changing randomly training and test dataset.

MAIN RESULTS

The HRTV parameters showed significant greater variability in IHD than in normal subjects. The ANN applied to meanRR, LF, LF/HF, Beta exponent, SD2 together with age and gender reached a maximum accuracy of 71.8% and, by adding as input LVEF, an accuracy of 79.8%.

SIGNIFICANCE

The study provides a deep insight into how a combination of some HRTV parameters and LVEF could be exploited to reliably detect the presence of subjects affected by IHD.

Optimizing Our Patients' Entropy Production as Therapy? Hypotheses Originating from the Physics of Physiology

Understanding how nature drives entropy production offers novel insights regarding patient care. Whilst energy is always preserved and energy gradients irreversibly dissipate (thus producing entropy), increasing evidence suggests that they do so in the most optimal means possible. For living complex non-equilibrium systems to create a healthy internal emergent order, they must continuously produce entropy over time. The Maximum Entropy Production Principle (MEPP) highlights nature's drive for non-equilibrium systems to augment their entropy production if possible. This physical drive is hypothesized to be responsible for the spontaneous formation of fractal structures in space (e.g., multi-scale self-similar tree-like vascular structures that optimize delivery to and clearance from an organ system) and time (e.g., complex heart and respiratory rate variability); both are ubiquitous and essential for physiology and health. Second, human entropy production, measured by heat production divided by temperature, is hypothesized to relate to both metabolism and consciousness, dissipating oxidative energy gradients and reducing information into meaning and memory, respectively. Third, both MEPP and natural selection are hypothesized to drive enhanced functioning and adaptability, selecting states with robust basilar entropy production, as well as the capacity to enhance entropy production in response to exercise, heat stress, and illness. Finally, a targeted focus on optimizing our patients' entropy production has the potential to improve health and clinical outcomes. With the implications of developing a novel understanding of health, illness, and treatment strategies, further exploration of this uncharted ground will offer value.

Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

Entropy-Based Measures of Hypnopompic Heart Rate Variability Contribute to the Automatic Prediction of Cardiovascular Events

Surges in sympathetic activity should be a major contributor to the frequent occurrence of cardiovascular events towards the end of nocturnal sleep. We aimed to investigate whether the analysis of hypnopompic heart rate variability (HRV) could assist in the prediction of cardiovascular disease (CVD). 2217 baseline CVD-free subjects were identified and divided into CVD group and non-CVD group, according to the presence of CVD during a follow-up visit. HRV measures derived from time domain analysis, frequency domain analysis and nonlinear analysis were employed to characterize cardiac functioning. Machine learning models for both long-term and short-term CVD prediction were then constructed, based on hypnopompic HRV metrics and other typical CVD risk factors. CVD was associated with significant alterations in hypnopompic HRV. An accuracy of 81.4% was achieved in short-term prediction of CVD, demonstrating a 10.7% increase compared with long-term prediction. There was a decline of more than 6% in the predictive performance of short-term CVD outcomes without HRV metrics. The complexity of hypnopompic HRV, measured by entropy-based indices, contributed considerably to the prediction and achieved greater importance in the proposed models than conventional HRV measures. Our findings suggest that Hypnopompic HRV assists the prediction of CVD outcomes, especially the occurrence of CVD event within two years.

Hypothetical Control of Heart Rate Variability

In the last three decades, the analysis of heart rate variability by nonlinear methods demonstrated the complexity of cardiovascular regulation. Additionally to the observations of periodic heart rate regulation by the autonomic nervous system, the long-term statistics of the heart rate has been determined to reminisce a tempered Lévy process. A number of heuristic arguments have previously been made to support a tempering conjecture, using exponentially truncated waiting times for the time intervals between heart beats. Herein we use the fractional probability calculus to frame our arguments and to parameterize the control process that tempers the Lévy process through a collective-induced potential. We also determine that the hypothesis of a self-induced nonlinear potential control resulting in such a tempered Lévy process is consistent with the hypothesis of disease being the loss of physiologic complexity made over 25 years ago.

Entropy Measures as Descriptors to Identify Apneas in Rheoencephalographic Signals

Rheoencephalography (REG) is a simple and inexpensive technique that intends to monitor cerebral blood flow (CBF), but its ability to reflect CBF changes has not been extensively proved. Based on the hypothesis that alterations in CBF during apnea should be reflected in REG signals under the form of increased complexity, several entropy metrics were assessed for REG analysis during apnea and resting periods in 16 healthy subjects: approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), corrected conditional entropy (CCE) and Shannon entropy (SE). To compute these entropy metrics, a set of parameters must be defined a priori, such as, for example, the embedding dimension m, and the tolerance threshold r. A thorough analysis of the effects of parameter selection in the entropy metrics was performed, looking for the values optimizing differences between apnea and baseline signals. All entropy metrics, except SE, provided higher values for apnea periods (p-values < 0.025). FuzzyEn outperformed all other metrics, providing the lowest p-value (p = 0.0001), allowing to conclude that REG signals during apnea have higher complexity than in resting periods. Those findings suggest that REG signals reflect CBF changes provoked by apneas, even though further studies are needed to confirm this hypothesis.

