Assessment of ventricular repolarization variability with the DeltaT50 method improves identification of patients with congenital long QT syndromes.

BACKGROUND: We analyzed ventricular repolarization variability in genotyped long QT syndrome (LQTS) patients and in healthy volunteers (HV). METHOD: The deltaT50, that is, the temporal variability of ventricular repolarization at 50% of the T-wave downslope, was analyzed every 15th minute on 175 and 390 Holter electrocardiogram (ECG) recordings from HV and genotyped LQTS patients, respectively. The average deltaT50 and QTcF were calculated in each subject. RESULTS: DeltaT50 was 2.26 ± 0.71 ms (mean ± SD) in the HV and 5.74 ± 2.30 ms in the LQTS population (P < 0.0001). The sensitivity and specificity of QTcF (cutoff value 450 ms) to discriminate between the LQTS patients and the HV were 51.5% and 98.9%, and for deltaT50 (cutoff value 3 ms) 93.9% and 88.6%, respectively. The combination of both variables improved the diagnosis of the LQTS patients even further. Subgroups of LQTS patients at higher risk of cardiac events (with LQTS3, JLN, QTc > 500 ms or symptoms) had higher deltaT50 than subgroups at lower risk (with LQTS1, QTc < 450 ms or without symptoms). The variation in deltaT50 between day and night was concordant with the risk of symptoms; patients with LQTS1 had higher deltaT50 in the daytime and patients with LQTS3 had higher deltaT50 during the night. CONCLUSION: DeltaT50 more accurately distinguished between LQTS patients and HV than QTcF and was higher in LQTS patients with a higher risk of cardiac events. DeltaT50 can be used together with QTcF to improve the diagnosis in patients with the LQTS phenotype and tentatively also be of value for risk assessment in such patients.

DeltaT50--a new method to assess temporal ventricular repolarization variability.

BACKGROUND: Increased beat-to-beat variability in cardiac repolarization time is a tentative risk marker of drug-induced torsades de pointes. We developed a new, automatic method based on the temporal variability of the T-wave down slope to assess this variability. METHOD AND RESULTS: Leads V(1) to V(6) of resting electrocardiograms were recorded in 42 healthy subjects (18-68 years, 22 men). The temporal variability at 50% of the T-wave down slope, deltaT50 (1.5 ± 0.41 milliseconds; range, 0.86-2.66 milliseconds), was measured with an accuracy of 1 millisecond on at least 9 pairs of electrocardiogram complexes with a signal-to-noise ratio more than 10 and changes in the R-R interval less than 150 milliseconds. The correlation between repeated measurements of deltaT50 was high. DeltaT50 was measured without corrections for age, sex, heart rate, T-wave amplitude, signal-to-noise ratio, R-R variability, and QTcF because none of these factors explained more than 4% of the within-subject deltaT50 variability. CONCLUSION: The beat-to-beat repolarization variability was measured with high fidelity with the deltaT50 method and was a robust measure in healthy volunteers.

PC-Based ECG waveform recognition-validation of novel software against a reference ECG database.

BACKGROUND: PC-based ECG measurements must cope with normal as well as pathological ECGs in a reliable manner. EClysis, a software for ECG measurements was tested against reference values from the Common Standards for Quantitative Electrocardiography (CSE) database. METHODS: Digital ECGs (12 leads, 500 Hz) were recorded by the CSE project. Data Set 3 contains reference values for 125 ECGs (33 normal and 92 pathological). Median values of measurements by 11 computer programs and by five cardiologists, respectively, refer to the earliest P and QRS onsets and to the latest P, QRS, and T offsets in any lead of a selected (index) beat. EClysis automatically measured all ECGs, without user interference. RESULTS: The PQRST points were correctly detected but in two ECGs with AV block II-III. The software was not designed to detect atrial activity in atrial fibrillation (n = 9) and flutter (n = 1). In one case of atrial fibrillation, atrial activity interfered with positioning of QRS and T offsets. Regression coefficients between EClysis and CSE (software-generated and human) were above 0.95 (P < 0.0001). The confidence intervals were 95% for the slope and the intercept of the regression lines. CONCLUSIONS: The PC-based detection and analysis of PQRST points showed a high level of agreement with the CSE database reference values.

Computer-based analysis of dynamic QT changes: toward high precision and individual rate correction.

BACKGROUND: New strategies are needed to improve the results of automatic measurement of the various parts of the ECG signal and their dynamic changes. METHODS: The EClysis software processes digitally-recorded ECGs from up to 12 leads at 500 Hz, using strictly defined algorithms to detect the PQRSTU points and to measure ECG intervals and amplitudes. Calculations are made on the averaged curve of each sampling period (beat group) or as means +/- SD for beat groups, after being analyzed at the individual beat level in each lead. Resulting data sets can be exported for further statistical analyses. Using QT and R-R measured on beat level, an individual correction for the R-R dependence can be performed. RESULTS: EClysis assigns PQRSTU points and intervals in a sensitive and highly reproducible manner, with coefficients of variation in ECG intervals corresponding to ca. 2 ms in the simulated ECG. In the normal ECG, the CVs are 2% for QRS, 0.8% for QT, and almost 6% for PQ intervals. EClysis highlights the increase in QT intervals and the decrease of T-wave amplitudes during almokalant infusion versus placebo. Using the observed linear or exponential relationships to adjust QT for R-R dependence in healthy subjects, one can eliminate this dependence almost completely by individualized correction. CONCLUSIONS: The EClysis system provides a precise and reproducible method to analyze ECGs.