Nonparametric Modeling and Population Approach to the Individualized Heart Rate Correction of the QT Interval
M. Germani (1), A. Russu (2), G. Greco (2), G. De Nicolao (2), F. Del Bene (1), F. Fiorentini (1), C. Arrigoni (1), M. Rocchetti (1)
(1) Pharmacokinetics & Modeling, Accelera - Nerviano Medical Sciences, Italy; (2) Dipartimento di Informatica e Sistemistica, Universitŗ di Pavia, Italy
Objectives: The QT interval is a measure of the time between the start of the Q wave and the end of the T wave in the heart's electrical cycle. Drug-induced ventricular arrhythmia associated with QT prolongation is a well-recognized form of drug toxicity. For this reason, the effect on the QT interval of compounds under development is evaluated in preclinical species.
The QT interval is dependent on the heart rate (the faster the heart rate, the shorter the QT interval) and has to be adjusted to aid interpretation. Existing correction formulas, such as Bazett's, Fridericia's, linear model, power model, and others, rely on the assumption of a parametric model, whose parameters are usually estimated from population data. Herein, a more flexible model-free nonparametric approach describing the dependence of the QT interval on the RR one is evaluated and a population modeling approach for individualized correction formulas is investigated.
Methods: The different approaches were compared using QT-RR data obtained in dogs from 24-h ambulatory electrocardiograms, for a total of 6108 QT-RR pairs. For scarcely sampled subjects, a population modeling approach was investigated. Differently from Piotrovsky , where a power model is used, a linear model in the logRR-logQT scale was assumed for each subject. Estimation of the model-free nonparametric correction formula was carried out according to an Empirical Bayes approach.
Results: The nonparametric approach provides a flexible method to perform QT correction. In particular, its performance, in terms of crossvalidatory RMSE, is superior to all the parametric formulas considered for both pooled and individualized correction. The study demonstrated and quantified also the definite advantage of individualized QT correction (over 30% in terms of RMSE). The population approach provides robust individual correction formulas also when few samples per subject are available.
Conclusions: This confirms that the individualized approach should be pursued whenever possible as suggested by the International Conference on Harmonization (ICH) , as well as in -. If a reference population is available, the individual correction formula of a new subject can be computed in closed form, thus easing the incorporation of the population approach within the standard data processing flow.
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 Piotrovsky VK et al. Cardiovascular safety data analysis via mixed-effects modelling. 9th PAGE Meeting, Salamanca, Spain, June 2000.