Clinical trial NCT04023188: A Ring-type Wearable Device for Atrial Fibrillation
Official title: CART Phase 2 Study: Diagnostic Performance of a Ring-type Wearable Device Using Deep Learning Analysis of Photoplethysmographic Signals for Detecting Atrial Fibrillation
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Atrial Fibrillation
Keywords: atrial fibrillation
deep learning
diagnosis
photoplethysmography
wearable electronic devices
Lead sponsor: Seoul National University Hospital
Sponsor class: OTHER
Collaborators: name: Sky Labs Inc.
First posted: 2019-07-17
Enrollment: 200
Sex: ALL
Minimum age (years): 19
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study consecutively recruits participants who admitted for elective direct-current cardioversion due to persistent atrial fibrillation.

Brief summary: This study investigates diagnostic performance of a ring-type wearable device for detecting atrial fibrillation. This is a prospective cohort registry study began after the date of IRB approval and until 31th Dec. 2019. The eligible criteria are (1) those who admitted the institution for elective direct-current cardioversion of persistent atrial fibrillation, and (2) aged 19 years or more. The exclusion criteria are (1) those aged less than 19 years, (2) those who are not eligible to perform cardioversion by any reasons. This study aims to recruit 200 participants in total. The participants are required to record photoplethysmographic signals by the ring device in addition to routine procedures of direct-current cardioversion, hence, the study is at minimal risk.

Detailed description: 1. Study title: Diagnostic Performance of a Ring-type Wearable Device Using Deep Learning Analysis of Photoplethysmographic Signals for Detecting Atrial Fibrillation.
2. Study type: Investigator initiated study.
3. Study design: Prospective observational cohort registry design.
4. Sponsor: Sky Labs, Inc., Republic of Korea.
5. The aim of the study: To evaluate diagnostic performance of a ring-type wearable device for detecting atrial fibrillation.
6. Study period: From the date of IRB approval to 31th Dec. 2019.
7. The size of study population: 200 participants
8. The eligibility criteria:

(1) Those with persistent atrial fibrillation (2) Those who admitted the institution for elective direct-current cardioversion (3) Those aged 19 years or more 9. The exclusion criteria:

(1) Those with other forms of cardiac arrhythmias (2) Those aged less than 19 hears (3) Those who are not eligible to perform electrical cardioversion 10. Procedures of the study

1. A participant is under the routine procedures of direct-current cardioversion.
2. On admission, a participant is given with detailed information of the study.
3. After the informed consent, the only additional procedure due to the study is recording photoplethysmographic signal by wearing a ring-type device.
4. Recorded signals are evaluated without encompassing persornal information. 11. Financial benefits to the participants: None.

Eligibility criteria: Inclusion Criteria:

* Those with persistent atrial fibrillation
* Those who admitted for elective direct-current cardioversion
* Those who aged 19 years or more

Exclusion Criteria:

* Those who aged less than 19 years
* Those who had other types of cardiac arrhythmias
* Those who are not eligible to perform electrical cardioversion

Primary outcomes:
measure: Diagnostic performance
timeFrame: Baseline
Secondary outcomes:
Other outcomes:
Locations:
city: Seoul
country: Korea, Republic of
facility: Seoul National university Hostpital
Study officials:
affiliation: Devision of Cardiology, Department of Internal Medicine, Seoul National University Hospital
name: Eue-Keun Choi, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Kwon S, Hong J, Choi EK, Lee B, Baik C, Lee E, Jeong ER, Koo BK, Oh S, Yi Y. Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study. J Med Internet Res. 2020 May 21;22(5):e16443. doi: 10.2196/16443.