Henk J. Ritsema van Eck

Local depolarization abnormalities are the dominant pathophysiologic mechanism for type 1 electrocardiogram in brugada syndrome a study of electrocardiograms, vectorcardiograms, and body surface potential maps during ajmaline provocation.

OBJECTIVESnWe sought to obtain new insights into the pathophysiologic basis of Brugada syndrome (BrS) by studying changes in various electrocardiographic depolarization and/or repolarization variables that occurred with the development of the signature type 1 BrS electrocardiogram (ECG) during ajmaline provocation testing.nnnBACKGROUNDnBrS is associated with sudden cardiac death. Its pathophysiologic basis, although unresolved, is believed to reside in abnormal cardiac depolarization or abnormal repolarization.nnnMETHODSnAjmaline provocation was performed in 269 patients suspected of having BrS with simultaneous recording of ECGs, vectorcardiograms, and 62-lead body surface potential maps.nnnRESULTSnA type 1 ECG was elicited in 91 patients (BrS patients), 162 patients had a negative test result (controls), and 16 patients had an abnormal test result. Depolarization abnormalities were more prominent in BrS patients and were mapped to the right ventricle (RV) by longer right precordial filtered QRS complex durations (142 +/- 23 ms vs. 125 +/- 14 ms, p < 0.01) and right terminal conduction delay (60 +/- 11 ms vs. 53 +/- 9 ms, p < 0.01). Repolarization abnormalities remained concordant with depolarization abnormalities as indicated by steady low nondipolar content (12 +/- 8% vs. 8 +/- 4%, p = NS), lower spatial QRS-T integrals (33 +/- 12 mV.ms vs. 40 +/- 16 mV.ms, p < 0.05), similar spatial QRS-T angles (92 +/- 39 degrees vs. 87 +/- 31 degrees , p = NS), similar T(peak)-T(end) interval (143 +/- 36 ms vs. 138 +/- 25 ms, p = NS), and similar T(peak)-T(end) dispersion (47 +/- 37 ms vs. 45 +/- 27 ms, p = NS).nnnCONCLUSIONSnThe type 1 BrS ECG is characterized predominantly by localized depolarization abnormalities, notably (terminal) conduction delay in the RV, as assessed with complementary noninvasive electrocardiographic techniques. We could not define a separate role for repolarization abnormalities but suggest that the typical signs of repolarization derangements seen on the ECG are secondary to these depolarization abnormalities.

The meaning of the Tp-Te interval and its diagnostic value

BACKGROUNDnThe interval between T peak (Tp) and T end (Te) has been proposed as a measure of transmural dispersion of repolarization, but experimental and clinical studies to validate Tp-Te have given conflicting results. We have investigated the meaning of Tp-Te and its diagnostic potential.nnnMETHODSnWe used a digital model of the left ventricular wall to simulate the effect of varying action potential durations on the timing of Tp and Te. Furthermore, we used the vectorcardiogram to explain the relationships between Tp locations in the precordial electrocardiogram leads.nnnRESULTSnProlongation or ischemic shortening of action potentials in our model did not result in substantial Tp shifts. The phase relationships revealed by the vectorcardiogram showed that Tp-Te in the precordial leads is a derivative of T loop morphology.nnnCONCLUSIONnTp-Te is the resultant of the global distribution of the repolarization process and is a surrogate diagnostic parameter.

