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.

Rationale and design of a digital trial using smartphones to detect subclinical atrial fibrillation in a population at risk: The eHealth-based bavarian alternative detection of Atrial Fibrillation (eBRAVE-AF) trial.

Current guidelines recommend opportunistic screening for subclinical atrial fibrillation (AF) taking advantage of e-health-based technologies. However, the efficacy of a fully scalable e-health-based strategy for AF detection in a head-to-head comparison with routine symptom-based screening is unknown. eBRAVE-AF is an investigator-initiated, digital, prospective, randomized, siteless, open-label, cross-over study to evaluate an e-health-based strategy for detection of AF in a real-world setting. 67,488 policyholders of a large German health insurance company (Versicherungskammer Bayern, Germany) selected by age ≥ 50 years and a CHA2DS2-VASc score ≥ 1 (females ≥2) are invited to participate. Subjects with known AF or on treatment with oral anticoagulation are excluded. After obtaining electronic informed consent, at least 4,400 participants will be randomly assigned to an e-health-based screening strategy or routine symptom-based screening. The e-health-based strategy consists of repetitive one-minute photoplethysmographic (PPG) pulse wave assessments using a certified smartphone app (Preventicus Heartbeats, Preventicus, Jena, Germany), followed by a confirmatory 14-day ECG patch (CardioMem CM 100 XT, Getemed, Teltow, Germany) in case of abnormal findings. After 6 months, participants are crossed over to the other study arm. Primary endpoint is the incidence of newly diagnosed AF leading to oral anticoagulation indicated by an independent physician. Clinical follow-up will be at least 12 months. In both groups, follow-up is performed by 4-week app-based questionnaires, personal contact in case of abnormal findings, and matching with claim-based insurance data and medical reports. At time of writing enrollment is completed. First results are expected to be available in mid-2021.

Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial.

Digital smart devices have the capability of detecting atrial fibrillation (AF), but the efficacy of this type of digital screening has not been directly compared to usual care for detection of treatment-relevant AF. In the eBRAVE-AF trial ( NCT04250220 ), we randomly assigned 5,551 policyholders of a German health insurance company who were free of AF at baseline (age 65 years (median; interquartile range (11) years, 31% females)) to digital screening (n = 2,860) or usual care (n = 2,691). In this siteless trial, for digital screening, participants used a certified app on their own smartphones to screen for irregularities in their pulse waves. Abnormal findings were evaluated by 14-day external electrocardiogram (ECG) loop recorders. The primary endpoint was newly diagnosed AF within 6 months treated with oral anti-coagulation by an independent physician not involved in the study. After 6 months, participants were invited to cross-over for a second study phase with reverse assignment for secondary analyses. The primary endpoint of the trial was met, as digital screening more than doubled the detection rate of treatment-relevant AF in both phases of the trial, with odds ratios of 2.12 (95% confidence interval (CI), 1.19-3.76; P = 0.010) and 2.75 (95% CI, 1.42-5.34; P = 0.003) in the first and second phases, respectively. This digital screening technology provides substantial benefits in detecting AF compared to usual care and has the potential for broad applicability due to its wide availability on ordinary smartphones. Future studies are needed to test whether digital screening for AF leads to better treatment outcomes.

PG545 enhances anti-cancer activity of chemotherapy in ovarian models and increases surrogate biomarkers such as VEGF in preclinical and clinical plasma samples.

BACKGROUND: Despite the utility of antiangiogenic drugs in ovarian cancer, efficacy remains limited due to resistance linked to alternate angiogenic pathways and metastasis. Therefore, we investigated PG545, an anti-angiogenic and anti-metastatic agent which is currently in Phase I clinical trials, using preclinical models of ovarian cancer. METHODS: PG545's anti-cancer activity was investigated in vitro and in vivo as a single agent, and in combination with paclitaxel, cisplatin or carboplatin using various ovarian cancer cell lines and tumour models. RESULTS: PG545, alone, or in combination with chemotherapeutics, inhibited proliferation of ovarian cancer cells, demonstrating synergy with paclitaxel in A2780 cells. PG545 inhibited growth factor-mediated cell migration and reduced HB-EGF-induced phosphorylation of ERK, AKT and EGFR in vitro and significantly reduced tumour burden which was enhanced when combined with paclitaxel in an A2780 model or carboplatin in a SKOV-3 model. Moreover, in the immunocompetent ID8 model, PG545 also significantly reduced ascites in vivo. In the A2780 maintenance model, PG545 initiated with, and following paclitaxel and cisplatin treatment, significantly improved overall survival. PG545 increased plasma VEGF levels (and other targets) in preclinical models and in a small cohort of advanced cancer patients which might represent a potential biomarker of response. CONCLUSION: Our results support clinical testing of PG545, particularly in combination with paclitaxel, as a novel therapeutic strategy for ovarian cancer.