Article Impact Level: HIGH Data Quality: STRONG Summary of Circulation: Arrhythmia and Electrophysiology, https://doi.org/10.1161/CIRCEP.125.014708 Dr. Roos Edgar et al.
Points
- A clinical validation study demonstrates that a novel wrist-worn wearable device utilizing a photoplethysmography algorithm can automatically detect acute out-of-hospital cardiac arrest events.
- The device accurately identified ninety-two percent of induced shockable cardiac disruptions including one hundred percent of ventricular fibrillation and ninety percent of pulseless ventricular tachycardia events.
- Investigators documented nine false-positive readings across one hundred twenty-five hours of continuous monitoring, indicating a low frequency of diagnostic errors within the controlled clinical cohort.
- Senior researchers indicate that integrating automated wearable sensors with emergency volunteer networks could significantly accelerate the delivery of localized cardiopulmonary resuscitation and defibrillation.
- Experts note that while early validation data is promising, additional real-world clinical research is required to evaluate algorithm performance against more common pulseless electrical activity rhythms.
Summary
This study evaluated the external validation of a wrist-derived photoplethysmography (PPG) algorithm designed for the automated detection of out-of-hospital cardiac arrest (OHCA). Utilizing data from the DETECT-1b trial, researchers analyzed 49 adult patients in the Netherlands with underlying cardiac arrhythmias who underwent procedures involving the controlled, short-lasting induction of life-threatening circulatory arrest. The clinical investigation aimed to determine if this non-invasive, algorithm-based wearable wristband could effectively act as a digital witness by identifying shockable rhythms, specifically pulseless ventricular tachycardia (pVT) and ventricular fibrillation (VF), to compress the emergency medical response timeline.
A total of 59 induced shockable cardiac arrest events were recorded and evaluated within a controlled clinical environment. The PPG algorithm successfully identified circulatory arrest with an overall accuracy of 92%, which remained consistent at 92% during the subsequent per-patient analysis evaluating only the initial induced event. Stratified diagnostic performance demonstrated a 100% detection rate for ventricular fibrillation and a 90% detection rate for pulseless ventricular tachycardia. Over a total duration of 125 monitoring hours, the wearable sensor recorded nine false-positive events, demonstrating a low overall frequency of erroneous alarms during continuous vital sign monitoring.
The study serves as an external validation of wearable PPG monitoring technology using prospective patient data, building on the 98% sensitivity observed during previous DETECT-1a algorithm development. The researchers noted that while conventional smartwatches utilize similar light-based sensors, they are not engineered to recognize malignant circulatory arrest. The findings indicate that integration of this validated algorithm into emergency networks could significantly improve survival odds by automating dispatch alerts, though future investigations are mandatory to establish diagnostic reliability, minimize false alarms, and analyze responses to non-shockable pulseless electrical activity in real-world settings.
Link to the article: https://www.ahajournals.org/doi/10.1161/CIRCEP.125.014708
References
Edgar, R., Scholte, N. T. B., Ebrahimkheil, K., Jansen, C. E., Beukema, R. J., Brouwer, M. A., Yap, S.-C., Mafi-Rad, M., Knops, R. E., Ronner, E., Cetinyurek-Yavuz, A., Vernooy, K., Boersma, E., Stas, P. C., Van Royen, N., & Bonnes, J. L. (2026). Automated cardiac arrest detection using wrist-worn photoplethysmography: External validation in patients with induced shockable cardiac arrest(DETECT-1b). Circulation: Arrhythmia and Electrophysiology, 19(5). https://doi.org/10.1161/CIRCEP.125.014708
