Abnormal dynamics of ventricular repolarization--a new insight into the mechanisms of life-threatening ventricular arrhythmias

Strategies for the prevention and treatment of sudden cardiac death

Cardiovascular diseases account for 40% of all deaths in the West. Sudden cardiac death (SCD) is a major health problem affecting over 300,000 patients annually in the United States alone. Presence of coronary artery disease (CAD), usually in the setting of diminished left ventricular ejection fraction, is still the single major risk factor for SCD. Additionally, acute myocardial ischemia, structural cardiac defects, anomalous coronary arteries, cardiomyopathies, genetic mutations, and ventricular arrhythmias are all attributed to SCD, demonstrating the perplexity of this condition. With the recent advancements in cardiovascular medicine, the incidence of SCD is expected to increase steeply as the prevalence of CAD and heart failure is uprising in general population. Considering SCD, the major challenge confronting contemporary cardiology, multiple strategies for prevention against SCD have been developed. β-blockers have been shown to reduce the risk of SCD, whereas implantable cardioverter-defibrillator devices are found to be effective at terminating the malignant arrhythmias. In recent years, multiple clinical trials were carried out to identify patients who may benefit from preventive intervention, including medical therapy and automatic cardioverter-defibrillator implantations. This review article provides insight into the advanced strategies for the prevention and treatment of SCD based on the data available in medical literature to date.

Analysis of 12-lead T-wave morphology for risk stratification after myocardial infarction

BACKGROUND

The stratification of post-myocardial infarction (MI) patients at risk of sudden cardiac death remains important. The aim of the present study was to assess the prognostic value of novel T-wave morphology descriptors derived from resting 12-lead ECGs.

METHODS AND RESULTS

In 280 consecutive post-MI patients, a 12-lead ECG was recorded before discharge, optically scanned, and digitized. For the present study, 5 T-wave morphology descriptors were automatically calculated after singular value decomposition of the ECG signal. The total cosine R-to-T (TCRT [describes the global angle between repolarization and depolarization wavefront]) and the T-wave loop dispersion were univariately associated (P:=0.0002 and P:<0.002, respectively, U: test) with 27 prospectively defined clinical events in 261 patients (mean follow-up 32+/-10 months). Kaplan-Meier event probability curves for strata above and below the median confirmed the strong risk discrimination by TCRT and T-wave loop dispersion (P:<0.003 and P:<0.001, respectively, log-rank test). On Cox regression analysis, with the entering of age, left ventricular ejection fraction, heart rate, QRS width, reperfusion therapy, beta-adrenergic-blocker treatment, and standard deviation of R-R intervals on 24-hour Holter monitoring, TCRT (P:<0.03) yielded independent predictive value, whereas T-wave loop dispersion was of borderline independence (P:=0.064). Heart rate (P:<0.02), left ventricular ejection fraction (P:<0.02), and reperfusion therapy (P:<0.02) also remained in the final model.

CONCLUSIONS

Computerized T-wave morphology analysis of the 12-lead resting ECG permits independent assessment of post-MI risk and an improved risk stratification when combined with other risk markers.

Using simulated noise to define optimal QT intervals for computer analysis of ambulatory ECG

The ambulatory electrocardiogram (ECG) is an important medical tool, not only for diagnosis of adverse cardiac events, but also to predict the risk of such events occurring. The 24-hour ambulatory ECG has certain problems and drawbacks because the signal is corrupted by noise from various sources and also several other conditions which may alter the ECG morphology. We have developed a Windows based program for the computer analysis of ambulatory ECG which attempts to address these problems. The software includes options for importing ECG data, different methods of waveform analysis, data-viewing, and exporting the extracted time series. In addition, the modular structure allows for flexible maintenance and expansion of the software. The ECG was recorded using a Holter device and oversampled to enhance the fidelity of the low sampling rate of the ambulatory ECG. The influence of different sampling rates on the interval variability were studied. The noise sensitivity of the implemented algorithm was tested with several types of simulated noise and the precision of the interval measurement was reported with SD values. Our simulations showed that, in most of the cases, defining the end of QT interval at the maximum of the T wave gave the most precise measurement. The definition of the onset of the ventricular repolarization duration is most precisely made on the maximum or descending maximal slope of the R wave. We also analyzed some examples of time series from patients using power spectrum estimates in order to validate the low level QT interval variability.