A comparison of three software platforms for automated ECG analysis

Area of Science:

• Pharmacology and Toxicology
• Cardiovascular Research
• Biomedical Engineering

Background:

• Regulatory guidelines for new compound safety assessment lack standardized data derivation methods.
• Increasing data volumes in safety studies necessitate evaluation of automated electrocardiogram (ECG) analysis tools.
• Cardiovascular telemetry studies generate extensive data requiring efficient analysis.

Purpose of the Study:

• To assess and compare the performance of commercially available automated ECG analysis software.
• To evaluate the suitability of these tools for analyzing cardiovascular telemetry data in preclinical safety studies.
• To identify the most effective ECG analysis approach for long-term telemetry monitoring.

Main Methods:

• Cardiovascular telemetry studies were performed in beagle dogs administered E-4031 (0 and 1 mg/kg).
• Raw ECG data were processed using Data Sciences International (DSI) Ponemah with pattern recognition option (PRO), EMKA ecgAuto, and Notocord HEM.
• Software performance was validated using a reference signal generator.

Main Results:

• All evaluated software applications demonstrated comparable performance.
• Significant QT/QTc interval increases (25-40 ms) were observed after E-4031 administration.
• All software consistently identified similar trends in QT/QTc analyses.

Conclusions:

• The selection of ECG analysis software depends on factors like animal model, real-time needs, and flexibility.
• While all tested applications showed similar trends, pattern recognition methods proved more adaptable to ECG morphology changes.
• Automated ECG analysis tools are crucial for efficient and reliable cardiovascular safety assessment.