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K Hnatkova

Publications and source records attributed to K Hnatkova.

At least 55 records · Page 3Linked to original sources
PubMed1996

Comparison of time domain and spectral turbulence analysis of the signal-averaged electrocardiogram for the prediction of prognosis in idiopathic dilated cardiomyopathy.

BACKGROUND: Despite significant advances in the treatment of heart failure, the prognosis of patients with idiopathic dilated cardiomyopathy remains poor. Although several of prognostic variables have been shown to be useful in risk stratification of patients with idiopathic dilated cardiomyopathy, their predictive accuracy is low and clinical usefulness uncertain. HYPOTHESIS: This study was undertaken to assess the signal-averaged electrocardiogram (SAECG) in patients with idiopathic dilated cardiomyopathy and to compare the ability of time domain and spectral turbulence analytic techniques to predict clinical outcome. METHODS: SAECG analysis was performed in 80 patients with idiopathic dilated cardiomyopathy. Nineteen patients had left bundle-branch block and eight were taking low-dose amiodarone for life-threatening arrhythmias. Conventional time domain and spectral turbulence analyses of the SAECG were performed using Del Mar 183 software. RESULTS: During a follow-up of 27 +/- 19 months, 24 patients developed progressive heart failure, while the others remained clinically stable. Late potentials were detected in 28% of patients and were equally frequent in patients with and without progressive heart failure (38 vs. 23%, p = 0.20). Spectral turbulence analysis was abnormal in 34% of patients, and patients with abnormal results developed progressive heart failure more frequently than those with normal results (50 vs. 17%, p = 0.01). All spectral turbulence analysis parameters were significantly different in patients with progressive heart failure compared with those who remained clinically stable (p < or = 0.01). Furthermore, progressive heart failure-free survival at 2 years was significantly lower in patients with abnormal compared with normal results (63 vs. 87%; p < 0.05), but was similar in patients with and without late potentials (72 vs. 83%; p = 0.30). The relative risk for developing progressive heart failure using spectral turbulence analysis was 3.4 (95% confidence interval 1.2-9.7) and 2.8 (95% confidence interval 1.1-8.7) using time domain analysis. The sensitivity, specificity, and the positive and negative predictive accuracy for identifying patients who developed progressive heart failure were 50, 83, 50, and 83%, respectively, (p = 0.01) for spectral turbulence analysis, and 36, 85, 45 and 80%, respectively, (p = 0.09) for time domain analysis. CONCLUSION: Abnormalities in the SAECG of patients with idiopathic dilated cardiomyopathy are common and appear to provide a noninvasive marker for development of progressive heart failure.

Adult↗
PubMed1996

Predictive power of increased heart rate versus depressed left ventricular ejection fraction and heart rate variability for risk stratification after myocardial infarction. Results of a two-year follow-up study.

OBJECTIVES: The aim of this study was to compare the predictive value of mean RR interval assessed from predischarge Holter recordings with that of heart rate variability and left ventricular ejection fraction for risk stratification after myocardial infarction. BACKGROUND: Heart rate variability is a powerful tool for risk stratification after myocardial infarction. Although heart rate variability is related to heart rate, little is known of the prognostic value of 24-h mean heart rate. METHODS: A total of 579 patients surviving the acute phase of myocardial infarction were followed up for at least 2 years. Predischarge heart rate variability, 24-h mean RR interval and left ventricular ejection fraction were analyzed. RESULTS: During the first 2 years of follow-up, there were 54 deaths, 42 of which were cardiac (26 sudden). Shorter mean RR interval was a better predictor of all-cause mortality as well as cardiac and sudden death than depressed left ventricular ejection fraction. Depressed heart rate variability predicted the risk of death better than mean RR interval for sensitivities < 40%. For sensitivities > or = 40%, mean RR interval was as powerful as heart rate variability. All three variables performed equally well in predicting nonsudden cardiac death. For cardiac death prediction, a left ventricular ejection fraction < 35% had a 40% sensitivity, 78% specificity and 14% positive predictive accuracy; a mean RR interval < 700 ms had a 45% sensitivity, 85% specificity and 20% positive predictive accuracy; and a heart rate variability < 17 U had a 40% sensitivity, 86% specificity and 20% positive predictive accuracy. CONCLUSIONS: Predischarge 24-h mean heart rate is a strong predictor of mortality after myocardial infarction that can compete with left ventricular ejection fraction and heart rate variability.

