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Periodic repolarization dynamics: Different methods for quantifying low-frequency oscillations of repolarization.

Lauren E Sams¹, Maximilian Wörndl², Leonie Bachinger²

¹Medizinische Klinik und Poliklinik I, University Hospital Munich, Ludwig-Maximilians University, Munich, Germany; German Center for Cardiovascular Research (DZHK), Partner Site: Munich Heart Alliance, Munich, Germany.

Journal of Electrocardiology

|November 23, 2023

Summary

This study introduces a new algorithm to convert Phase Rectified Signal Averaging (PRSA) measurements to Periodic Repolarization Dynamics (PRD) wavelet analysis. This conversion improves accuracy in predicting mortality risk in cardiac patients.

Keywords:

Autonomic regulation Periodic repolarization dynamics (PRD)Phase rectified signal averaging T-wave Wavelet analysis

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Area of Science:

Cardiology
Biomedical Engineering
Signal Processing

Background:

Periodic Repolarization Dynamics (PRD) is an electrocardiographic biomarker for repolarization instability, predicting mortality in cardiomyopathy.
Two methods exist: PRD wavelet analysis (PRD^wavelet) in deg² and PRSA (PRD^PRSA) in deg.
A correlation and conversion formula between these PRD calculation methods were previously unknown.

Purpose of the Study:

To investigate the relationship between PRD^wavelet and PRD^PRSA.
To develop and validate a formula for converting PRD^PRSA to PRD^wavelet.
To assess the clinical utility of the conversion formula in predicting mortality post-myocardial infarction.

Main Methods:

Simulated 1,000,000 beat-to-beat repolarization direction (dT°) signals to calculate PRD using both methods.
Determined the ratio between PRD^wavelet and PRD^PRSA across varying dT° and heart rates.
Validated the conversion formula in 455 post-myocardial infarction patients, correlating calculated PRD^wavelet with observed outcomes.

Main Results:

The ratio of PRD^wavelet to PRD^PRSA increased with heart rate and mean dT° (p < 0.001).
Correlation between PRD^wavelet and PRD^PRSA improved from 0.908 to 0.945 after applying the derived conversion formula.
The converted PRD^wavelet accurately classified 98% of low-risk and 87% of high-risk patients, identifying 97% of high-risk mortality.

Conclusions:

This study provides the first analytical comparison of PRD calculation methods using simulated and clinical data.
A novel algorithm is proposed to convert PRD^PRSA to PRD^wavelet.
This conversion has the potential to unify PRD calculation methods and established cut-off values for clinical application.