The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method

Congestive heart failure (CHF) is a cardiovascular disease associated with autonomic dysfunction, where sympathovagal imbalance was reported in many studies using heart rate variability (HRV). To learn more about the dynamic interaction in the autonomic nervous system (ANS), we explored the directed interaction between the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS) with the help of transfer entropy (TE). This article included 24-h RR interval signals of 54 healthy subjects (31 males and 23 females, 61.38 ± 11.63 years old) and 44 CHF subjects (8 males and 2 females, 19 subjects' gender were unknown, 55.51 ± 11.44 years old, 4 in class I, 8 in class II and 32 in class III~IV, according to the New York Heart Association Function Classification), obtained from the PhysioNet database and then segmented into 5-min non-overlapping epochs using cubic spline interpolation. For each segment in the normal group and CHF group, frequency-domain features included low-frequency (LF) power, high-frequency (HF) power and LF/HF ratio were extracted as classical estimators of autonomic activity. In the nonlinear domain, TE between LF and HF were calculated to quantify the information exchanging between SNS and PNS. Compared with the normal group, an extreme decrease in LF/HF ratio (p = 0.000) and extreme increases in both TE(LF→HF) (p = 0.000) and TE(HF→LF) (p = 0.000) in the CHF group were observed. Moreover, both in normal and CHF groups, TE(LF→HF) was a lot greater than TE(HF→LF) (p = 0.000), revealing that TE was able to distinguish the difference in the amount of directed information transfer among ANS. Extracted features were further applied in discriminating CHF using IBM SPSS Statistics discriminant analysis. The combination of the LF/HF ratio, TE(LF→HF) and TE(HF→LF) reached the highest screening accuracy (83.7%). Our results suggested that TE could serve as a complement to traditional index LF/HF in CHF screening.

Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations

Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse fields. However, the practical application of entropy methods is challenging, due to the variety of entropy measures and estimators and the complexity of real-world time series, including nonstationarities and long-range correlations (LRC). We conduct a systematic study on the performance, bias, and limitations of three basic measures (entropy, conditional entropy, information storage) and three traditionally used estimators (linear, kernel, nearest neighbor). We investigate the dependence of entropy measures on estimator- and process-specific parameters, and we show the effects of three types of nonstationarities due to artifacts (trends, spikes, local variance change) in simulations of stochastic autoregressive processes. We also analyze the impact of LRC on the theoretical and estimated values of entropy measures. Finally, we apply entropy methods on heart rate variability data from subjects in different physiological states and clinical conditions. We find that entropy measures can only differentiate changes of specific types in cardiac dynamics and that appropriate preprocessing is vital for correct estimation and interpretation. Demonstrating the limitations of entropy methods and shedding light on how to mitigate bias and provide correct interpretations of results, this work can serve as a comprehensive reference for the application of entropy methods and the evaluation of existing studies.

Correlation between autonomous function and left ventricular performance after acute myocardial infarction

Reduced ejection fraction (EF), possibly induced/mediated by autonomic abnormal activation, is one of the most powerful predictors of adverse outcome after acute myocardial infarction (MI). A deep understanding of the correlation between the autonomous functionality and the left ventricular performance in these patients is therefore of paramount importance. The autonomous function is reflected in the cardiac activity and, specifically, in the heart rate variability (HRV) signal. Given the cardiac activity nonlinearity, growing interest is being manifested towards nonlinear methods of analysis, which might provide more significant information than the traditional linear approaches. The aim of the present study was to investigate if non-linear HRV metrics change between MI patients with preserved EF (pEF) and MI patients with reduced EF (rEF). Data were acquired in the context of the cardioRisk project. Ten MI patients with rEF and six MI patients with pEF, admitted to Intensive Cardiac Care after a first acute MI episode, were studied. The ECG was acquired during a Holter recording and the tachogram was extracted. Sample entropy (SE) and Lempel-Ziv Complexity (LZC 1 and LZC 2) metrics were computed on five hour long tachogram portions. A significant correlation was found between LZC indices and EF in the whole population; SE, LZC 1 and LZC 2 were significantly higher in patients with pEF. Our results indicate that lower complexity characterizes the HRV of MI patients with rEF. Complexity reduction might be due to a simplification of regulatory mechanisms, which might explain why MI patients with rEF are at higher risk for subsequent non-fatal and fatal events.

Are Complexity Metrics Reliable in Assessing HRV Control in Obese Patients During Sleep?

Obesity is associated with cardiovascular mortality. Linear methods, including time domain and frequency domain analysis, are normally applied on the heart rate variability (HRV) signal to investigate autonomic cardiovascular control, whose imbalance might promote cardiovascular disease in these patients. However, given the cardiac activity non-linearities, non-linear methods might provide better insight. HRV complexity was hereby analyzed during wakefulness and different sleep stages in healthy and obese subjects. Given the short duration of each sleep stage, complexity measures, normally extracted from long-period signals, needed be calculated on short-term signals. Sample entropy, Lempel-Ziv complexity and detrended fluctuation analysis were evaluated and results showed no significant differences among the values calculated over ten-minute signals and longer durations, confirming the reliability of such analysis when performed on short-term signals. Complexity parameters were extracted from ten-minute signal portions selected during wakefulness and different sleep stages on HRV signals obtained from eighteen obese patients and twenty controls. The obese group presented significantly reduced complexity during light and deep sleep, suggesting a deficiency in the control mechanisms integration during these sleep stages. To our knowledge, this study reports for the first time on how the HRV complexity changes in obesity during wakefulness and sleep. Further investigation is needed to quantify altered HRV impact on cardiovascular mortality in obesity.

Autonomic dysfunction and heart rate variability in depression

Depression occurs in people of all ages across all world regions; it is the second leading cause of disability and its global burden increased by 37.5% between 1990 and 2010. Autonomic changes are often found in altered mood states and appear to be a central biological substrate linking depression to a number of physical dysfunctions. Alterations of autonomic nervous system functioning that promotes vagal withdrawal are reflected in reductions of heart rate variability (HRV) indexes. Reduced HRV characterizes emotional dysregulation, decreased psychological flexibility and defective social engagement, which in turn are linked to prefrontal cortex hypoactivity. Altogether, these pieces of evidence support the idea that HRV might represent a useful endophenotype for psychological/physical comorbidities, and its routine application should be advised to assess the efficacy of prevention/intervention therapies in a number of psychosomatic and psychiatric dysfunctions. Further research, also making use of appropriate animal models, could provide a significant support to this point of view and possibly help to identify appropriate antidepressant therapies that do not interefere with physical health.

