Katerina Hnatkova

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

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

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.

Spatial, temporal and wavefront direction characteristics of 12-lead T-wave morphology

Three new approaches for the analysis of ventricular repolarisation in 12-lead electrocardiograms (ECGs) are presented: the spatial and temporal variations in T-wave morphology and the wavefront direction difference between the ventricular depolarisation and repolarisation waves. The spatial variation characterises the morphology differences between standard leads. The temporal variation measures the change in interlead relationships. A minimum dimensional space, constructed by ECG singular value decomposition, is used. All descriptors are measured using the ECG vector in the constructed space and the singular vectors that define this space. None of the descriptors requires time domain measurements (e.g. the precise detection of the T-wave offset), and so the inaccuracies associated with conventional QT interval related parameters are avoided. The new descriptors are compared with the conventional measurements provided by a commercial system for an automatic evaluation of QT interval and QT dispersion in digitally recorded 12-lead ECGs. The basic comparison uses a set of 1100 normal ECGs. The short-term intrasubject reproducibility of the new descriptors is compared with that of the conventional measurements in a set of 760 ECGs recorded in 76 normal subjects and a set of 630 ECGs recorded in 63 patients with hypertrophic cardiomyopathy (ten serial recordings in each subject of both these sets). The discriminative power of the new and conventional parameters to distinguish normal and abnormal repolarisation patterns is compared using the same set. The results show that the new parameters do not correlate with the conventional QT interval-related descriptors (i.e. they assess different ECG qualities), are generally more reproducible than the conventional parameters, and lead to a more significant separation between normal and abnormal ECGs, both univariately and in multivariate regression models.

Agreement and Reproducibility of Automatic Versus Manual Measurement of QT Interval and QT Dispersion

To determine whether the automatic measurement of the QT interval is consistent with the manual measurement, this study evaluated the reproducibility and agreement of both methods in 70 normal subjects and 54 patients with hypertrophic cardiomyopathy. The mean, minimum, and maximum QT interval and QT dispersion were computed in a set of 6 consecutive electrocardiograms (3 in the supine and 3 in the standing position) obtained from each subject. The automatic method determined the T-wave end as the intersect of the least-squares-fit line around the tangent to the T-wave downslope with the isoelectric baseline. Manual measurements were obtained using a high-resolution digitizing board. QT dispersion was defined as the difference between the maximum and minimum QT interval and as standard deviations of the QT interval duration in all and precordial leads. In patients with hypertrophic cardiomyopathy, the absolute values of the QT interval and QT dispersion were significantly higher than those in normal subjects (p < 0.0001). In both groups, the intrasubject variability of the QT interval was significantly lower with automatic than with manual measurement (p < 0.05). The agreement between automatic and manual QT interval measurements was surprisingly poor, but it was better in patients with hypertrophic cardiomyopathy (r2 = 0.46 to 0.67) than in normal subjects (r2 = 0.10 to 0.25). In both groups, the reproducibility and agreement of both methods for QT dispersion were significantly poorer than for QT interval. Hence, the automatic QT interval measurements are more stable and reproducible than manual measurement, but the lack of agreement between manual and automatic measurement suggests that clinical experience gained with manual assessment cannot be applied blindly to data obtained from the automatic systems.

QT dispersion does not represent electrocardiographic interlead heterogeneity of ventricular repolarization.

QT Dispersion and Repolarization Heterogeneity. Introduction: QT dispersion (QTd, range of QT intervals in 12 ECG leads) is thought to reflect spatial heterogeneity of ventricular refractoriness. However, QTd may be largely due to projections of the repolarization dipole rather than “nondipolar” signals.

Improved Stratification of Autonomic Regulation for risk prediction in post-infarction patients with preserved left ventricular function (ISAR-Risk)

Aims To investigate the combination of heart rate turbulence (HRT) and deceleration capacity (DC) as risk predictors in post-infarction patients with left ventricular ejection fraction (LVEF) > 30%. Methods and results We enrolled 2343 consecutive survivors of acute myocardial infarction (MI) (<76 years) in sinus rhythm. HRT and DC were obtained from 24 h Holter recordings. Patients with both abnormal HRT (slope ≤ 2.5 ms/RR and onset ≥ 0%) and abnormal DC (≤4.5 ms) were considered suffering from severe autonomic failure (SAF) and prospectively classified as high risk. Primary and secondary endpoints were all-cause, cardiac, and sudden cardiac mortality within the first 5 years of follow-up. During follow-up, 181 patients died; 39 deaths occurred in 120 patients with LVEF ≤ 30%, and 142 in 2223 patients with LVEF>30% (cumulative 5-year mortality rates of 37.9% and 7.8%, respectively). Among patients with LVEF > 30%, SAF identified another high-risk group of 117 patients with 37 deaths (cumulative 5-year mortality rates of 38.6% and 6.1%, respectively). Merging both high-risk groups (i.e. LVEF ≤ 30% and/or SAF) doubled the sensitivity of mortality prediction compared with LVEF ≤ 30% alone (21.1% vs. 42.1%, P < 0.001) while preserving 5-year mortality rate (38.2%). Conclusion In post-MI patients with LVEF>30%, SAF identifies a high-risk group equivalent in size and mortality risk to patients with LVEF ≤ 30%.

