Model-Based Correction to the QT Interval for Heart Rate for Assessing Mean QT Interval Change Due to Drug Effect
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Model-Based Correction to the QT Interval for Heart Rate for Assessing Mean QT Interval Change Due to Drug Effect
Greg C.G. Wei, PLD Associate Director, Biostatistics, Pfizer Global Research and Development, New London, Connecticut
Josh Y.H. Chon, PLD Senior Biometrician, Clinical Biostatistics. Merck Research Labs, West Point, Pennsylvania
Key Words QT interval; Prolongation; Correction formula; Model-based correction Correspondence Address Greg C. G. Wei, PhD (e-mail: greg_cg_wei@groton .pfizer.com) .
Drugs that alter ventricular repdarization have been associated with malignant ventricular arrhythmia, which has been shown to increasewith increasing QT interval-the time required for ventricular depdarization and rqdanzation. To determine whether a new drug, especial!^ a nonanti-arrhythmia drug, can cause an increase in the QT interval is a necessary step in safety evaluation. Since the QT interval is inversely related to the heart rate, this relationship needs to be accounted for in the assessment of drug effect. In current practice, the most commonlyused approachfor assessingmean change in the QT interval is to cowed the QT interval ly one of many competing cowedion methods to QT, then to conduct statistical analysis on QT, a disjoint two-step approach. The shortcomings of such an approach are: I. The cowection formulas often fail to cowect the QT interval for the change in heart rate and this can lead to biased estimation of the drug effect; and 2. The analysis based on QT, only provides estimation of the drug fled at afied heart rate (60 bpm), which ignores potential interaction between the drug effect on the QT interval and the heart rate. The analysis based on QT, also ignores the
INTRODUCTION Drugs that alter ventricular repolarization have been associated with malignant ventricular arrhythmia, which has been shown to increase with QT interval-the time required for ventricular depolarization and repolarization. A delay in cardiac repolarization creates an electrophysiological environment that favors the development of cardiac arrhythmias, most severely torsade de pointes, but possibly other ventricular arrhythmias as well. While the degree of QT prolongation is recognized as an imperfect biomarker for proarrhythmic risk, there is a relationship between QT prolongation and the risk
variability in data-driven correction formulas, which can lead to incorrect inferences. In this article a new approach is proposed based on statistical modeling of thefinctional relationship between the QT interval and the heart rate. Such modeling captures the nature of the relationship between the QT interval and the heart rate based on measurements from placebo subjects or at baseline, then uses this relationship to proviide adjustments to the estimation of the drug effect on the QT interval for the heart rate change at a given heart rate. The key idea behind this new approach is that the potential drug effect on the QT interval at a given heart rate can be perceived as the change in the QT interval after dosing the
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