Cohort Studies↗
PubMed1996

Stepwise strategy of using short- and long-term heart rate variability for risk stratification after myocardial infarction.

Independent of other established risk factors, depressed heart rate variability (HRV) has been shown to be a powerful predictor of cardiac events after MI. Unfortunately, the need of 24-hour ECG recording and subsequent laborious editing of Holter data limits the clinical use of long-term HRV. In order to perform post-MI risk stratification more efficiently, we evaluated the value of short-term HRV estimates for preselection of patients who might benefit from long-term HRV assessment. Two measures were assessed from 24-hour ambulatory ECGs recorded in 729 survivors of acute MI prior to hospital discharge. In addition to a complete 24-hour HRV index, a standard deviation of normal-to-normal RR intervals (SDNN) was obtained from the first stationary and ectopic free 5-minute segment of the Holter recording. Predictive power (relation between positive predictive accuracy and sensitivity) of a complete 24-hour HRV index in identifying patients who suffered from cardiac mortality or arrhythmic events during a 2-year follow-up was compared to the predictive power of assessing the 24-hour HRV index limited to 50%, 40%, or 20% of patients with the lowest values of 5-minute SDNN. The HRV index was significantly lower in patients who died (19 +/- 11 units) or had an arrhythmic event (AE) (18 +/- 11 units) compared to those who survived without an event (28 +/- 10 resp. 27 +/- 11 units; P < 0.001). Similarly, 5-minute SDNN was significantly lower in patients who died (25 +/- 12 ms) or suffered an AE (26 +/- 13 ms) compared to survivors (40 +/- 19 ms resp. 39 +/- 19 ms; P < 0.001). When limited to patients with depressed 5-minute SDNN, assessment of the HRV index performed better than 5-minute SDNN alone in positive prediction of cardiac events. Preselected assessment of the lowest HRV index in 50% to 20% of the total population yielded a 2-year cardiac event prediction rate as high as analysis of the HRV index in all patients. Long-term HRV assessment for risk stratification after MI in patients preselected by depressed short-term SDNN is safe and efficient, and allows a practical identification of patients with the highest likelihood of cardiac events during long-term follow-up.

Aged↗
PubMed1996

Effect of digoxin on the ventricular rate variability during paroxysmal atrial fibrillation.

This study investigated whether the irregularity of ventricular cycle length during atrial fibrillation (AF) is affected by digoxin. Patients (n = 41) with paroxysmal AF enrolled in a randomized crossover comparison of digoxin and placebo underwent 24-hour ambulatory monitoring during each treatment. Tapes containing AF episodes lasting at least 2 minutes were selected (24 recordings on placebo and 17 on digoxin). The mean (mRR) and standard deviation (SDRR) of RR intervals was calculated for each 30-second segment of AF. The resulting SDRR values were clustered according to bins of mRR values ranging from 350-650 ms in 25-ms steps. In each bin, the SDRR values of all placebo and all digoxin recordings were statistically compared for the top 5, 10, and 15 percentiles of each bin which represented the extremes of ventricular cycle length irregularity during AF. There were no significant differences between the total data of SDRR values in individual bins of mRR. However, the top 5, 10, and 15 percentiles of SDRR values corresponding to mRR values from 350-550 ms were significantly reduced by digoxin (P < 0.0001). The study concludes that although digoxin does not influence the mean variability of RR cycles during AF paroxysms, it suppresses episodes in which a fast ventricular response is associated with extreme variability of RR periods.

Anti-Arrhythmia Agents↗
PubMed1996

Changes of QT intervals associated with postural change in patients with chronic atrial fibrillation.