Cardiovascular autonomic function analysis using approximate entropy from 24-h heart rate variability and its frequency components in patients with type 2 diabetes

Aims/Introduction

The principal aim of the present study was to investigate the cardiovascular autonomic system status of diabetes patients using approximate entropy (ApEn) extracted from 24-h heart rate variability (HRV) and its frequency components.

Materials and Methods

A total of 29 healthy controls and 63 type 2 diabetes patients were included. Participants’ 24-h HRV signals were recorded, and decomposed and reconstructed into four frequency components: high, low, very low and ultra low. The total 24-h HRV and its four components were divided into 24 1-h segments. ApEn values were extracted and statistically analyzed. Four traditional HRV indices, namely standard deviation of the RR intervals, root mean square of successive differences, coefficient of variance of RR intervals and ratio of low to high power of HRV, were also calculated.

Results

The low-frequency component contained the most abundant non-linear information, so was potentially most suitable for studying the cardiovascular system status with non-linear methods. ApEn values extracted from low- and high-frequency components of healthy controls were higher than those of diabetes patients. Except for root mean square of successive differences, standard deviation of the RR intervals, low to high power of HRV and coefficient of variance of RR intervals of healthy controls were all higher than those of diabetes patients.

Conclusions

The results showed that ApEn contained information on disorders of autonomic system function of diabetes patients as traditional HRV indices in time and frequency domains. ApEn and three traditional indices showed accordance to some degree. Non-linear information in subcomponents of HRV was shown, which is potentially more effective for distinguishing healthy individuals and diabetes patients than that extracted from the total HRV. Compared with diabetes patients, the cardiovascular system of healthy controls showed information of higher complexity, and better regulation function in response to changes of environment.

A new approach to detect congestive heart failure using short-term heart rate variability measures

Heart rate variability (HRV) analysis has quantified the functioning of the autonomic regulation of the heart and heart's ability to respond. However, majority of studies on HRV report several differences between patients with congestive heart failure (CHF) and healthy subjects, such as time-domain, frequency domain and nonlinear HRV measures. In the paper, we mainly presented a new approach to detect congestive heart failure (CHF) based on combination support vector machine (SVM) and three nonstandard heart rate variability (HRV) measures (e.g. SUM_TD, SUM_FD and SUM_IE). The CHF classification model was presented by using SVM classifier with the combination SUM_TD and SUM_FD. In the analysis performed, we found that the CHF classification algorithm could obtain the best performance with the CHF classification accuracy, sensitivity and specificity of 100%, 100%, 100%, respectively.

Ischemic risk stratification by means of multivariate analysis of the heart rate variability

In this work, a univariate and multivariate statistical analysis of indexes derived from heart rate variability (HRV) was conducted to stratify patients with ischemic dilated cardiomyopathy (IDC) in cardiac risk groups. Indexes conditional entropy, refined multiscale entropy (RMSE), detrended fluctuation analysis, time and frequency analysis, were applied to the RR interval series (beat-to-beat series), for single and multiscale complexity analysis of the HRV in IDC patients. Also, clinical parameters were considered. Two different end-points after a follow-up of three years were considered: (i) analysis A, with 151 survivor patients as a low risk group and 13 patients that suffered sudden cardiac death as a high risk group; (ii) analysis B, with 192 survivor patients as a low risk group and 30 patients that suffered cardiac mortality as a high risk group. A univariate and multivariate linear discriminant analysis was used as a statistical technique for classifying patients in risk groups. Sensitivity (Sen) and specificity (Spe) were calculated as diagnostic criteria in order to evaluate the performance of the indexes and their linear combinations. Sen and Spe values of 80.0% and 72.9%, respectively, were obtained during daytime by combining one clinical parameter and one index from RMSE, and during nighttime Sen = 80% and Spe = 73.4% were attained by combining one clinical factor and two indexes from RMSE. In particular, relatively long time scales were more relevant for classifying patients into risk groups during nighttime, while during daytime shorter scales performed better. The results suggest that the left atrial size, indexed to body surface and RMSE indexes are those that allow enhanced classification of ischemic patients in their respective risk groups, confirming that a single measurement is not enough to fully characterize ischemic risk patients and the clinical relevance of HRV complexity measures.

Heart rate variability

Heart rate variability (HRV) provides indirect insight into autonomic nervous system tone, and has a well-established role as a marker of cardiovascular risk. Recent decades brought an increasing interest in HRV assessment as a diagnostic tool in detection of autonomic impairment, and prediction of prognosis in several neurological disorders. Both bedside analysis of simple markers of HRV, as well as more sophisticated HRV analyses including time, frequency domain and nonlinear analysis have been proven to detect early autonomic involvement in several neurological disorders. Furthermore, altered HRV parameters were shown to be related with cardiovascular risk, including sudden cardiac risk, in patients with neurological diseases. This chapter aims to review clinical and prognostic application of HRV analysis in diabetes, stroke, multiple sclerosis, muscular dystrophies, Parkinson's disease and epilepsy.

Autonomic modulation of heart rate in paraplegic wheelchair basketball players: Linear and nonlinear analysis

This study aimed to evaluate the autonomic modulation of heart rate in sedentary paraplegics and paraplegic wheelchair basketball players with thoracic spinal cord injury below T6. Seven paraplegic wheelchair basketball players (active paraplegic group), five paraplegics who were not involved in regular exercise (sedentary paraplegic group) and 10 able-bodied participants (control group) took part in the study. The heart rate variability was evaluated by linear (low frequency and high frequency band in normalised units and low frequency/high frequency ratio) and nonlinear methods (Shannon entropy, corrected conditional entropy, and symbolic analysis). The sedentary group presented significantly higher values for low frequency, low frequency/high frequency ratio and symbolic index with no significant variations (0V%), and also lower values for the high frequency and symbolic index with two significant unlike variation (2ULV%) compared to active paraplegic group. Shannon entropy and corrected conditional entropy analyses revealed significantly lower values in the sedentary group than in the control or active paraplegic groups. Paraplegic individuals who regularly undertake physical exercise have higher complexity of R-R interval time series, lower sympathetic modulation, and higher parasympathetic modulation than sedentary paraplegic participants.

Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score

Introduction

A key aim of triage is to identify those with high risk of cardiac arrest, as they require intensive monitoring, resuscitation facilities, and early intervention. We aim to validate a novel machine learning (ML) score incorporating heart rate variability (HRV) for triage of critically ill patients presenting to the emergency department by comparing the area under the curve, sensitivity and specificity with the modified early warning score (MEWS).

Methods

We conducted a prospective observational study of critically ill patients (Patient Acuity Category Scale 1 and 2) in an emergency department of a tertiary hospital. At presentation, HRV parameters generated from a 5-minute electrocardiogram recording are incorporated with age and vital signs to generate the ML score for each patient. The patients are then followed up for outcomes of cardiac arrest or death.

Results

From June 2006 to June 2008 we enrolled 925 patients. The area under the receiver operating characteristic curve (AUROC) for ML scores in predicting cardiac arrest within 72 hours is 0.781, compared with 0.680 for MEWS (difference in AUROC: 0.101, 95% confidence interval: 0.006 to 0.197). As for in-hospital death, the area under the curve for ML score is 0.741, compared with 0.693 for MEWS (difference in AUROC: 0.048, 95% confidence interval: -0.023 to 0.119). A cutoff ML score ≥ 60 predicted cardiac arrest with a sensitivity of 84.1%, specificity of 72.3% and negative predictive value of 98.8%. A cutoff MEWS ≥ 3 predicted cardiac arrest with a sensitivity of 74.4%, specificity of 54.2% and negative predictive value of 97.8%.

Conclusion

We found ML scores to be more accurate than the MEWS in predicting cardiac arrest within 72 hours. There is potential to develop bedside devices for risk stratification based on cardiac arrest prediction.

Fractal variability: an emergent property of complex dissipative systems

The patterns of variation of physiologic parameters, such as heart and respiratory rate, and their alteration with age and illness have long been under investigation; however, the origin and significance of scale-invariant fractal temporal structures that characterize healthy biologic variability remain unknown. Quite independently, atmospheric and planetary scientists have led breakthroughs in the science of non-equilibrium thermodynamics. In this paper, we aim to provide two novel hypotheses regarding the origin and etiology of both the degree of variability and its fractal properties. In a complex dissipative system, we hypothesize that the degree of variability reflects the adaptability of the system and is proportional to maximum work output possible divided by resting work output. Reductions in maximal work output (and oxygen consumption) or elevation in resting work output (or oxygen consumption) will thus reduce overall degree of variability. Second, we hypothesize that the fractal nature of variability is a self-organizing emergent property of complex dissipative systems, precisely because it enables the system's ability to optimally dissipate energy gradients and maximize entropy production. In physiologic terms, fractal patterns in space (e.g., fractal vasculature) or time (e.g., cardiopulmonary variability) optimize the ability to deliver oxygen and clear carbon dioxide and waste. Examples of falsifiability are discussed, along with the need to further define necessary boundary conditions. Last, as our focus is bedside utility, potential clinical applications of this understanding are briefly discussed. The hypotheses are clinically relevant and have potential widespread scientific relevance.

Correntropy-based nonlinearity test applied to patients with chronic heart failure

In this study we propose the correntropy function as a discriminative measure for detecting nonlinearities in the respiratory pattern of chronic heart failure (CHF) patients with periodic or nonperiodic breathing pattern (PB or nPB, respectively). The complexity seems to be reduced in CHF patients with higher risk level. Correntropy reflects information on both, statistical distribution and temporal structure of the underlying dataset. It is a suitable measure due to its capability to preserve nonlinear information. The null hypothesis considered is that the analyzed data is generated by a Gaussian linear stochastic process. Correntropy is used in a statistical test to reject the null hypothesis through surrogate data methods. Various parameters, derived from the correntropy and correntropy spectral density (CSD) to characterize the respiratory pattern, presented no significant differences when extracted from the iteratively refined amplitude adjusted Fourier transform (IAAFT) surrogate data. The ratio between the powers in the modulation and respiratory frequency bands R was significantly different in nPB patients, but not in PB patients, which reflects a higher presence of nonlinearities in nPB patients than in PB patients.

Association of Holter-based measures including T-wave alternans with risk of sudden cardiac death in the community-dwelling elderly: the Cardiovascular Health Study

BACKGROUND

Sudden cardiac death (SCD) can be the first manifestation of cardiovascular disease. Development of screening methods for higher/lower risk is critical.

METHODS

The Cardiovascular Health Study is a population-based study of risk factors for coronary heart disease and stroke those 65 years or older. Forty-nine (of 1649) with usable Holters and in normal sinus rhythm had SCD during follow-up and were matched with 2 controls, alive at the time of death of the case and not experiencing SCD on follow-up. Univariate and multivariate conditional logistic regression determined the association of Holter-based information and SCD.

RESULTS

In univariate models, the upper half of ventricular premature contraction (VPC) counts, abnormal heart rate turbulence, decreased normalized low-frequency power, increased T-wave alternans (TWA), and decreased the short-term fractal scaling exponent (DFA(1)) were associated with SCD, but time domain heart rate variability was not. In multivariate models, the upper half of VPC counts (odds ratio [OR], 6.6) and having TWA of 37 muV or greater on channel 2 (OR, 4.8) were independently associated with SCD. Also, the upper half of VPC counts (OR, 6.9) and having a DFA(1) of less than 1.05 (OR, 5.0) were independently associated with SCD. When additive effects were explored, having both higher VPCs and higher TWA was associated with an OR of 8.2 for SCD compared with 2.6 for having either. Also, having both higher VPCs and lower DFA(1) was associated with an OR of 9.6 for SCD compared with 3.1 for having either.

