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 deg2 units by application of continuous wavelet transformation (PRDwavelet). The phase rectified signal averaging method (PRDPRSA) 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 PRDwavelet and PRDPRSA 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 PRDwavelet. PRDwavelet was dichotomized at the established cut-off value of ≥5.75 deg2. RESULTS: The ratio between PRDwavelet and PRDPRSA increased with increasing heart-rate and mean dT°-values (p < 0.001 for both). The correlation coefficient between PRDwavelet and PRDPRSA 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 PRDwavelet and PRDPRSA obtained from the simulation cohort. The calculated PRDwavelet 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 PRDPRSA to the widely validated PRDwavelet, which could unify the calculation methods and cut-offs for PRD.

Telemedical management of symptomatic COVID-19 outpatients.

Background: COVID-19 remains a challenge to individual health and healthcare resources worldwide. Telemedical surveillance might minimise hospitalisation and direct patient-physician contacts. Yet, randomised clinical trials evaluating telemedical management of COVID-19 patients are lacking. Methods: COVID-SMART is a randomised, open-label, controlled clinical trial investigating whether telemedicine reduces the primary end-point of hospitalisation or any unscheduled utilisation of an emergency medical service within 30 days of follow-up. Key secondary end-points included mortality and primary end-point components. We enrolled acutely infected SARS-CoV-2 patients suitable for outpatient care. All presented with ≥1 risk factor for an adverse COVID-19 course. Patients were randomised 1:1 into a control group receiving standard of care and an intervention group receiving smartphone-based assessment of oxygen saturation, heart rate and electrocardiogram, and telemedical counselling via a 24/7 emergency hotline. Results: Of 607 enrolled patients (mean±sd age 46.7±13.5 years), 304 were randomised into the intervention and 303 into the control group. The primary end-point occurred in 6.9% (n=21) of the intervention and in 9.6% (n=29) of the control group (hazard ratio (HR) 0.72, 95% confidence interval (CI) 0.41-1.26; p=0.24). No deaths occurred during follow-up. Fewer intervention group participants utilised outpatient-based emergency medical services (HR 0.43, 95% CI 0.20-0.90; p=0.03). Conclusions: COVID-SMART is the first randomised clinical trial assessing the benefit of telemedicine in an acute respiratory infectious disease. Whereas telemedical management did not reduce the primary end-point of hospitalisation, fewer intervention group patients used outpatient-based emergency services, suggesting a potential benefit for less-acutely infected individuals.