Although different computerized systems have been developed to localize specific patterns in electrocardiographic (ECG) signals, it is still difficult to detect T waves and measure QT intervals during atrial fibrillation. This article demonstrates the use of an auto-correlation (ECG) based system that was used to investigate the dynamicity of QT intervals related to active postural change in patients with chronic atrial fibrillation. Twenty patients (9 male, mean age 63 years) with chronic atrial fibrillation (8 idiopathic, 12 organic heart disease) were examined. Seventeen of these patients were on digoxin, but patients with other conditions potentially affecting the autonomic nervous system were not included. A 3-channel ECG was recorded digitally during active postural change from supine to standing. Data were first analyzed by the Burdick Altair system and subsequently processed using an in-house software package evaluating auto-correlations of ECG signals. An ECG channel with suitable repolarization patterns was found in 15 patients. The mean QT interval of 409.8 +/- 11.1 ms (mean +/- SE) recorded during supine position shortened to 401.9 +/- 9.89 ms during the first minute of active standing (P < 0.05) and to 394.8 +/- 10.0 ms during the second minute of active standing (P < 0.005). It did not further change during the subsequent minutes of active standing. The study shows that automatic detection of QT intervals during atrial fibrillation is possible. Although the effect of position change of the heart cannot be completely excluded, the study suggests that QT interval is changed directly by autonomic nervous mechanisms rather than indirectly via the mean heart rate.

Atrial Fibrillation↗
PubMed1996

Spectral turbulence versus time-domain analysis of signal-averaged ECG used for the prediction of different arrhythmic events in survivors of acute myocardial infarction.

INTRODUCTION: Spectral turbulence analysis of the signal-averaged ECG (SAECG) combines spectral analysis with statistical evaluation of spectrograms of individual parts of the QRS complex. It has been suggested that it may be superior to conventional time-domain analysis of the SAECG. METHODS AND RESULTS: This study compared the power of conventional time-domain (40 to 250 Hz) and spectral turbulence analyses of SAECG for the prediction of cardiac death, ventricular tachycardia, sudden arrhythmic death, and arrhythmic events (ventricular tachycardia or fibrillation, and/or sudden arrhythmic death) after acute myocardial infarction in 603 patients. The population excluded patients with bundle branch block and other conduction abnormalities. During the first 2 years of follow-up, there were 40 cardiac deaths, 21 cases of ventricular tachycardia, 1 sudden arrhythmic deaths, and 29 arrhythmic events. The positive predictive accuracy of spectral turbulence analysis was significantly higher than time-domain analysis for cardiac death at most levels of sensitivity (e.g., 26% vs 20% at 40% sensitivity, P < 0.05). The positive predictive accuracies of the two techniques were not statistically different for the prediction of ventricular tachycardia. For the prediction of sudden arrhythmic death and arrhythmic events, the positive predictive accuracy of spectral turbulence was better than that of time-domain analysis only at the higher levels of sensitivity (9% vs 2%, P < 0.001 for sudden arrhythmic death at 60% sensitivity, and 14% vs 11%, P < 0.05 for arrhythmic events at 60% sensitivity). CONCLUSIONS: Spectral turbulence analysis is essentially equivalent to time-domain analysis for the prediction of arrhythmic events after myocardial infarction. However, it performed significantly better than time-domain analysis for the prediction of cardiac death.

Acute Disease↗
PubMed1996

[Reproducibility of signal-averaged electrocardiography].

The reproducibility of the parameters defining the presence of late potentials on the signal-averaged electrocardiogram is one of the limiting factors of the method. The authors studied the coefficients of correlation and reproducibility of these parameters in patients with coronary artery disease. In addition, they tried to determine which parameter was most often responsible for changing a diagnostic conclusion (i.e., presence or absence of late potentials). Two signal-averaged ECGs were recorded one after the other in 127 patients. The presence of late potentials was defined as the presence of a least two of the following criteria: total amplified and averaged QRS duration (tQRS) > 114 ms: duration of the last signal of under 40 microV (LAS) > 38 ms, and root mean square of the amplitude of the last 40 ms (RMS) < 20 microV. The correlation coefficients were 0.98, 0.96 and 0.94 for the duration of tQRS, LAS and RMS respectively (p < 0.0001). The coefficients of reproducibility were 7.0 ms. 7.0 ms and 16.1 microV respectively. Late potentials were present in 22% of patients. A change in diagnosis between the first and second recording was observed in 10 subjects (8% of the population). A combined change in LAS and RMS was responsible for 6 of these revised diagnoses, a change in LAS alone in 2 cases, of the RMS alone in 1 case and the tQRS alone in 1 case. In patients with coronary artery disease, the immediate reproducibility of the diagnosis of late potentials is affected by changes in LAS and RMS. The tQRS is only rarely responsible for a change in diagnosis. This study suggests that the result of the signal-averaged ECG should be interpreted with caution when the LAS or RMS are near their threshold values.

