Design of the DETECT project: automated cardiac arrest detection and activation of the emergency medical chain integrated into a wristband
Roos Edgar, Kambiz Ebrahimkheil, Niels T B Scholte, Catharina E. Jansen, Rypko Beukema, Marc A. Brouwer, Eelko Ronner, Aysun Cetinyurek‐Yavuz, Marit van Barreveld, Marcel G. W. Dijkgraaf, Peter Stas, Eric Boersma, Niels van Royen, Judith Bonnes
Abstract
Introduction While survival rates for witnessed out-of-hospital cardiac arrest have improved, assistance for unwitnessed cases often arrives too late. Automated cardiac arrest detection and alerting through wearable biosensor technology could catalyse early assistance and improve survival. Recently, an algorithm for automated detection of induced cardiac arrest was developed using wrist-derived photoplethysmography (DETECT-1a study). This paper outlines the next steps in the DETECT program, aimed at developing, validating, and preparing for the future implementation of automated cardiac arrest detection technology. Methods External validation (sensitivity; false positives) of the PPG-algorithm developed in DETECT-1a will be performed in patients with induced shockable cardiac arrest (DETECT-1b; n=50), and in cardiac arrest following withdrawal of life-sustaining treatment (DETECT-1c; n=∼50). To optimize cardiac arrest detection, DETECT-2 is set out to develop an algorithm for detection of sudden falls mimicking cardiac arrest related collapses using accelerometry data (n=20). In DETECT-3, false positive alarm rates will be studied in daily life settings, with multiple iterations to refine the algorithm: healthy volunteers/patients (n=∼300) will wear the wristband for approximately two months. Finally, DETECT-4 will validate the cardiac arrest detection technology in healthy volunteers (4a; n=∼50) and implantable cardioverter defibrillator-patients (4b; n=∼200), assessing sensitivity and false positives. Early health technology assessment is part of the project. Discussion The DETECT project aims to develop and validate a wristband-integrated technology for automated cardiac arrest detection and alerting in daily life settings. To support future implementation, early health technology assessment is incorporated from the outset. By providing immediate alerts, this technology has the potential to enhance rescuer response and improve cardiac arrest survival.