Clinical trial NCT06692374: Automated Cardiac Arrest Detection and Alerting in Daily Life
Official title: Smartwatch-based Automated Cardiac Arrest Detection and Activation of the Emergency Medical Chain: Validation in a Home Setting
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Cardiac Arrest (CA)
Sudden Death, Cardiac
Keywords: Automated cardiac arrest detection
Wearable
Photoplethysmography
Sudden Cardiac Death
Lead sponsor: Radboud University Medical Center
Sponsor class: OTHER
Collaborators: name: Corsano Health
First posted: 2024-11-18
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: The study population consists of patients with an ICD.

Brief summary: 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.

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

Primary outcomes:
measure: Sensitivity for cardiac arrest detection
timeFrame: From the time of enrollment to the end of the wearing period of the wristband (maximal two years)
Secondary outcomes:
measure: Positive predictive value for cardiac arrest detection
timeFrame: From the time of enrollment to the end of the wearing period of the wristband (maximal two years)
measure: False positive alarm rate
timeFrame: From the time of enrollment to the end of the wearing period of the wristband (maximal two years)
Other outcomes:
Locations:
city: Nijmegen
country: Netherlands
facility: RadboudUMC
state: Gelderland
Study officials:
References:
citation: 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/S2589-7500(23)00249-2.