Action Potentials↗
PubMed1996

[Influence of the duration of recording in the reproducibility of the signal averaged electrocardiogram].

The authors studied the possibility of improving the reproducibility of the signal averaged ECG by increasing the number of averaged QRS complexes. One hundred patients were included in the study. In each cases, 400 QRS complexes were recorded on twice, consecutively, in strictly identical conditions. During each recording, the total duration of the amplified and averaged QRS complex (tQRS), the duration of the terminal signal below 40 microV (LAS) and the root mean square of the amplitude of the last 40 ms (RMS) were determined for 100, 200, 300 and 400 recorded QRS complexes. The presence of late potentials was defined as the positivity of two of the following criteria: tQRS > 114 ms, LAS > 38 ms, RMS < 20 microV. The number of contradictory diagnostic conclusions between two successive recordings of the same duration decreased progressively with the number of averaged QRS complexes: 10 for 100 QRS, 10 for 200 QRS, 9 for 300 QRS and 6 for 400 QRS complexes, but this improvement was not statistically significant. The absolute differences of tQRS and RMS between two successive recordings of the same duration were statistically different for the four durations of recording (p = 0.05) and there was a tendency towards statistical significance for LAS (p = 0.09). The best quantitative reproducibility of the 3 parameters was obtained with the recording of 300 QRS complexes. In conclusion, the reproducibility of the signal averaged ECG is improved when the number of average QRS complexes is increased. The authors' results suggests that reproducibility this is optimal with the amplification and averaging of 300 QRS complexes.

Action Potentials↗
PubMed1996

[Risk of mortality after myocardial infarction: value of heart rate, its variability and left ventricular ejection fraction].

Heart rate variability is a useful parameter for risk stratification after myocardial infarction. However, the relationship between heart rate itself and its variability has not been adequately studied. The authors compared the average RR interval of 24 hours recorded by Holter monitoring with the variability of heart rate and of left ventricular ejection fraction to assess the risk of death after myocardial infarction. A total of 579 patients was followed up for 2 years after acute myocardial infarction. During this period, there were 54 deaths, 42 of cardiac origin, 26 being classified as sudden death. The positive predictive value of left ventricular ejection fraction was lower than those of mean RR interval and the variability of heart rate for overall mortality, cardiac mortality and sudden death. The three indices were essentially equivalent for the prediction of non-sudden cardiac death. The positive predictive value of heart rate variability was better than the mean RR interval for sensitivities < 40%, for all cause mortality. However, for sensitivities > 40%, the two parameters were equivalent or slightly in favour of the mean heart rate over 24 hours. The authors conclude that the mean RR interval over 24 hours is an important prognostic index after myocardial infarction. This index is more powerful than left ventricular ejection fraction and comparable to heart rate variability.

Aged↗
PubMed1995

Day-to-day reproducibility of time-domain measures of heart rate variability in survivors of acute myocardial infarction.

We conclude that in clinically unchanged conditions, the time-domain HR variability measures derived from 24-hour ambulatory recordings of AMI survivors are stable during the early convalescent phase, and the day-to-day differences have only little effect on the result. The only exception appears to be the pNN50 parameter, the use of which should be favorably substituted by the rMSSD measurement. Geometric estimates of HR variability are highly and consistently correlated with statistical measures of overall HR variability, and may be used as substitutes for each other.

Adult↗
PubMed1995

Influence of the infarct site on the identification of patients with ventricular tachycardia after myocardial infarction based on the time-domain and spectral turbulence analysis of the signal-averaged electrocardiogram.