CONCLUSIONS

Results support a potential role for 24-hour Holter recordings to identify older adults at increased or lower risk of SCD.

Clinical review: a review and analysis of heart rate variability and the diagnosis and prognosis of infection

Bacterial infection leading to organ failure is the most common cause of death in critically ill patients. Early diagnosis and expeditious treatment is a cornerstone of therapy. Evaluating the systemic host response to infection as a complex system provides novel insights: however, bedside application with clinical value remains wanting. Providing an integrative measure of an altered host response, the patterns and character of heart rate fluctuations measured over intervals-in-time may be analysed with a panel of mathematical techniques that quantify overall fluctuation, spectral composition, scale-free variation, and degree of irregularity or complexity. Using these techniques, heart rate variability (HRV) has been documented to be both altered in the presence of systemic infection, and correlated with its severity. In this review and analysis, we evaluate the use of HRV monitoring to provide early diagnosis of infection, document the prognostic implications of altered HRV in infection, identify current limitations, highlight future research challenges, and propose improvement strategies. Given existing evidence and potential for further technological advances, we believe that longitudinal, individualized, and comprehensive HRV monitoring in critically ill patients at risk for or with existing infection offers a means to harness the clinical potential of this bedside application of complex systems science.

Complexity of cardiovascular regulation in small animals

Oscillations of heart rate and blood pressure are related to the activity of the underlying control mechanism. They have been investigated mostly with linear methods in the time and frequency domains. Also, in recent years, many different nonlinear analysis methods have been applied for the evaluation of cardiovascular variability. This review presents the most commonly used nonlinear methods. Physiological understanding is obtained from various results from small animals.

Methods derived from nonlinear dynamics for analysing heart rate variability

Methods from nonlinear dynamics (NLD) have shown new insights into heart rate (HR) variability changes under various physiological and pathological conditions, providing additional prognostic information and complementing traditional time- and frequency-domain analyses. In this review, some of the most prominent indices of nonlinear and fractal dynamics are summarized and their algorithmic implementations and applications in clinical trials are discussed. Several of those indices have been proven to be of diagnostic relevance or have contributed to risk stratification. In particular, techniques based on mono- and multifractal analyses and symbolic dynamics have been successfully applied to clinical studies. Further advances in HR variability analysis are expected through multidimensional and multivariate assessments. Today, the question is no longer about whether or not methods from NLD should be applied; however, it is relevant to ask which of the methods should be selected and under which basic and standardized conditions should they be applied.

An observational, prospective study exploring the use of heart rate variability as a predictor of clinical outcomes in pre-hospital ambulance patients

OBJECTIVE

To explore the use of pre-hospital heart rate variability (HRV) as a predictor of clinical outcomes such as hospital admission, intensive care unit (ICU) admission and mortality. We also implemented an automated pre-analysis signal processing algorithm and multiple principal component analysis (PCA) for outcomes.

MATERIALS AND METHODS

We conducted a prospective observational clinical study at an emergency medical services (EMS) system in a medium sized urban setting in the United States. Electrocardiogram (ECG) data was obtained from a sample of 45 ambulance patients conveyed to a tertiary hospital, monitored with a LIFEPAK12 defibrillator/monitor. After extracting the data, filtering for noise reduction and isolating non-sinus beats, various HRV parameters were computed. These included time domain, frequency domain and geometric parameters. PCA was performed on the hospital outcomes for these patients.

RESULTS

We used a combination of HRV parameters, age and vital signs such as respiratory rate, SpO2 and Glasgow coma score (GCS) in a PCA analysis. For predicting admission to ICU, sensitivity was 100%, specificity was 48.6%, and negative predictive value (NPV) was 100%; for predicting admission to hospital, sensitivity was 78.9%, specificity was 85.7%, and NPV was 75.0%; for predicting death, sensitivity was 50.0%, specificity was 100%, and NPV was 97.4%. There was also a significant correlation of several HRV parameters with length of hospital stay.

CONCLUSIONS

With signal processing techniques, it is feasible to filter and analyze ambulance ECG data for HRV. We found a combination of HRV parameters and traditional 'vital signs' to have an association with clinical outcomes in pre-hospital patients. This may have potential as a triage tool for ambulance patients.

Inflammation in chronic heart failure

OBJECTIVE

To summarize findings regarding the association of inflammatory processes with chronic heart failure (HF).

DATA SOURCES

We conducted PubMed/MEDLINE searches (1966-January 2008) of primary literature using the following key words: ACE inhibitors, allopurinol, angiotensin-receptor antagonists, cardiomyopathy, chemokines, cytokines, diuretics, heart failure, inflammation, interleukins, HMG-CoA reductase inhibitors, immunotherapy, medications used in heart failure, thalidomide, tumor necrosis factor, and uric acid.

STUDY SELECTION AND DATA EXTRACTION

All articles that appeared to be relevant were read; of 305 articles examined, 87 were selected for discussion. Articles were selected if they were written in English and focused on any of the key words or appeared to have substantial content addressing inflammation in HF.

DATA SYNTHESIS

Cytokines, uric acid, and other inflammatory mediators are associated with physiologic effects that are also prominent features of HF (eg, reduced contractility and cardiac output, endothelial dysfunction, hypercoagulability, autonomic dysfunction as evidenced by reduced resting heart rate variability, insulin resistance). With the exception of elevated tumor necrosis factor-alpha as a cause of insulin resistance, it is not clear whether elevated inflammatory mediators directly cause HF signs and symptoms or whether they are incidental markers. Awareness of these associations has occurred relatively recently; there have been few clinical studies of efforts to directly modify inflammatory mediators. Most currently accepted drug therapies of HF reduce concentrations of circulating cytokines, but the significance of these findings awaits directed study.

