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

BACKGROUND

Periodic repolarization dynamics (PRD) is an electrocardiographic biomarker that quantifies low-frequency (LF) instabilities of repolarization. PRD is a strong predictor of mortality in patients with ischaemic and non-ischaemic cardiomyopathy. Until recently, two methods for calculating PRD have been proposed. The wavelet analysis has been widely tested and quantifies PRD in deg units by application of continuous wavelet transformation (PRD). The phase rectified signal averaging method (PRD) is an algebraic method, which quantifies PRD in deg. units. The correlation, as well as a conversion formula between the two methods remain unknown.

METHOD

The first step for quantifying PRD is to calculate the beat-to-beat change in the direction of repolarization, called dT°. PRD is subsequently quantified by means of either wavelet or PRSA-analysis. We simulated 1.000.000 dT°-signals. For each simulated signal we calculated PRD using the wavelet and PRSA-method. We calculated the ratio between PRD and PRD for different values of dT° and RR-intervals and applied this ratio in a real-ECG validation cohort of 455 patients after myocardial infarction (MI). We finally calculated the correlation coefficient between real and calculated PRD. PRD was dichotomized at the established cut-off value of ≥5.75 deg.

RESULTS

The ratio between PRD and PRD increased with increasing heart-rate and mean dT°-values (p < 0.001 for both). The correlation coefficient between PRD and PRD in the validation cohort was 0.908 (95% CI 0.891-0.923), which significantly (p < 0.001) improved to 0.945 (95% CI 0.935-0.955) after applying the formula considering the ratio between PRD and PRD obtained from the simulation cohort. The calculated PRD correctly classified 98% of the patients as low-risk and 87% of the patients as high-risk and correctly identified 97% of high-risk patients, who died within the follow-up period.

CONCLUSION

This is the first analytical investigation of the different methods used to calculate PRD using simulated and clinical data. In this article we propose a novel algorithm for converting PRD to the widely validated PRD, which could unify the calculation methods and cut-offs for PRD.