In a significant proportion of patients with sustained ventricular tachycardia (VT) following anterior myocardial infarction, the areas of slow conduction are activated early during cardiac depolarization. Therefore, they may not be detected by the standard time-domain analysis of the signal-averaged electrocardiogram (SAECG) which is limited to the terminal part of the QRS complex. Spectral turbulence analysis of the SAECG is a new frequency domain technique which examines the whole QRS complex and may improve identification of patients with sustained VT following anterior infarction. We compared the results of time-domain and spectral turbulence analyses of the SAECG in 53 postinfarction patients with sustained VT and in 53 age-, gender- and infarct site-matched patients without VT. The receiver operator characteristic curves have shown that the time-domain analysis resulted in better identification of patients with VT following inferior than following anterior infarction (e.g., at the sensitivity level of 90%, the corresponding values of specificity were 96 and 90%, respectively), whereas the spectral turbulence analysis performed better in the anterior site of infarction. When both time-domain and spectral turbulence analyses were combined, the accuracy of the SAECG for identification of patients with VT following anterior infarction improved, reaching a specificity of 97% at the sensitivity level of 90%. In conclusion (1) spectral turbulence analysis of the SAECG results in better identification of patients with VT following anterior than following inferior infarction, and (2) the combination of time-domain and spectral turbulence analyses of the SAECG may improve identification of patients with VT following anterior infarction.

Aged↗
PubMed1995

Numeric processing of Lorenz plots of R-R intervals from long-term ECGs. Comparison with time-domain measures of heart rate variability for risk stratification after myocardial infarction.

The so-called "Lorenz plots" are scatterplots that show the R-R interval as a function of the preceding R-R intervals. Repeatedly, it has been proposed that these plots might be used for visualizing the variability of the heart rate and that the assessment of heart rate variability (HRV) from these plots might be superior to conventional measures of HRV. However, a precise numeric evaluation of the images of Lorenz plots have never been suggested. To classify the images of Lorenz plots, a computer package that measures their density was developed. For each rectangular area of the plot, the relative number of R1/R2 samples in that area is established and a function is created that assigns the maximum relative number of samples (i.e., the maximum density) to each size of an area of the plot. Plots that are very compact result in a sharply falling density function, while plots that are more diffuse lead to a flat density function. The distinction between such types of density function may be expressed as a logarithmic integral of the density function to express the "compactness" of the plot numerically. As the computational demands of this approach are intensive, an approximate method that restricts the measurement of the density to the area around the peak of the plot was also developed. The results of this approximate method correlate strongly with the full results (r = .98), and approximate measurement of one plot requires less than 1 minute of computer time. The approximate method has been applied to a set of 24-hour Holter records obtained from 637 survivors of acute myocardial infarction. For each record, the SDNN and SDANN values were also calculated as conventional measures of HRV. Both the density of the Lorenz plots and the conventional measures of HRV were used to investigate the differences among 48 patients who suffered an arrhythmic event (sudden death or sustained symptomatic ventricular tachycardia) during a 2-year follow-up period and the remaining 589 patients without arrhythmic postinfarction complications. At a sensitivity of 30%, the Lorenz plot density distinguished the patients with events with a positive predictive accuracy of 58%, while the SDNN and SDANN led to a positive predictive accuracy of only 23 and 18%, respectively. Thus, a detailed analysis of Lorenz plots is feasible and more clinically useful than the conventional measures of HRV.

Adult↗
PubMed1994

Change of autonomic influence on the heart immediately before the onset of spontaneous idiopathic ventricular tachycardia.