CONCLUSIONS

Loss of myocardial function, autonomic dysfunction, and glucose intolerance are interrelated and linked by underlying chronic low-grade inflammation. Drug therapy with statins, pentoxifylline, and perhaps urate-lowering agents, in addition to current therapies, holds promise for treatment of HF.

Heart rate chaos as a mortality predictor in mild to moderate heart failure

Linear and nonlinear indices of heart rate variability (HRV) have been shown to predict mortality in congestive heart failure (CHF). However, most nonlinear indices describe only the fractality or complexity of HRV but not the intrinsic chaotic properties. In the present study, we performed linear (time- and frequency-domain), complexity (sample entropy), fractal (detrended fluctuation analysis) and chaos (numerical titration) analyses on the HRV of 50 CHF patients from the United Kingdom heart failure evaluation and assessment of risk trial database. Receiver operating characteristic and survival analysis yielded the chaos level to be the best predictor of mortality (followed by low/high frequency power ratio, LF/HF), such that these indices were significant in both univariate and multivariate models. These results indicate the power of heart rate chaos analysis as a potential prognostic tool for CHF.

Diastolic dysfunction and autonomic abnormalities in patients with systolic heart failure

BACKGROUND

Patients with systolic heart failure (SHF) often have concomitant diastolic dysfunction (DD). SHF is associated with decreased heart rate variability (HRV), but the impact of degree of DD on HRV in SHF is unclear.

METHODS AND RESULTS

HRV was measured in 139 patients, aged 64+/-12 years, 74% male, LVEF 30+/-8%. Patients had stable NYHA class II-III CHF on ACE inhibitors or ATII receptor blockers, with LVEF<or=40% and BNP>or=200 pg/ml. Subjects underwent 2-D echocardiography with Doppler assessment and 24-h Holters. Patients were categorized as having impaired relaxation (E-deceleration time>2 SD above age-adjusted normal values (AANV), E/A<or=1, systolic/diastolic pulmonary vein flow>or=1; N=30), pseudonormal (E-deceleration time within 2 SD of AANV, E/A=1-2, systolic/diastolic pulmonary vein flow<1; N=25) or restrictive filling patterns (E-deceleration time>2 SD below AANV or/and E/A ratio>or=2; N=84) Differences were adjusted for clinical covariates using UNIANOVA, p<0.05. HRV was reduced and BNP higher in pseudonormal patients compared to impaired relaxation, but this difference was only significant for restrictive vs. impaired filling. Differences remained significant after adjustment for covariates.

CONCLUSION

Significantly more abnormal HRV, reflecting greater cardiac autonomic dysfunction, is associated with restrictive DD compared to impaired relaxation.

An integrated approach based on uniform quantization for the evaluation of complexity of short-term heart period variability: Application to 24 h Holter recordings in healthy and heart failure humans

We propose an integrated approach based on uniform quantization over a small number of levels for the evaluation and characterization of complexity of a process. This approach integrates information-domain analysis based on entropy rate, local nonlinear prediction, and pattern classification based on symbolic analysis. Normalized and non-normalized indexes quantifying complexity over short data sequences ( approximately 300 samples) are derived. This approach provides a rule for deciding the optimal length of the patterns that may be worth considering and some suggestions about possible strategies to group patterns into a smaller number of families. The approach is applied to 24 h Holter recordings of heart period variability derived from 12 normal (NO) subjects and 13 heart failure (HF) patients. We found that: (i) in NO subjects the normalized indexes suggest a larger complexity during the nighttime than during the daytime; (ii) this difference may be lost if non-normalized indexes are utilized; (iii) the circadian pattern in the normalized indexes is lost in HF patients; (iv) in HF patients the loss of the day-night variation in the normalized indexes is related to a tendency of complexity to increase during the daytime and to decrease during the nighttime; (v) the most likely length L of the most informative patterns ranges from 2 to 4; (vi) in NO subjects classification of patterns with L=3 indicates that stable patterns (i.e., those with no variations) are more present during the daytime, while highly variable patterns (i.e., those with two unlike variations) are more frequent during the nighttime; (vii) during the daytime in HF patients, the percentage of highly variable patterns increases with respect to NO subjects, while during the nighttime, the percentage of patterns with one or two like variations decreases.

Dependence of heart rate variability on heart period in disease and aging

The aim of this study was to compare the dependence of heart rate variability (HRV) on heart period (RR interval length) under different physiological and pathological states in order to detect changes in HR modulation. The dependence of HRV on the RR interval length in healthy elderly subjects, congestive heart failure (CHF) patients and one patient with a transplanted heart (T) was compared with healthy young subjects. Spectral powers, sample entropy (SampEn) and short-term fractal scaling exponent (alpha1) were determined from 24 h free-running recordings. For the same HR, HRV measures were different in different groups. In healthy subjects HRV measures depended on RR interval length and all spectral powers were highly correlated, although reduced in elderly subjects. SampEn at high HR was the most sensitive quantity to changes induced by aging. In disease, CHF and T, an achievable HR range was decreased, all spectral powers were reduced, but correlated, and the dependence of HRV measures on RR was lost. There was an evident difference in the dependence of nonlinear on linear measures between young subjects and all the other studied groups. In disease the reduction in autonomic control was associated with the decrease in short-range correlation and regularity in RR series. We have concluded that the analysis of HRV measures as functions of RR interval length can reveal important aspects of HR control that might be lost in averaging.

Loss of lag-response curvilinearity of indices of heart rate variability in congestive heart failure

BACKGROUND

Heart rate variability (HRV) is known to be impaired in patients with congestive heart failure (CHF). Time-domain analysis of ECG signals traditionally relies heavily on linear indices of an essentially non-linear phenomenon. Poincaré plots are commonly used to study non-linear behavior of physiologic signals. Lagged Poincaré plots incorporate autocovariance information and analysis of Poincaré plots for various lags can provide interesting insights into the autonomic control of the heart.

