Dynamic analysis of Holter or continuous ECG data beat-to-beat (QTbtb) is an advanced cardiac safety assessment method that allows for quantification of QT interval changes under varying conditions of heart rate and autonomic tone. This assessment is not possible with the use of standard QT correction formulae. The advanced ECG biomarker relies on a set of sophisticated algorithms for dynamic data analysis, providing drug developers with an ability to visualize how a sequential series of QT-RR measurements or “cloud” of data moves over time at baseline and at the matched on-drug time period. This visualization ability allows for a more precise evaluation of pharmacokinetics in relationship pharmacodynamic events that affect the ECG such as behavioral or hemodynamic changes. To obtain an accurate statistical representation of the dynamic QT-RR states, instead of linear correction, the QTbtb method relies on a statistical methods such as a bootstrapping technique which determines the upper (or lower) 95% confidence boundary of the normal or baseline dataset.

QTbtb provides a means to differentiate QT interval prolongation effects incurred through inhibition of the repolarization process from changes in the QT interval incurred through physiologic autonomic-mediated reflexes. It avoids errors [link to false-positive QTc case studies in 2b.] associated with the use of standard correction factors such as Bazett and Fridericia. Much of the cause for discrepancy in correction factors is because no single mathematical transformation can describe the rapidly changing nonlinear dynamics of the QT-RR interval relationship.

This sampling technique is commonly used in various scientific fields and it is more appropriate than linear methods for studying changes in the non-uniformly distributed data samples, such as QT-RR measurements from Holter recordings. This technique of random, iterative sampling of the sequential cardiac cycles is necessary to mathematically reflect the varying density and non-uniform distribution bounds of each dataset as they are formed into “clouds” represented as the uncorrected QTbtb value. Traditional mathematical averaging, data smoothing and estimates of error should not be applied in this situation because they reduce outlier beats and total heterogeneity that may provide useful information to understanding arrhythmogenic liability. The bootstrap sampling technique, used across the continuum of RR intervals during a baseline period, allows differences in the clouds to be compared to the asymmetric upper 95% confidence bounds that accurately describe the normal QT limits (bounds) under varying physiological conditions and to permit quantification of outlier beats above this boundary.

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I Zhou X-H and Tu W (2000) Confidence intervals for the mean of diagnostic test charge data containing zeros. Biometrics 56:1118–1125.

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