OBJECTIVES: This study aimed to assess heart rate variability immediately before the onset of episodes of spontaneous ventricular tachycardia. BACKGROUND: It has been shown that decreased heart rate variability may be associated with a propensity to ventricular tachyarrhythmias. However, it is still disputed whether there is an abrupt change in heart rate variability immediately before the onset of these arrhythmias. METHODS: Twenty-three patients with idiopathic ventricular tachycardia underwent two-channel 24-h Holter monitoring in a drug-free state. Spectral heart rate variability was computed as low (0.04 to 0.15 Hz) and high (0.15 to 0.40 Hz) frequency components at 2-min intervals over a 1-h period immediately before the onset of ventricular tachycardia. Average values of heart rate variability were also computed for the entire 24-h recordings. The low/high frequency component ratio was calculated as an index of the autonomic balance of the heart. RESULTS: Seventy-one episodes of ventricular tachycardia from the 23 recordings formed this study. There was an increased low/high ratio during 6- to 8-min periods immediately before the onset of ventricular tachycardia episodes compared with the average values for the entire 24 h. This increase in the low/high ratio resulted largely from a decrease in the high frequency component value (4.70 +/- 1.15 vs. 5.10 +/- 1.06 ln[ms2] [mean +/- SD], p = 0.001) because there was no significant change in the low frequency component value (6.37 +/- 1.20 vs. 6.34 +/- 0.91 ln[ms2], p = 0.786, 95% confidence interval -0.25 to 0.19 ln[ms2], type II error < 0.0001 for change of 7.8%). In contrast, there were no significant differences in the low or high frequency components or low/high ratio between 6-min salvo-free periods 40 min before the onset of ventricular tachycardia and the average 24-h values (type II error < 0.0001, < 0.038 and < 0.1841, respectively, for change of 7.8%). The low/high ratio was also significantly higher during the 6 min immediately before the onset of ventricular tachycardia compared with that during the 6-min salvo-free periods 40 min before the onset of ventricular tachycardia. A significant increase in mean heart rate immediately before the onset of ventricular tachycardia was also noted. CONCLUSIONS: There is a significant change in autonomic influence on the heart during the last few minutes preceding the onset of episodes of idiopathic ventricular tachycardia. This seems to result mainly from decreased vagal activity rather than enhanced sympathetic input to the heart.

Adolescent↗
PubMed1994

Computation of multifactorial receiver operator and predictive accuracy characteristics.

The computation of the so-called receiver operator characteristics (i.e. functions that assign the maximum specificity to each value of sensitivity) is simple when the characteristics are based on univariate data. On the contrary, multivariate characteristics are difficult to compute, as the complexity of their calculation increases exponentially with the dimension of the data. This paper describes an algorithm for computation of multivariate receiver operator characteristics and derived functions (namely positive and negative predictive characteristics). The algorithm is based on several concepts that increase its computational efficiency. The most important of them is a pre-sorting of the data in each dimension and the division of each dimension into groups in which the positive and negative cases are 100% stratified. The paper also presents a risk stratification study that utilised this algorithm. The study was aimed at identifying those survivors of acute myocardial infarction who are at risk of early death. A cohort of 539 patients was stratified based on time-domain (three variables) and spectral turbulence (six variables) indices of signal-averaged electrocardiogram. The computing times of the algorithm in this study are presented in the text, and the efficiency of the computation is discussed in detail.

Algorithms↗
PubMed1994

Spectral turbulence analysis versus time-domain analysis of the signal-averaged ECG in survivors of acute myocardial infarction.

This study compared the time-domain and spectral turbulence analyses of signal-averaged electrocardiogram (ECG) for the prediction of risk after acute myocardial infarction. Signal-averaged ECGs were recorded in 553 survivors of acute myocardial infarction before hospital discharge. The study excluded cases with bundle branch block and other conduction abnormalities, and patients were followed for at least 1 year. During the first year of the follow-up period, 30 patients died and 20 presented with ventricular tachycardia/fibrillation. The signal-averaged ECG recordings were analyzed using conventional time domain at 40-250 Hz and spectral turbulence analyses. The indices provided by both types of analysis were compared in patients with and without endpoints. The optimum positive predictive characteristics were calculated for the prediction of all cause mortality and of ventricular tachycardia based on the time domain and on the spectral turbulence indices. Spectral turbulence analysis provided significantly lower positive predictive accuracy (14.5% at 40% sensitivity) than the time-domain analysis (26.7% at 40% sensitivity) for prediction of ventricular tachycardia/fibrillation during 1 year after infarction (P < .01). However, spectral turbulence analysis provided significantly higher positive predictive accuracy (27.2% at 30% sensitivity) than the time-domain analysis (16.9% at 30% sensitivity) for the prediction of 1-year all-cause mortality (P < .01). Thus, spectral turbulence analysis was inferior to the time-domain analysis in predicting ventricular tachycardia/fibrillation during the first year after myocardial infarction, but it was more powerful in predicting 1-year mortality.

Aged↗