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Atrial natriuretic peptides (ANP) and brain natriuretic peptides (BNP) are powerful neurohormonal indicators of left-ventricular function and prognosis in heart failure (HF). Chagas disease (CD) caused by the protozoan Trypanosoma cruzi. remains a major cause of HF in Latin America. We assessed whether the plasma concentration of the third natriuretic peptide, C-type natnuretic peptide (CNP), also has diagnostic and prognostic properties in patients with CD or other dilated cardiomyopathies (DCM). Blood samples were obtained from 66 patients with CD, 50 patients with DCM from other causes, and 30 gender- and age-matched healthy subjects. Patients were subdivided according to the New York Heart Association (NYHA) class. The CNP concentration was determined by radioimmunoassay (Immundiagnostik, Bensheim, Germany). The main duration of follow-up was 31.4 months (range 13 to 54 months), 19 patients had died and 11 patients received a heart transplant. CNP concentrations were only significantly altered in patients with DCM or CD of the NYHA classes III and IV (P < 0.05). The Pearson correlation of echocardiographic data with CNP revealed an association only with the left-ventricular end systolic volume (P = 0.03) in patients with DCM. Furthermore, CNP did not predict mortality or the necessity for heart transplant. Our data are the first to demonstrate the raised levels of the third natriuretic peptide CNP in CD and other DCM Whereas ANP and BNP have a high predictive value for mortality in both diseases, CNP is without any predictive potency.
Standard time and frequency parameters of heart rate variability (HRV) describe only linear and periodic behaviour, whereas more complex relationships cannot be recognised. A method that may be capable of assessing more complex properties is the non-linear measure of 'renormalised entropy.' A new concept of the method, RE(AR), has been developed, based on a non-linear renormalisation of autoregressive spectral distributions. To test the hypothesis that renormalised entropy may improve the result of high-risk stratification after myocardial infarction, it is applied to a clinical pilot study (41 subjects) and to prospective data of the St George's Hospital post- infarction database (572 patients). The study shows that the new RE(AR) method is more reproducible and more stable in time than a previously introduced method (p<0.001). Moreover, the results of the study confirm the hypothesis that on average, the survivors have negative values of RE(AR) (-0.11+/-0.18), whereas the non-survivors have positive values (0.03+/-0.22, p<0.01). Further, the study shows that the combination of an HRV triangular index and RE(AR) leads to a better prediction of sudden arrhythmic death than standard measurements of HRV. In summary, the new RE(AR) method is an independent measure in HRV analysis that may be suitable for risk stratification in patients after myocardial infarction.
Objectives. Ventricular tachycardia (VT) provoking sudden cardiac death (SCD) are a major cause of mortality in the developed countries. The most efficient therapy for SCID prevention are implantable cardioverter defibrillators (ICD). In this study heart rate variability (HRV) measures were analyzed for short-term forecasting of VT in order to improve VT sensing and to enable a patient warning of forthcoming shocks. Methods. The lost 1000 normal beat-to-beat intervals before 50 VT episodes stored by the ICD were analyzed and compared to individually acquire control time series (CON). HRV analysis was performed with standard parameters of time and frequency domain as suggested by the HRV Task Force and furthermore with a newly developed and optimized nonlinear parameter that assesses the compression entropy of heart rate (H-c). Results. Except of meanNN (p = 0.02) we found no significant differences in standard HRV parameters. In contrast, H, revealed highly significant (p = 0.007) alterations in VT compared with CON suggesting a decreased complexity before the onset of VT. Conclusion: Compression entropy might be a suitable parameter for short-term forecasting of life-threatening tachycardia in ICD
Objective: Impairment of the baroreceptor reflex activity reflects an alteration of the autonomous regulation of the cardiovascular system and has proven to predict fatal outcome in patients after acute myocardial infarction. The following study was performed to analyse the baroreceptor sensitivity, heart rate variability and blood pressure variability in patients early after coronary surgery. Methods: Twenty-five male patients undergoing coronary artery bypass were examined in a prospective study; normal values were obtained from healthy volunteers. Arterial pressure signals were recorded from a radial artery catheter for 30 min preoperatively and in short intervals after surgery. Mechanical manipulations and pharmacological interventions were avoided during the sampling periods. Baroreflex function was calculated according to the dual sequence method, heart rate variability and blood pressure variability were calculated including nonlinear methods. Results: Initial values of the patients did not differ from healthy volunteers. The strength of baroreflex sensitivity (increase in blood pressure causing a synchronous decrease of heart rate) is low 2 It postoperatively. The number of delayed tachycardic changes of heart rate, which are caused by sympathetic activation, is only moderately reduced as compared to values obtained from healthy volunteers. Heart rate variability is widely unchanged as compared to preoperative values; blood pressure variability showed an increase of low-frequency components, again indicating sympathetic predominance. Nonlinear analyses revealed reduced system complexity at the beginning of the postoperative course. Conclusion: Obviously, there is a vagal suppression 20 h after surgery, while the sympathetic tonus works in a normal range. This unbalanced interaction of the autonomous systems is similar to findings in patients after myocardial infarction. The predictive value of these markers has to be elucidated in further clinical studies. (C) 2003 Elsevier B.V. All rights reserved
Objectives: Scoring sleep visually based on polysomnography is an important but time-consuming element of sleep medicine. Where-as computer software assists human experts in the assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient. This study evaluates the possibility to fully automatize sleep staging considering the reliability of the sleep stages available from human expert sleep scorers. Methods: We obtain features from EEG, ECG and respiratory signals of polysomnograms from ten healthy subjects. Using the sleep stages provided by three human experts, we evaluate the performance of linear discriminant analysis on the entire polysomnogram and:only on epochs where the three experts agree in their-sleep stage scoring. Results: We show that in polysomnogram intervals, to which all three scorers assign the same sleep stage, our algorithm achieves 90% accuracy. This high rate of agreement with the human experts is accomplished with only a small set of three frequency features from the EEG. We increase-the performance to 93% by including ECG and respiration features. In contrast, on intervals of ambiguous sleep stage, the sleep stage classification obtained from our algorithm, agrees with the human consensus scorer in approximately 61%. Conclusions: These findings suggest that machine classification is highly consistent with human sleep staging and that error in the algorithm's assignments is rather a problem of lack of well-defined criteria for human experts to judge certain polysomnogram epochs than an insufficiency of computational procedures
The incidence of cardiovascular diseases increases with the growth of the human population and an aging society, leading to very high expenses in the public health system. Therefore, it is challenging to develop sophisticated methods in order to improve medical diagnostics. The question whether the normal heart rate is chaotic or not is an attempt to elucidate the underlying mechanisms of cardiovascular dynamics and therefore a highly controversial topical challenge. In this contribution we demonstrate that linear and nonlinear parameters allow us to separate completely the data sets of the three groups provided for this controversial topic in nonlinear dynamics. The question whether these time series are chaotic or not cannot be answered satisfactorily without investigating the underlying mechanisms leading to them. We give an example of the dominant influence of respiration on heart beat dynamics, which shows that observed fluctuations can be mostly explained by respiratory modulations of heart rate and blood pressure (coefficient of determination: 96%). Therefore, we recommend reformulating the following initial question: "Is the normal heart rate chaotic?" We rather ask the following: " Is the normal heart rate 'chaotic' due to respiration?"