Hypertensive Stress Increases Dispersion of Repolarization

Several electrocardiographic indices for repolarization heterogeneity have been proposed previously. The behavior of these indices under two different stressors at the same heart rate (i.e., normotensive gravitational stress, and hypertensive isometric stress) was studied. ECG and blood pressure were recorded in 56 healthy men during rest (sitting with horizontal legs), hypertensive stress (performing handgrip), and normotensive stress (sitting with lowered legs). During both stressors, heart rates differed <10% in 41 subjects, who constituted the final study group. Heart rate increased from 63 ± 9 beats/min at rest to 71 ± 11 beats/min during normotensive, and to 71 ± 10 beats/min during hypertensive stress (P < 0.001). Systolic blood pressure was 122 ± 15 mmHg at rest and 121 ± 15 mmHg during normotensive stress, and increased to 151 ± 17 mmHg during hypertensive stress (P < 0.001). The QT interval was larger during hypertensive (405 ± 27) than during normotensive stress (389 ± 26, P < 0.001). QT dispersion did not differ significantly between the two stressors. The mean interval between the apex and the end of the T wave (Tapex‐Tend) of the mid‐precordial leads was larger during hypertensive (121 ± 17 ms) than during normotensive stress (116 ± 15 ms, P < 0.001). The singular value decomposition T wave index was larger during hypertensive (0.144 ± 0.071) than during normotensive stress (0.089 ± 0.053, P < 0.001). Most indices of repolarization heterogeneity were larger during hypertensive stress than during normotensive stress. Hypertensive stressors are associated with arrhythmogeneity in vulnerable hearts. This may in part be explained by the induction of repolarization heterogeneity by hypertensive stress.

Validation of automatic measurement of QT interval variability

Background Increased variability of beat-to-beat QT-interval durations on the electrocardiogram (ECG) has been associated with increased risk for fatal and non-fatal cardiac events. However, techniques for the measurement of QT variability (QTV) have not been validated since a gold standard is not available. In this study, we propose a validation method and illustrate its use for the validation of two automatic QTV measurement techniques. Methods Our method generates artificial standard 12-lead ECGs based on the averaged P-QRS-T complexes from a variety of existing ECG signals, with simulated intrinsic (QT interval) and extrinsic (noise, baseline wander, signal length) variations. We quantified QTV by a commonly used measure, short-term QT variability (STV). Using 28,800 simulated ECGs, we assessed the performance of a conventional QTV measurement algorithm, resembling a manual QTV measurement approach, and a more advanced algorithm based on fiducial segment averaging (FSA). Results The results for the conventional algorithm show considerable median absolute differences between the simulated and estimated STV. For the highest noise level, median differences were 4–6 ms in the absence of QTV. Increasing signal length generally yields more accurate STV estimates, but the difference in performance between 30 or 60 beats is small. The FSA algorithm proved to be very accurate, with most median absolute differences less than 0.5 ms, even for the highest levels of disturbance. Conclusions Artificially constructed ECGs with a variety of disturbances allow validation of QTV measurement procedures. The FSA algorithm provides highly accurate STV estimates under varying signal conditions, and performs much better than traditional beat-by-beat analysis. The fully automatic operation of the FSA algorithm enables STV measurement in large sets of ECGs.

Circadian pattern of short-term variability of the QT-interval in primary prevention ICD patients - EU-CERT-ICD methodological pilot study

Objective Short-term variability of the QT-interval (STV-QT) was shown to be associated with an increased risk of ventricular arrhythmias. We aimed at investigating (a) whether STV-QT exhibits circadian pattern, and (b) whether such pattern differs between patients with high and low arrhythmia risk. Methods As part of the ongoing EU-CERT-ICD study, 24h high resolution digital ambulatory 12-lead Holter recordings are collected prior to ICD implantation for primary prophylactic indication. Presently available patients were categorized based on their arrhythmia score (AS), a custom-made weighted score of the number of arrhythmic events on the recording. STV-QT was calculated every hour in 30 patients of which 15 and 15 patients had a high and a low AS, respectively. Results The overall dynamicity of STV-QT showed high intra- and inter-individual variability with different circadian patterns associated with low and high AS. High AS patients showed a prominent peak both at 08:00 and 18:00. At these times, STV-QT was significantly higher in the high AS patients compared to the low AS patients (1.22ms±0.55ms vs 0.60ms±0.24ms at 08:00 and 1.12ms±0.39ms vs 0.64ms±0.29ms at 18:00, both p < 0.01). Conclusion In patients with high AS, STV-QT peaks in the early morning and late afternoon. This potentially reflects increased arrhythmia risk at these times. Prospective STV-QT determination at these times might thus be more sensitive to identify patients at high risk of ventricular arrhythmias.