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QT Measurements (qt + measurement)

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Medical Sciences	100%

Selected Abstracts

An Update on QT Measurement and Interpretation Methodologies

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 2009
Jean-Philippe Couderc Ph.D.
No abstract is available for this article. [source]

Automated QT Measurement and Application to Detection of Moxifloxacin-Induced Changes

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 2009
David W. Mortara Ph.D.
Background: Concern for drug-induced QT prolongation has caused significant investment in QT measurement to safety-test new compounds. Manual methods are expensive and time-consuming. Reliable automatic methods would be highly desirable. Methods: Twelve-lead Holter recordings were annotated beat-to-beat by an automatic algorithm for global QRS onset and T offset. T offset was established from the time of peak T downslope plus a rate-dependent offset, analogous to the "tangent method," wherein T offset is determined by extrapolating the T downslope to an intersection with the baseline. Results and Conclusions: Variances of the beat-to-beat QT measurements were in the range 2.5,3.4 ms over three distinct databases, including a large heart failure database. Application to a moxifloxacin/placebo control database of 29 subjects showed excellent results. [source]

Philips QT Interval Measurement Algorithms for Diagnostic, Ambulatory, and Patient Monitoring ECG Applications

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 2009
F.A.C.C., Sophia H. Zhou Ph.D.
Background: Commonly used techniques for QT measurement that identify T wave end using amplitude thresholds or the tangent method are sensitive to baseline drift and to variations of terminal T wave shape. Such QT measurement techniques commonly underestimate or overestimate the "true" QT interval. Methods: To find the end of the T wave, the new Philips QT interval measurement algorithms use the distance from an ancillary line drawn from the peak of the T wave to a point beyond the expected inflection point at the end of the T wave. We have adapted and optimized modifications of this basic approach for use in three different ECG application areas: resting diagnostic, ambulatory Holter, and in-hospital patient monitoring. The Philips DXL resting diagnostic algorithm uses an alpha-trimming technique and a measure of central tendency to determine the median QT value of eight most reliable leads. In ambulatory Holter ECG analysis, generally only two or three channels are available. QT is measured on a root-mean-square vector magnitude signal. Finally, QT measurement in the real time in-hospital application is among the most challenging areas of QT measurement. The Philips real time QT interval measurement algorithm employs features from both Philips DXL 12-lead and ambulatory Holter QT algorithms with further enhancements. Results: The diagnostic 12-lead algorithm has been tested against the gold standard measurement database established by the CSE group with results surpassing the industrial ECG measurement accuracy standards. Holter and monitoring algorithm performance data on the PhysioNet QT database were shown to be similar to the manual measurements by two cardiologists. Conclusion: The three variations of the QT measurement algorithm we developed are suitable for diagnostic 12-lead, Holter, and patient monitoring applications. [source]

Automated QT Measurement and Application to Detection of Moxifloxacin-Induced Changes

Robust QT Interval Estimation,From Algorithm to Validation

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 2009
Joel Q. Xue Ph.D.
Background: This article presents an effort of measuring QT interval with automatic computerized algorithms. The aims of the algorithms are consistency as well as accuracy. Multilead and multibeat information from a given segment of ECG are used for more consistent QT interval measurement. Methods: A representative beat is generated from selected segment of each lead, and then a composite beat is formed by the representative beats of all independent leads. The end result of the QT measure is so-called global QT measurement, which usually correlates with the longest QT interval in multiple leads. Individual lead QT interval was estimated by using the global measurement as a starting point, and then adapted to the signal of the particular lead and beat. In general, beat-by-beat QT measurement is more prone to noise, therefore less reliable than the global estimation. It is usually difficult to know if difference of beat-by-beat QT interval is due to true physiological change or noise fluctuation. Results: The algorithm was tested independently by a clinical database. It is also tested against action potential duration (APD) generated by a Cell-to-ECG forward-modeling based simulation signals. The modeling approach provided an objective test for the QT estimation. The modeling approach allowed us to evaluate the QT measurement versus APD. The mean error between the algorithm and cardiologist QT intervals is 3.95 ± 5.5 ms, based on the large clinical trial database consisting of 15,910 ECGs. The mean error between QT intervals and maximum APD is 17 ± 2.4, and the correlation coefficient is 0.99. Conclusions: The global QT interval measurement method presented in this study shows very satisfactory results against the CSE database and a large clinical trial database. The modeling test approach used in this study provides an alternative "gold standard" for QT interval measurement. [source]

Methodology of QT-Interval Measurement in the Modular ECG Analysis System (MEANS)