Multiparametric Analysis of Heart Rate Variability Used for Risk Stratification Among Survivors of Acute Myocardial Infarction

A multiparametric heart rate variability analysis was performed to prove if combined heart rate variability (HRV) measures of different domains improve the result of risk stratification in patients after myocardial infarction. In this study, standard time domain, frequency domain and non‐linear dynamics measures of HRV assessment were applied to 572 survivors of acute myocardial infarction. Three parameter sets each consisting of 4 parameters were applied and compared with the standard measurement of global heart rate variability HRVi. Discriminant analysis technique and t‐test were performed to separate the high risk groups from the survivors. The predictive value of this approach was evaluated with receiver operator (ROC) and positive predictive accuracy (PPA) curves. Results ‐ The discriminant analysis shows a separation of patients suffered by all cause mortality in 80% (best single parameter 74%) and sudden arrhythmic death in 86% (73%). All parameters of set I show a high significant difference (p<0.001) between survivors and non‐survivors based on two‐tailed t‐test. The specificity level of the multivariate parameter sets is at the 70% sensitivity level (ROC) about 85–90%, whereas HRVi shows maximum levels of 70%. The PPA in the all cause mortality group is at the 70% sensitivity level twice as high as the univarihate HRV measure and increases to more than fourfold as high within the VT/VF group. In conclusion, in this population, the multiparametric approach with the combination of four parameters from all domains especially from NLD seems to be a better predictor of high arrhythmia risk than the standard measurement of global heart rate variability.

Subject-specific profiles of QT/RR hysteresis.

The time lag of the QT interval adaptation to heart rate changes (QT/RR hysteresis) was studied in 40 healthy subjects (18 females; mean age, 30.4+/-8.1 yr) with 3 separate daytime (>13 h) 12-lead electrocardiograms (ECG) in each subject. In each recording, 330 individual 10-s ECG segments were measured, including 100 segments preceded by 2 min of heart rate varying greater than +/-2 beats/min. Other segments were preceded by a stable heart rate. In segments preceded by variable rate, QT/RR hysteresis was characterized by lambda parameters of the exponential decay models. The intrasubject SDs of lambda values were compared with the intersubject SD of the individual means. The lambda values were also correlated to individually optimized parameters of heart rate correction. Intrasubject SDs of lambda were substantially smaller than the population SD of individual means (0.390+/-0.197 vs. 0.711, P<0.0001). The lambda values were unrelated to the QT/RR correction parameters. When compared with the corrected QT (QTc) for averaged RR intervals in 10-s ECGs and with the averaged RR intervals in 2-min history, QTc for QT/RR hysteresis led to a substantially smaller SD of QTc values (11.4+/-2.00, 6.33+/-1.31, and 4.66+/-0.85 ms, respectively, P<0.0001). Thus the speed with which the QT interval adapts to heart rate changes is highly individual with intrasubject stability and intersubject variability. QT/RR hysteresis is independent of the static QT/RR relationship and should be considered as a separate physiological process. The combination of individual heart rate correction with individual hysteresis correction of the QT interval is likely to lead to substantial improvements of cardiac repolarization studies.

Assessment of repolarization heterogeneity for prediction of mortality in cardiovascular disease: peak to the end of the T wave interval and nondipolar repolarization components.

BACKGROUND In the canine wedge preparation, the interval from the peak to the end of the T wave (TpTe) reflects transwedge heterogeneities. Increase of ventricular dispersion of action potential durations has been repeatedly shown to be arrhythmogenic; thus, prolonged TpTe intervals were assumed to reflect increased risk. However, despite attempted extrapolation to clinical electrocardiograms, the appropriateness of this assumption has not been investigated in a large population. In another animal model, nondipolar components of the descending T-wave limb (TWRd) have been shown to correlate with TpTe interval. Although total T-wave nondipolar components (TWRt), believed to reflect heterogeneities during total repolarization, were shown associated with worse outcome of cardiac patients, this has not been investigated for TWRd. METHODS AND RESULTS Male cardiovascular patients (n = 813) had digital 12-lead electrocardiograms recorded between 1984 and 1991 and were followed until 2000. Using commercial and previously validated technology, QT intervals, TpTe intervals, TWRd, and TWRt were calculated, heart rate corrected, and compared between survivors and nonsurvivors. Their predictive power was also compared with established markers of mortality risk. In contrast to former reports, TpTe(c) intervals were significantly shorter in nonsurvivors (98.76 ± 20.63 milliseconds vs 103.14 ± 20.87 milliseconds, P = .016) and not predictive of outcome. Although TWRd(c) was significantly higher in nonsurvivors (0.007% ± 0.02% vs 0.005% ± 0.08%, P = .03), it was also not predictive of outcome. Only increased TWRt(c), increased heart rate, and increased age were predictive of death. CONCLUSIONS The findings challenge the concept that prolongation of TpTe corresponds to higher risk of death from any cause in every population. Further investigations are needed to confirm that clinically measured TpTe reflects transmural repolarization heterogeneity in all clinical populations and indeed is a useful risk marker.

Optimum formulae for heart rate correction of the QT interval.

The study investigated the performance of several generic QT/RR regression models in a dataset of QT and RR intervals obtained from resting electrocardiograms of 1,100 healthy subjects (913 male, mean age 33 ± 12 years). All the investigated models have three degrees of freedom and included the hyperparabolic and hyper‐hyperbolic models, algorithmic models, negative exponential models, and models involving inverse tangent, hyperbolic tangent, and inverse hyperbolic sign functions. For each generic model, the combination of parameters leading to the lowest regression residuum was found. The results of the study show that the goodness of the optimum fit is practically independent of the generic form of the regression model and that different datasets lead to different combinations of the numerical values of parameters of the corresponding regression models. The study concludes that the search for a universally applicable QT/RR regression model that would provide the best fit in all circumstances is most likely fruitless. Rather, individual studies such as those investigating drug related QT prolongation might benefit from establishing a best‐fit regression that would provide the optimum model for each particular dataset.