Variability of the QTc interval: Impact on defining drug effect and low-frequency cardiac event

Morganroth, Joel; Brown, Arthur M.; Critz, Stuart D.; Crumb, William J.; Kunze, Diana L.; Lacerda, Antonio E.; López, Héctor S.

doi:10.1016/0002-9149(93)90037-d

Cited by 76 publications

(42 citation statements)

References 3 publications

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“…Although there is a circadian pattern of QTc intervals during the daytime hours (e.g., due to postprandial changes), the variability of QTc values when accurately measured and corrected is much smaller than previously reported (13,24,27).…”

Section: Discussionmentioning

confidence: 66%

Subject-specific profiles of QT/RR hysteresis

Malik¹,

Hnatkova²,

Novotný³

et al. 2008

American Journal of Physiology-Heart and Circulatory Physiology

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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 Ϯ 8.1 yr) with 3 separate daytime (Ͼ13 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 Ϯ2 beats/min. Other segments were preceded by a stable heart rate. In segments preceded by variable rate, QT/RR hysteresis was characterized by parameters of the exponential decay models. The intrasubject SDs of values were compared with the intersubject SD of the individual means. The values were also correlated to individually optimized parameters of heart rate correction. Intrasubject SDs of were substantially smaller than the population SD of individual means (0.390 Ϯ 0.197 vs. 0.711, P Ͻ 0.0001). The 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 Ϯ 2.00, 6.33 Ϯ 1.31, and 4.66 Ϯ 0.85 ms, respectively, P Ͻ 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.QT adaptation; individual QT correction; electrocardiogram measurement; corrected QT variability HEART RATE DEPENDENCY of the electrocardiographic QT interval has been the subject of numerous studies. Electrocardiographic data sets of variable sizes, sources, and quality have been used to investigate the relationship of the QT interval to heart rate (3,12,14,26,28), most frequently with the aim of finding a universally applicable heart rate correction formula. On the contrary, the adaptation of QT interval duration to rapid rate changes, which is the lag with which QT interval duration responds to heart rate changes, has been studied much less frequently. Indeed, in numerous studies that investigated the relationship between QT interval and heart rate in short-term (such as 10 s) electrocardiograms (ECG), the QT adaptation to heart rate changes has been, as a rule, impossible since no heart rate history preceding the measured ECGs was available.The time scale of the adaptation of QT interval to heart rate changes, most frequently termed the QT/RR hysteresis, has been previously approximated in investigations with constant pacing rates. Such experiments measured both the action potential durations (11) and QT intervals in surface ECGs (17). Studies of QT/RR hysteresis in long-term ECG recordings without pacing provocation appeared only recently, ma...

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Section: Discussionmentioning

confidence: 66%

Subject-specific profiles of QT/RR hysteresis

Malik¹,

Hnatkova²,

Novotný³

et al. 2008

American Journal of Physiology-Heart and Circulatory Physiology

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“…[55][56][57][58][59] Circadian rhythm is characteristic for QTc as well. [60][61][62][63][64][65][66] Figure 4 shows pooled, individually corrected (as described earlier) QT intervals before treatment vs the clock time in 26 unrelated healthy individuals from the previously presented data. The smoothing line indicates a circadian variation in QTc I with the peak near 10 am.…”

Section: Circadian Rhythm In Qt and Qtcmentioning

confidence: 99%

Pharmacokinetic-pharmacodynamic modeling in the data analysis and interpretation of drug-induced QT/QTc prolongation

Piotrovsky¹

2005

AAPS J

109

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A BSTRACTIn this review, factors affecting the QT interval and the methods that are currently in use in the analysis of drug effects on the QT interval duration are overviewed with the emphasis on (population) pharmacokinetic-pharmacodynamic (PK-PD) modeling. Among which the heart rate (HR) and the circadian rhythm are most important since they may interfere with the drug effect and need to be taken into account in the data analysis. The HR effect or the RR interval (the distance between 2 consecutive R peaks) effect is commonly eliminated before any further analysis, and many formulae have been suggested to correct QT intervals for changes in RR intervals. The most often used are Bazett and Fridericia formulae introduced in 1920. They are both based on the power function and differ in the exponent parameter. However, both assume the same exponent for different individuals. More recent fi ndings do not confi rm this assumption, and individualized correction is necessary to avoid under-or overcorrection that may lead to artifi cial observations of drug-induced QT interval prolongation. Despite the fact that circadian rhythm in QT and QTc intervals is a welldocumented phenomenon, it is usually overlooked when drug effects are evaluated. This may result in a false-positive outcome of the analysis as the QTc peak due to the circadian rhythm may coincide with the peak of the drug plasma concentration. In view of these effects interfering with a potential drug effect on the QTc interval and having in mind low precision of QT interval measurements, a preferable way to evaluate the drug effect is to apply a population PK-PD modeling. In the literature, however, there are only a few publications in which population PK-PD modeling is applied to QT interval prolongation data, and they all refer to antiarrhythmic agents. In this review, after the most important sources of variability are outlined, a comprehensive population PK-PD model is presented that incorporates an individualized QT interval correction, a circadian rhythm in the individually corrected QT intervals, and a drug effect. The model application is illustrated using real data obtained with 2 compounds differing in their QT interval prolongation potential. The usefulness of combining data of several studies is stressed. Finally, the standard approach based on the raw observations and formal statistics, as described in the Preliminary Concept paper of the International Conference on Harmonization, is briefl y compared with the method based on population PK-PD modeling, and the advantages of the latter are outlined.

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“…Widely used measures are QT interval, PR interval, etc, such as those described in Morganroth, Brown and Critz (1993). ECG analyses based on visual inspection by cardiologists (a common practice in many hospitals and pharmaceutical companies) are likely to induce human error and are very costly in time.…”

Section: Accepted Manuscript 4 Real Data Resultsmentioning

confidence: 99%

Local functional data model for characterizing shape variation of similar curves

Zhou

Zhu

Sedransk

2017

Communications in Statistics - Simulation and Computation

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Similar curves" in the present paper refers to a family of curves whose major shape are similar, but who have variation coming from curve-specific sources. The goal here is to develop a general methodology to describe small changes among similar curves. Previous methods mainly focus on dimension reduction through FPCA, which are not appropriate for quantifying local variation. Here we consider a local functional data model which divides data into segments adaptively and models each segment with a shape invariant model. Such model has great flexibility in characterizing local variation of curves, as illustrated by simulation and real data examples.

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Variability of the QTc interval: Impact on defining drug effect and low-frequency cardiac event

Cited by 76 publications

References 3 publications

Subject-specific profiles of QT/RR hysteresis

Subject-specific profiles of QT/RR hysteresis

Pharmacokinetic-pharmacodynamic modeling in the data analysis and interpretation of drug-induced QT/QTc prolongation

Local functional data model for characterizing shape variation of similar curves

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