Paperdoi:10.1016/j.lanepe.2026.101791
Automated cardiac arrest detection using wrist-derived photoplethysmography during withdrawal of life-sustaining treatment: a prospective clinical validation study
The Lancet Regional Health – Europe (Aug 2026); PMCID PMC13427576
Licence: CC BY 4.0
TL;DR
Prospective single-centre ICU study, adults with planned withdrawal of life-sustaining treatment; CardioWatch 287-2 (Corsano) worn until death; multi-wavelength PPG (green, red, infrared), 32 or 128 Hz, streamed via Samsung Galaxy A40. Reference: continuous ECG + invasive arterial pressure. Prior rule-based model (DETECT-1A/1B, induced arrest) refined on two training cohorts, then tested on a separate cohort. Preprocessing: 2nd-order Butterworth 0.5–4 Hz, downsampled to 32 Hz.
- Form factor: wrist
Abstract
BackgroundAutomated cardiac arrest detection and alerting using wearable technology has the potential to shorten recognition delays for unwitnessed out-of-hospital cardiac arrest. In DETECT-1A and -1B, a photoplethysmography-based detection model was developed and validated in patients with induced cardiac arrest. This study evaluates model performance in true cardiac arrests following withdrawal of life-sustaining treatment.MethodsProspective, single-center study in adult ICU patients with planned withdrawal of life-sustaining treatment. Patients wore a photoplethysmography-wristband (CardioWatch) until death. Continuous ECG and invasive arterial pressure served as reference standards. The previously developed rule-based algorithm was refined using two training cohorts and evaluated in a separate test cohort. Endpoints were sensitivity for cardiac arrest detection and false positive alerts.FindingsForty-four patients were included (training 1: n = 10; training 2: n = 11; test: n = 23), median age 65 years; 75% male, all with non-shockable cardiac arrest. Sensitivity for cardiac arrest detection was 100% (10/10; 95% confidence interval [CI] 66-100%) and 90% (9/10; 95% CI 54-99%), in training 1 and 2, respectively. In the test set, sensitivity was 100% (23/23; 95% CI 82-100%), with one false positive alert. Cardiac arrest was detected at a mean arterial pressure of 30 mmHg (IQR 24-35) and pulse pressure of 13 mmHg (IQR 11-19).InterpretationCardiac arrest can be detected with high sensitivity using wrist-derived photoplethysmography, providing first evidence on model performance in true cardiac arrest, specifically in non-shockable cases. Findings support further development of wearable-based cardiac arrest detection technologies to enable earlier recognition for unwitnessed cardiac arrest.FundingDutch Heart Foundation, Radboudumc.
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Tasks & signals
Signals
Keywords
How it works
- Wavelengths
- green, red, ir
Cite this paper
APA
Automated cardiac arrest detection using wrist-derived photoplethysmography during withdrawal of life-sustaining treatment: a prospective clinical validation study. (2026). The Lancet Regional Health – Europe. https://doi.org/10.1016/j.lanepe.2026.101791
BibTeX
@article{edgarwristppgcar2026automated,
title = {{Automated cardiac arrest detection using wrist-derived photoplethysmography during withdrawal of life-sustaining treatment: a prospective clinical validation study}},
year = {2026},
journal = {The Lancet Regional Health – Europe},
doi = {10.1016/j.lanepe.2026.101791}
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