The Role of Continuous Monitoring in Atrial Fibrillation Management

Assessment of OMT-28, a synthetic analog of omega-3 epoxyeicosanoids, in patients with persistent atrial fibrillation: Rationale and design of the PROMISE-AF phase II study

We designed a placebo controlled, double-blind, randomized, dose-finding phase II study on OMT-28 in the maintenance of sinus rhythm after electrical cardioversion (DCC) in patients with persistent atrial fibrillation (PROMISE-AF). OMT-28 is a first-in-class, synthetic analog of 17,18-epoxyeicosatetetraenoic acid, a bioactive lipid mediator generated by cytochrome P450 enzymes from the omega-3 fatty acid eicosapentaenoic acid. OMT-28 improves Ca²⁺-handling and mitochondrial function in cardiomyocytes and reduces pro-inflammatory signaling. This unique mode of action may provide a novel approach to target key mechanism contributing to AF pathophysiology. In a recent phase I study, OMT-28 was safe and well tolerated and showed favorable pharmacokinetics. The PROMISE-AF study (NCT03906799) is designed to assess the efficacy (primary objective), safety, and population pharmacokinetics (secondary objectives) of three different doses of OMT-28, administered once daily, versus placebo until the end of the follow-up period. Recruitment started in March 2019 and the study will include a total of 120 patients. The primary efficacy endpoint is the AF burden (% time with any AF), evaluated over a 13-week treatment period after DCC. AF burden is calculated based on continuous ECG monitoring using an insertable cardiac monitor (ICM). The primary efficacy analysis will be conducted on the modified intention-to-treat (mITT) population, whereas the safety analysis will be done on the safety population. Although ICMs have been used in other interventional studies to assess arrhythmia, PROMISE-AF will be the first study to assess antiarrhythmic efficacy and safety of a novel rhythm-stabilizing drug after DCC by using ICMs.

An On-Skin Electrode with Anti-Epidermal-Surface-Lipid Function Based on a Zwitterionic Polymer Brush

On-skin flexible devices provide a noninvasive approach for continuous and real-time acquisition of biological signals from the skin, which is essential for future chronic disease diagnosis and smart health monitoring. Great progress has been achieved in flexible devices to resolve the mechanical mismatching between conventional rigid devices and human skin. However, common materials used for flexible devices including silicon-based elastomers and various metals exhibit no resistance to epidermal surface lipids (skin oil and grease), which restricts the long-term and household usability. Herein, an on-skin electrode with anti-epidermal-surface-lipid function is reported, which is based on the grafting of a zwitterionic poly(2-methacryl-oyloxyethyl, methacryloyl-oxyethyl, or meth-acryloyloxyethyl phosphorylcholine) (PMPC) brush on top of gold-coated poly(dimethylsiloxane) (Au/PDMS). Such an electrode allows the skin-lipids-fouled surface to be cleaned by simple water rinsing owing to the superhydrophilic zwitterionic groups. As a proof-of-concept, the PMPC-Au/PDMS electrodes are employed for both electrocardiography (ECG) and electromyography (EMG) recording. The electrodes are able to maintain stable skin-electrode impedance and good signal-to noise ratio (SNR) by water rinsing alone. This work provides a material-based solution to improve the long-term reusability of on-skin electronics and offers a unique prospective on developing next generation wearable healthcare devices.

Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study

Background

Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF).

Objective

We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals.

Methods

Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR).

Results

In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR.

Conclusions

A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations.

Trial Registration

ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188