METHODS

Using Poincaré plot analysis, we assessed whether the relation of the lag between heart beats and HRV is altered in CHF. We studied the influence of lag on estimates of Poincaré plot indices for various lengths of beat sequence in a public domain data set (PhysioNet) of 29 subjects with CHF and 54 subjects with normal sinus rhythm.

RESULTS

A curvilinear association was observed between lag and Poincaré plot indices (SD1, SD2, SDLD and SD1/SD2 ratio) in normal subjects even for a small sequence of 50 beats (p value for quadratic term 3 x 10-5, 0.002, 3.5 x 10-5 and 0.0003, respectively). This curvilinearity was lost in patients with CHF even after exploring sequences up to 50,000 beats (p values for quadratic term > 0.5).

CONCLUSION

Since lagged Poincaré plots incorporate autocovariance information, these analyses provide insights into the autonomic control of heart rate that is influenced by the non-linearity of the signal. The differences in lag-response in CHF patients and normal subjects exist even in the face of the treatment received by the CHF patients.

EFFECTS OF AUTONOMIC BLOCKADE ON NON-LINEAR CARDIOVASCULAR VARIABILITY INDICES IN RATS

1. The present study assesses the effects of autonomic blockade (alpha- and beta-adrenoceptor and cholinergic) on cardiovascular function studied by heart rate variability (HRV), blood pressure variability (BPV) and baroreflex sensitivity in rats using non-linear dynamics. Little is known about the influence of pharmacological autonomic nervous system interventions on non-linear cardiovascular regulatory indices. 2. In 13 conscious rats, heart rate and aortic blood pressure were measured continuously before, during and after autonomic blockade with atropine, phentolamine and propranolol. Non-linear scaling properties were studied using 1/f slope, fractal dimension and long- and short-term correlation. Non-linear complexity was described with correlation dimension, Lyapunov exponent and approximate entropy. Non-linear indices were compared with linear time and frequency domain indices. 3. Beta-adrenoceptor blockade did not alter the non-linear characteristics of HRV and BPV, although low-frequency power of HRV was depressed. Alpha-adrenoceptor blockade decreased the scaling behaviour of HRV, whereas cholinergic blockade decreased the complexity of the non-linear system of HRV. For BPV, the scaling behaviour was increased during alpha-adrenoceptor blockade and the complexity was increased during cholinergic blockade. The linear indices of HRV and BPV were decreased. 4. The present results indicate that the beta-adrenoceptor system has little involvement in the generation of non-linear HRV and BPV in rats. 5. Alpha-adrenoceptor blockade mostly influenced the scaling properties of the time series, whereas cholinergic blockade induced changes in the complexity measures. 6. The absence of the baroreflex mechanism can trigger a compensatory feed-forward system increasing the complexity of BPV.

Long-Term Effects of Cardiac Resynchronization Therapy on Heart Rate and Heart Rate Variability

Congestive heart failure is characterized by significant autonomic dysfunction. Development of left bundle branch block in congestive heart failure is a predictor of worse outcome. There are several lines of evidence that cardiac resynchronization therapy (CRT), by biventricular stimulation in patients with severe heart failure and left bundle branch block, improves autonomic functions which can be quantified by measuring heart rate variability. The aim of the present study was to assess the effect of CRT on autonomic functions quantified by heart rate variability and mean heart rate (HR) in patients with advanced heart failure and left bundle branch block in short and long-term follow-up. A total of 35 patients with systolic heart failure and left bundle branch block (mean-age 60 +/- 11 years; 24 male and 11 female; mean left ventricular ejection fraction [EF]: 22.3 +/- 3%) were enrolled. Clinical assessment and echocardiographic examination were performed at baseline and every three months. Continuous electrocardiographic monitorization by 24-hour Holter recordings was performed pre-implantation, 3 months and 2 years after implantation. Mean HR and one of the time-domain parameters of heart rate variability, standard deviation of the R-R intervals (SDNN) were measured. CRT was associated with a decrease in the mean duration of QRS, and an increase in diastolic filling time, the rate with which the left ventricular pressure rises (dP/dt), and left ventricular ejection fraction. Decrease in mean heart rate and increase in SDNN were statistically significant in the third month and second year recordings when compared to baseline recording (p values were < 0.001 for both). In conclusion, CRT with biventricular pacing provides sustained improvement in autonomic function in patients with advanced heart failure and left bundle branch block.

Aging and nonlinear heart rate control in a healthy population

In recent years more studies are using nonlinear dynamics to describe cardiovascular control. Because of the large dispersion of physiological data, it is important to have large studies with both male and female participants to establish a range of physiological healthy values. This study investigated the effect of gender and age on nonlinear indexes. Nonlinear scaling properties were studied by using 1/f slope (where f is frequency), fractal dimension, and detrended fluctuation analysis short- and long-term correlations (DFAalpha(1) and DFAalpha(2), respectively). Nonlinear complexity was described with correlation dimension (CD), Lyapunov exponent (LE), and approximate entropy (ApEn). The population consisted of 135 women and 141 men (age, 18-71 yr). Twenty-four hour ECG recordings were obtained by using Holter monitoring. The recordings were split into daytime (8 AM-9 PM) and nighttime (11 PM-6 AM). A day-night variation was present in all nonlinear heart rate variability (HRV) indexes, except for the CD in the female population. During the night the percentage of CD values of surrogate data files differing from the CD value of the original data increased. All nonlinear indexes were significantly correlated with age. Deeper analysis per age category of 10 yr showed a stabilization in the age decline of the fractal dimension and ApEn at the age of > or =40 yr. The vagal pathways seemed to be more involved in the generation of nonlinear fluctuations. Higher nonlinear behavior was evident during the night. No clear difference between men and women was found in the nonlinear indexes. Nonlinear indexes decline with age. This can be related to the concept of decreasing autonomic modulation with advancing age.

