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Background: Unwitnessed out-of-hospital cardiac arrest is associated with low survival chances because of the delayed activation of the emergency medical system in most cases. Automated cardiac arrest detection and alarming using biosensor technology would offer a potential solution to provide early help. We developed and validated an algorithm for automated circulatory arrest detection using wrist-derived photoplethysmography from patients with induced circulatory arrests., Methods: In this prospective multicentre study in three university medical centres in the Netherlands, adult patients (aged 18 years or older) in whom short-lasting circulatory arrest was induced as part of routine practice (transcatheter aortic valve implantation, defibrillation testing, or ventricular tachycardia induction) were eligible for inclusion. Exclusion criteria were a known bilateral significant subclavian artery stenosis or medical issues interfering with the wearing of the wristband. After providing informed consent, patients were equipped with a photoplethysmography wristband during the procedure. Invasive arterial blood pressure and electrocardiography were continuously monitored as the reference standard. Development of the photoplethysmography algorithm was based on three consecutive training cohorts. For each cohort, patients were consecutively enrolled. When a total of 50 patients with at least one event of circulatory arrest were enrolled, that cohort was closed. Validation was performed on the fourth set of included patients. The primary outcome was sensitivity for the detection of circulatory arrest., Findings: Of 306 patients enrolled between March 14, 2022, and April 21, 2023, 291 patients were included in the data analysis. In the development phase (n=205), the first training set yielded a sensitivity for circulatory arrest detection of 100% (95% CI 94-100) and four false positive alarms; the second training set yielded a sensitivity of 100% (94-100), with six false positive alarms; and the third training set yielded a sensitivity of 100% (94-100), with two false positive alarms. In the validation phase (n=86), the sensitivity for circulatory arrest detection was 98% (92-100) and 11 false positive circulatory arrest alarms. The positive predictive value was 90% (95% CI 82-94)., Interpretation: The automated detection of induced circulatory arrests using wrist-derived photoplethysmography is feasible with good sensitivity and low false positives. These promising findings warrant further development of this wearable technology to enable automated cardiac arrest detection and alarming in a home setting., Funding: Dutch Heart Foundation (Hartstichting)., Competing Interests: Declaration of interests KE is a data scientist at Corsano Health. RJB received a research grant from Biosense Webster, and payment for a lecture from The Cardiovascular Education Institute. KV received a research grant from Medtronic; consulting fees from Medtronic, Abbott, and Biosense Webster; a speakers fee from Philips; participates in the advisory board of Medtronic; and received educational grants from Medtronic, Abbott, and Biosense Webster. S-CY is a consultant of Boston Scientific; has received research grants from Biotronik, Medtronic, and Boston Scientific; received personal payments for presentations from Boston Scientific, Biotronik, and Medtronic; and his institution received payment for his participation on a Data Safety Monitoring Board from Boston Scientific. ER received consulting fees from Corsano Health. NvM received grants from Abbott Vascular, Boston Scientific, Medtronic, Edwards Lifesciences, Daiichi Sankyo, and AstraZeneca; received personal consulting fees from Abbott Vascular, Boston Scientific, Medtronic, Anteris, JenaValve, Daiichi Sankyo, AstraZeneca, Amgen, Siemens, and Pie Medical; and received payment for presentations from Abbott Vascular, Biotronik, Amgen, Daiichi Sankyo, Medtronic, and Boston Scientifc. PCS is the CEO of Corsano Health. NvR received a research grant from the Dutch Heart Foundation related to this manuscript; received research grants from Biotronik, Abbott, Medtronic, and Philips, not related to this manuscript; and speaker fees were received from Abbott, Bayer, RainMed, and Microport, not related to this manuscript. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)