Marking the Ends of ECG T-Waves for Assessing Cardiac Safety: Algorithms and Experts

Abstract

The prolongation of QT interval on electrocardiogram (ECG) is the current measure for cardiac safety that is used in drug development and drug approval. Although in thorough QT studies pharmaceutical companies need to measure QT intervals for thousands of beats, they mainly rely on experts to mark the QT interval endpoints. However, selected beats of data show that the difference between two experts’ marks can easily exceed 10 milliseconds. Note that for QT analyses presented to the FDA, if the maximal difference over all time points between QT measures comparing control to drug exceeds 10 milliseconds, the question of cardiac safety requires further discussion. Indeed experts appear to use the slope and curvature of the waveform differently in judging the end of the T-wave.

This article develops a Bayesian approach combining both slope and curvature information. We show that the difference between the automatic Bayesian marks and either of the experts’ marks is not statistically larger than the difference between two experts’ marks, thus this approach is successful in closely approximating the experts’ results in marking T-wave end, and it is much faster and cost efficient. Being algorithmic, our method offers the opportunity to be more consistent.

Keywords: :

• Bayesian algorithm
• Functional data analysis
• QT interval