Prolongation of the QTc interval is the primary endpoint and surrogate biomarker used to monitor abnormal cardiac repolarization following drug treatment1.
However, there is a high degree of natural variability in any ambulatory cardiovascular assessment, and detecting cardiovascular changes of small, but biologically relevant, magnitude can be challenging.
The use of larger time-averaged data “bins” called “super-intervals” improves statistical sensitivity to detect QTc interval prolongation and thus allows to get more robust QTc datasets with reduced signal variability.
To create super-intervals in our ecgAUTO software, the user must create protocol instructions to bin average values of physiological parameters (heart rate, blood pressure, body temperature, or activity) in “steps”, for instance every 1-5 minutes, and then further average group of steps in “trains”.
In the example below, each color represents a super-interval averaged every 15 minutes, from data averaged every 5 minutes.
These data can subsequently be used for statistical analysis in separate software, such as ExcelPython, SPSS, SYSTAT, or Prism.
1 Improving the in Vivo QTc assay: The value of implementing best practices to support an integrated nonclinical-clinical QTc risk assessment and TQT substitute. Journal of Pharmacological and Toxicological Methods, 2023
Statistical power analysis of cardiovascular safety pharmacology studies in conscious rats. Siddhartha Bhatt et al. J Pharmacol Toxicol Methods. 2016
Best practice considerations for nonclinical in vivo cardiovascular telemetry studies in non-rodent species: Delivering high quality QTc data to support ICH E14/S7B Q&As. Eric I. Rossman et al. Journal of Pharmacological and Toxicological Methods, 2023.
Cardiovascular safety assessments in the conscious telemetered dog: Utilisation of super-intervals to enhance statistical power. A. Sivarajah et al. Journal of Pharmacological and Toxicological Methods, 2010