Heart rate variability analysis in the assessment of autonomic function in heart failure

Heart rate is not static, but rather changes continuously in response to physical and mental demands. In fact, an invariant heart rate is associated with disease processes such as heart failure. Heart rate variability analysis is a noninvasive technique used to quantify fluctuations in heart rate. In this article, the authors review neural control of heart rate, briefly describe heart rate variability, and summarize research data demonstrating that heart failure is associated with altered heart rate variability. In addition, the authors present evidence that heart failure patients with decreased heart rate variability are at risk for future cardiac events, heart transplantations, and death.

Different spectral components of 24 h heart rate variability are related to different modes of death in chronic heart failure

AIMS

To assess whether analysis of heart rate variability (HRV) from 24 h Holter recordings provides information about the mode of death (pump failure vs. sudden death) in chronic heart failure (CHF).

METHODS AND RESULTS

We analysed 24 h HRV in 330 consecutive CHF patients in sinus rhythm. Indices derived from time domain, spectral domain, and fractal analyses of 24 h automatic HRV were evaluated. Data from clinical assessment, echocardiography, right heart catheterization, exercise test, blood biochemical examination, and arrhythmia pattern were analysed. Patients were followed up for 3 years. Two simple multivariable models, both including 24 h spectral indices, were able to identify patients at higher risk of progressive pump failure and sudden death, respectively. Depressed power of night-time HRV (< or = 509 ms(2)) below 0.04 Hz [very low frequency (VLF)], high pulmonary wedge pressure (PWP > or = 18 mm Hg) and low left ventricular ejection fraction (LVEF < or = 24%) were independently related to death for progressive pump failure, while the reduction of power between 0.04 and 0.15 Hz at night (LF < or = 20 ms(2)) and increased left ventricular end-systolic diameter (LVESD > or = 61 mm) were linked to sudden mortality.

CONCLUSION

Automatic spectral analysis of 24 h HRV provides independent risk indices related to mode of death in sinus rhythm CHF patients.

Complex systems and the technology of variability analysis

Characteristic patterns of variation over time, namely rhythms, represent a defining feature of complex systems, one that is synonymous with life. Despite the intrinsic dynamic, interdependent and nonlinear relationships of their parts, complex biological systems exhibit robust systemic stability. Applied to critical care, it is the systemic properties of the host response to a physiological insult that manifest as health or illness and determine outcome in our patients. Variability analysis provides a novel technology with which to evaluate the overall properties of a complex system. This review highlights the means by which we scientifically measure variation, including analyses of overall variation (time domain analysis, frequency distribution, spectral power), frequency contribution (spectral analysis), scale invariant (fractal) behaviour (detrended fluctuation and power law analysis) and regularity (approximate and multiscale entropy). Each technique is presented with a definition, interpretation, clinical application, advantages, limitations and summary of its calculation. The ubiquitous association between altered variability and illness is highlighted, followed by an analysis of how variability analysis may significantly improve prognostication of severity of illness and guide therapeutic intervention in critically ill patients.

Functional assessment of heart rate variability: physiological basis and practical applications

The autonomic nervous system dynamically controls the response of the body to a range of external and internal stimuli, providing physiological stability in the individual. With the progress of information technology, it is now possible to explore the functioning of this system reliably and non-invasively using comprehensive and functional analysis of heart rate variability. This method is already an established tool in cardiology research, and is increasingly being used for a range of clinical applications. This review describes the theoretical basis and practical applications for this emerging technique.

Emerging excitatory role of cardiovascular sympathetic afferents in pathophysiological conditions

There is sound experimental evidence that cardiovascular sympathetic afferent fibers mediate cardiovascular reflexes largely excitatory in nature with positive-feedback characteristics. This afferent neural channel is likely to normally participate in the neural regulation of cardiovascular function. The hypothesis, which is the core of this article, is that in some pathophysiological conditions, sympathetic overactivity may be partly due to an emerging excitatory reflex action of cardiovascular sympathetic afferents. In fact, the early phase of congestive heart failure can be characterized by an increase in arterial pressure and heart rate and/or by a diastolic dysfunction, leaving unchanged the cardiac output; in these conditions, in which no baroreceptor deactivation should occur, it is possible that cardiovascular sympathetic afferents with sensory endings in the thoracic low-pressure areas, highly responsive to volume loading, are responsible for mediating the reflex sympathetic excitation. Similarly, during acute myocardial infarction, ventricular sympathetic afferents are likely to mediate a reflex sympathetic overactivity, which is known to facilitate sudden death. Finally, numerous reports have described in essential arterial hypertension an increased sympathetic activity that may be due, at least in part, to the reinforcing action of sympathosympathetic reflexes. Thus, in pathophysiological conditions, cardiovascular sympathetic afferents would mediate a reflex sympathetic overactivity independently of baroreceptive mechanisms, and such an absence of a homeostatic purpose would provide a better rationale for some beneficial effects of therapeutic correction.

Heart rate variability in chronic heart failure

The analysis of heart rate variability (HRV) provides information about autonomic cardiovascular control in healthy subjects. In the past 15 years, several articles have been published regarding HRV and chronic heart failure (CHF). The results of these papers substantially demonstrated that HRV is significantly different in CHF patients compared to controls. Moreover, some variables derived from HRV analysis showed significant independent prognostic capacity. In particular, the reduction of variance (expressed as SDNN) and low-frequency spectral component of HRV (ranging from 0.03 to 0.15 Hz) seem related to an increased mortality in CHF. Nevertheless, these variables are not yet considered in clinical practice. A better understanding of the physiopathological basis of the reported alterations of HRV in CHF patients is required in order to permit its use as a clinical tool for prognosis and tailored therapy in individual CHF patients.