Automated Cardiac Arrest Detection and Alerting in Daily Life
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Cardiac Arrest (CA), Sudden Death, Cardiac. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Netherlands
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Smartwatch-based Automated Cardiac Arrest Detection and Activation of the Emergency Medical Chain: Validation in a Home Setting
Overview
Surviving a cardiac arrest that happens outside the hospital depends on quickly recognizing the event and immediately starting CPR. Survival rates have improved when cardiac arrest is witnessed, but when it isn't, help often arrives too late. Wearable biosensors, like special wristbands, could detect cardiac arrest automatically and alert emergency responders, providing faster help. In the already finished DETECT-1 study, the investigators developed a system that uses wrist-worn sensors to identify cardiac arrest. The goal of the current study is to test how well this system works in people who have an implantable cardioverter defibrillator (ICD). An ICD is a device that monitors and treats dangerous heart rhythms. Study participants will wear a medical wristband with sensors that monitor the heartrate and movement during their daily activities to see if the system accurately detects cardiac arrest.
Detailed description
Survival from out-of-hospital cardiac arrest (OHCA) depends on fast recognition of cardiac arrest and immediate initiation of cardiopulmonary resuscitation (CPR). While survival chances for witnessed OHCA have increased, unwitnessed cases still often receive help too late. Wearable biosensor technology with the functionality of automated cardiac arrest detection and activation of the emergency medical chain would offer a potential solution to provide early help. In the recently published DETECT-1 study, an algorithm for automated cardiac arrest detection using wrist-derived photoplethysmography (PPG) was recently developed.
The current study is a prospective multicenter observational cohort study to validate the sensitivity of the developed cardiac arrest detection algorithm in patients with an implantable cardioverter defibrillator (ICD) during daily life. The study population includes patients with an ICD. Study participants will wear a medical wristband with PPG and accelerometer sensors during daily life.
Primary outcome measures
- Sensitivity for cardiac arrest detection [Time frame: From the time of enrollment to the end of the wearing period of the wristband (maximal two years)]
Secondary outcome measures (2)
- Positive predictive value for cardiac arrest detection [Time frame: From the time of enrollment to the end of the wearing period of the wristband (maximal two years)]
- False positive alarm rate [Time frame: From the time of enrollment to the end of the wearing period of the wristband (maximal two years)]
Eligibility criteria
Inclusion criteria
- Patients with an implantable cardioverter defibrillator (ICD)
- Age 18 years or older
- Fitting the wristband
- In possession of a smartphone that is compatible with the wristband
Exclusion criteria
- Known hemodynamically relevant bilateral subclavian artery stenosis
- Medical issues that interfere with wearing of the wristband (e.g. skin disorders)
- Insufficient skills to operate with the device/app
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Netherlands · 1 center
- RadboudUMC — Nijmegen
Publications
- Edgar R, Scholte NTB, Ebrahimkheil K, Brouwer MA, Beukema RJ, Mafi-Rad M, Vernooy K, Yap SC, Ronner E, van Mieghem N, Boersma E, Stas PC, van Royen N, Bonnes JL. Automated cardiac arrest detection using a photoplethysmography wristband: algorithm development and validation in patients with induced circulatory arrest in the DETECT-1 study. Lancet Digit Health. 2024 Mar;6(3):e201-e210. doi: 10.1016/ PMID 38395540
Identifiers
NCT: NCT06692374 · 116424