Resuscitation plus · 2026
Out-of-hospital cardiac arrest detection by a wearable: the first real-life case.
Клинический случайСтатьяНазвание и аннотация: Перевод в очереди
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Edgar Roos, Jansen Catharina E, Pol Lente R, Pisters Ron, van Royen Niels, Bonnes Judith L
Аннотация
INTRODUCTION: Automated cardiac arrest detection and alerting using wearable technology is an emerging innovation in resuscitation science. The DETECT program aims to develop a wristband that integrates photoplethysmography and accelerometry to identify out-of-hospital cardiac arrest events and alert the emergency medical services. We report the first case in which the algorithm successfully detected a cardiac arrest that occurred during daily life in an implantable cardioverter-defibrillator (ICD) patient.
CASE DESCRIPTION: A 64-year old male with a secondary prevention ICD participated in the DETECT-3 study. As part of the study, he was wearing the CardioWatch wristband during daily life for a two-month period. While cycling, the patient experienced a ventricular fibrillation cardiac arrest that was successfully terminated by the ICD. The cardiac arrest detection algorithm of the CardioWatch correctly recognized the cardiac arrest event and subsequent collapse based on photoplethysmography and accelerometry data during post-processing. The patient was admitted for rhythm observation and made a good recovery.
DISCUSSION: This first real-life case shows feasibility of cardiac arrest detection using wrist-derived photoplethysmography and accelerometry. Further studies are warranted to validate the algorithm in additional cardiac arrest cases, and to minimize false positive alerts during daily-life use. Once implemented, automated cardiac arrest detection and alerting has the potential to shorten treatment delays and improve survival.
CASE DESCRIPTION: A 64-year old male with a secondary prevention ICD participated in the DETECT-3 study. As part of the study, he was wearing the CardioWatch wristband during daily life for a two-month period. While cycling, the patient experienced a ventricular fibrillation cardiac arrest that was successfully terminated by the ICD. The cardiac arrest detection algorithm of the CardioWatch correctly recognized the cardiac arrest event and subsequent collapse based on photoplethysmography and accelerometry data during post-processing. The patient was admitted for rhythm observation and made a good recovery.
DISCUSSION: This first real-life case shows feasibility of cardiac arrest detection using wrist-derived photoplethysmography and accelerometry. Further studies are warranted to validate the algorithm in additional cardiac arrest cases, and to minimize false positive alerts during daily-life use. Once implemented, automated cardiac arrest detection and alerting has the potential to shorten treatment delays and improve survival.