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 2009
Jan A. Kors Ph.D.
Background: QT prolongation as can be induced by drugs, signals the risk of life-threatening arrhythmias. The methodology of QT measurement in the modular ECG analysis system (MEANS) is described. Methods: In the simultaneously recorded leads of the standard 12-lead electrocardiogram (ECG), the QRS complexes are detected by a spatial velocity function. They are typed as dominant or nondominant, and a representative complex per lead is obtained by averaging over the dominant complexes. QRS onset and T end are determined by a template technique, and QT is measured. MEANS performance was evaluated on the 125 ECGs of the common standards for quantitative electrocardiography (CSE) multilead database, of which the waveform boundaries have been released. Results: MEANS detected correctly all 1445 complexes of the CSE library, with one false-positive detection due to a sudden baseline jump. All dominant complexes were correctly typed. The average of the differences between MEANS and reference was less than 2 ms (=1 sample) for both QRS onset and T end, and 2.1 ms for QT duration. The standard deviation of the differences was 3.8, 8.4, and 10.4 ms, respectively. Conclusions: A standard deviation of 10.4 ms for QT measurement seems large when related to the regulatory requirement that a prolongation as small as 5 ms should be detected. However, QT variabilities as encountered in different individuals will be larger than when measured in one individual during pharmacological intervention. Finally, if the U wave is part of the total repolarization, then T and U form a continuum and the end of T becomes questionable. [source]

Steady-State versus Non-Steady-State QT-RR Relationships in 24-hour Holter Recordings

PACING AND CLINICAL ELECTROPHYSIOLOGY, Issue 3 2000
GILLES LANDE
The aim of the present study was to investigate the QT-RR interval relationship in ambulatory ECG recordings with special emphasis on the physiological circumstances under which the QT-RR intervals follow a linear relation. Continuous ECG recordings make it possible to automatically measure QT duration in individual subjects under various physiological circumstances. However, identification of QT prolongation in Holter recordings is hampered by the rate dependence of QT duration. Comparison of QT duration and QT interval rate dependence between different individuals implies that the nature of the QT-RR relationship is defined in ambulatory ECG. Holter recordings were performed in healthy volunteers at baseline and after administration of dofetilide, a Class III antiarrhythmic drug. After dofetilide, beat-to-beat automated QT measurements on Holter tapes were compared with manually measured QT intervals on standard ECGs matched by time. The QT-RR relationship was analyzed at baseline in individual and group data during three different periods: 24-hour, daytime, and nighttime. Data were collected under steady-state or non-steady-state conditions of cycle length and fitted with various correction formulae. Our study demonstrated an excellent agreement between manually and automated measurements. The classic Bazett correction formula did not fit the QT-RR data points in individual or group data. When heart beats were selected for a steady rhythm during the preceding minute, QT-RR intervals fit a linear relationship during the day and night periods, but not during the 24-hour period in both individual and group data. In contrast, in the absence of beat selection, data fit a more complex curvilinear relationship irrespective of the period. Our study provides the basis for comparison of QT interval durations and QT-RR relationships between individuals and between groups of subjects. [source]

Z-Score for Benchmarking Reader Competence in a Central ECG Laboratory

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 1 2009
Gopi Krishna Panicker B.H.M.S., P.G.D.C.R.
Background: ECGs from thorough QT studies must be read in a central laboratory by trained experts. Standards of expertise are not presently defined. We, therefore, studied the use of Z-scores to define reader competence. Methods: Two hundred ECGs were read by 24 experts and the mean and standard deviation (SD) of QT measurements calculated for each ECG. Z-scores ([QTreader, mean QTexperts]/ SDexperts) for each ECG and mean of absolute Z-scores of all ECGs read by a reader were calculated. The highest mean absolute Z-score of experts was considered the cutoff to define competence. Hundred of these standardized ECGs were used to assess performance of readers from the central laboratory. Results: All experts had mean absolute Z-scores , 1.5. Using this cutoff, one of 28 experienced readers and 7 of 15 trainees had unacceptable Z-scores. After re-training, all achieved Z-scores <1.5. Comparing histograms of actual Z-scores of the 100 ECGs of readers with unacceptable scores with that of the reader with the best Z-score showed two patterns. Readers with histograms having a peak and tails similar to that of the best reader, but with leftward or rightward shift, consistently made shorter or longer QT measurements, respectively. A histogram with a flatter peak and wider tails, suggested that measurements were long in some ECGs and short in others. Conclusion: Mean absolute Z-score is useful to assess competence for measuring the QT interval on ECGs. Analysis of histograms can pinpoint problems in QT measurements. [source]

Automated QT Measurement and Application to Detection of Moxifloxacin-Induced Changes

Beat-to-Beat QT Dynamics in Healthy Subjects

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 1 2004
Berit T. Jensen M.D.
Background: Measures of QT dynamics express repolarization abnormalities that carry prognostic information, but the reproducibility of beat-to-beat QT dynamics has never been established. The QT interval is prolonged at night, but how the circadian rhythm and heart rate influence the dynamic QT measurements is still unsettled. The aims of the present study were: (1) to describe the reproducibility of beat-to-beat QT dynamics with respect to intrasubject, between-subject, and between-observer variability and (2) to describe the normal range, circadian variation, and heart rate dependence of QT dynamics. Methods: Ambulatory Holter recordings were performed three times on 20 healthy volunteers and were analyzed by two experienced cardiologists. Slope and intercept of the QT/RR regression, the variability of QT and R-R intervals expressed as the standard deviation, and the relation between QT and RR variability expressed as a variability ratio were measured among other QT dynamics. Results: The reproducibility of all QT dynamics was good. All QT dynamics showed circadian variation when calculated on an hourly basis. The day/night variation in slope could be explained by the differences in heart rate, whereas the day/night variation in intercept was heart rate independent. Conclusion: The present study shows that reliable automatic QT measurements could be performed, encouraging further evaluation of the clinical value of QT dynamics in risk stratification of cardiac patients. [source]

Effect of single doses of maraviroc on the QT/QTc interval in healthy subjects

BRITISH JOURNAL OF CLINICAL PHARMACOLOGY, Issue 2008
John D. Davis
AIMS To assess the effect of a single dose of maraviroc on the QTc interval in healthy subjects and to evaluate the QTc interval,concentration relationship. METHODS A single-dose, placebo- and active-controlled, five-way crossover study was conducted to investigate the effects of maraviroc (100, 300, 900 mg) on QTc in healthy subjects. Moxifloxacin (400 mg) was used as the active comparator. The study was double-blind with respect to maraviroc/placebo and open label for moxifloxacin. There was a 7-day wash-out period between each dose. QT interval measurements obtained directly from the electrocardiogram (ECG) recorder were corrected for heart rate using Fridericia's correction (QTcF). A placebo run-in day was conducted before period 3, when ECGs were collected at intervals while subjects were resting or during exercise. These ECGs plus other predose ECGs were used to evaluate the QT/RR relationship for each subject to enable calculation of an individual's heart rate correction for their QT measurements (QTcI). ECGs were taken at various intervals pre- and postdose in each study period. Pharmacokinetic parameters were determined for each maraviroc dose. The end-points that were evaluated were QTcF at median time to maximum concentration (Tmax) based on the machine readings and QTcI at median Tmax based on manual over-reads of the QT/RR data. A separate analysis of variance was used for each of the pair-wise comparisons for each end-point. The relationship between QTc interval and plasma concentration was also investigated using a mixed-effects modelling approach, as implemented by the NONMEM software system. A one-stage model was employed in which the relationship between QT and RR and the effects of maraviroc plasma concentration on QT were estimated simultaneously. RESULTS The mean difference from placebo in machine-read QTcF at median Tmax for maraviroc 900 mg was 3.6 ms [90% confidence interval (CI) 1.5, 5.8]. For the active comparator, moxifloxacin, the mean difference from placebo in machine-read QTcF was 13.7 ms. The changes from placebo for each of the end-points were similar for men and women. No subjects receiving maraviroc or placebo had a QTcF ,,450 ms (men) or QTcF ,,470 ms (women), nor did any subject experience a QTcF increase ,,60 ms from baseline at any time point. Analysis based on the QTcI data obtained from the manual over-readings of the ECGs gave numerically very similar results. The QT:RR relationship was similar pre- and postdose and was not related to maraviroc concentration. The population estimate of the QT:RR correction factor was 0.324 (95% CI 0.309, 0.338). The population estimate of the slope describing the QT,concentration relationship was 0.97 ,s ml ng,1 (95% CI ,0.571, 2.48), equivalent to an increase of 0.97 ms in QT per 1000 ng maraviroc plasma concentration. Most adverse events were mild to moderate in severity. CONCLUSIONS Single doses of maraviroc, up to and including 900 mg, had no clinically relevant effect on QTcF or QTcI. At all maraviroc doses and for both end-points, the mean difference from placebo for QTc was <4 ms. There was no apparent relationship between QT interval and maraviroc plasma concentration up to 2363 ng ml,1. This conclusion held in both male and female subjects, and there was no evidence of a change in the QT/RR relationship with concentration. [source]