Prospective Validation Study of AI-based Prediction Algorithm for the Prediction of Paroxysmal Atrial Fibrillation

Atrial Fibrillation Paroxysmal Clinical Trial

— PROVISION-AF  

Official title:

Prospective Validation Study of Artificial Intelligence-based Prediction Algorithm for the Prediction of Paroxysmal Atrial Fibrillation

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT05725187
• Other study ID #: PROVISION-AF
• Status: Enrolling by invitation
• Start date: October 14, 2022
• Est. completion date: December 31, 2025

Study information

• Verified date: February 2023
• Source: Ewha Womans University Mokdong Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The purpose of this study is to predict the occurrence of paroxysmal atrial fibrillation by finding high-risk group from normal sinus rhythm ECG through artificial intelligence-based prediction algorithm.

Description:

This study is a multi-center, prospective observational validation study. Patients aged 18 or above who are hospitalized at our hospital or who visited the outpatient clinic with arrhythmia symptoms (such as palpitation) after the clinical research approval will be enrolled. The normal sinus rhythm electrocardiogram (ECG) at the time of participation in the study is recorded and put into the artificial intelligence prediction algorithm. The result of risk stratification is blinded and will not be informed to both the research director and subjects. After applying wearable devices to the subject, the ECG recorded for the first week is analyzed to confirm the occurrence of paroxysmal atrial fibrillation (the gold standard for diagnosis of atrial fibrillation). When the wearable devices are removed, the 12 lead electrocardiogram will be taken again, and if it shows normal sinus rhythm electrocardiogram, then it will be put into the artificial intelligence prediction algorithm to calculate the result as well.

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 650
• Est. completion date: December 31, 2025
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 20 Years and older

Eligibility

Inclusion Criteria:

• Participants must be above 20 in age
• Participants are patients with symptom of arrhythmia who visited outpatient clinic or who have been hospitalized

Exclusion Criteria:

• Excluding patients with cardiac implantable electronic device such as pacemakers, implantable defibrillators (ICD), or cardiac resynchronization therapy (CRT).
• Excluding pregnant women and lactating women.

Related Conditions & MeSH terms

• Atrial Fibrillation
• Atrial Fibrillation Paroxysmal

Intervention

Device:
• MobiCare
It is a 9.2g wearable electrocardiogram device, mobiCARE, in the form of a patch, and the model name is MC200M.

Locations

Country	Name	City	State
Korea, Republic of	Chungbuk National University Hospital	Chungbuk
Korea, Republic of	Chonnam National University Hospital	Gwangju
Korea, Republic of	Yongin Severance Hospital	Gyeonggi-do
Korea, Republic of	Gachon University Gil Medical Center	Incheon
Korea, Republic of	Chung-Ang University Hospital	Seoul
Korea, Republic of	Ewha Womans University Mokdong Hospital	Seoul
Korea, Republic of	Ewha Womans University Seoul Hospital	Seoul
Korea, Republic of	Hanyang University Seoul Hospital	Seoul
Korea, Republic of	Korea University Anam Hospital	Seoul
Korea, Republic of	Korea University Guro Hospital	Seoul
Korea, Republic of	Kyung Hee University Hospital	Seoul

Sponsors (2)

Lead Sponsor	Collaborator
Ewha Womans University Mokdong Hospital	Ewha Womans University Seoul Hospital

Country where clinical trial is conducted

Korea, Republic of, 

References & Publications (5)

Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S, Friedman PA. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019 Sep 7;394(10201):861-867. doi: 10.1016/S0140-6736(19)31721-0. Epub 2019 Aug 1. — View Citation

Noseworthy PA, Attia ZI, Behnken EM, Giblon RE, Bews KA, Liu S, Gosse TA, Linn ZD, Deng Y, Yin J, Gersh BJ, Graff-Radford J, Rabinstein AA, Siontis KC, Friedman PA, Yao X. Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. Lancet. 2022 Oct 8;400(10359):1206-1212. doi: 10.1016/S0140-6736(22)01637-3. Epub 2022 Sep 27. — View Citation

Park J, Shim J, Lee JM, Park JK, Heo J, Chang Y, Song TJ, Kim DH, Lee HA, Yu HT, Kim TH, Uhm JS, Kim YD, Nam HS, Joung B, Lee MH, Heo JH, Pak HN; RAFAS Investigators*. Risks and Benefits of Early Rhythm Control in Patients With Acute Strokes and Atrial Fibrillation: A Multicenter, Prospective, Randomized Study (the RAFAS Trial). J Am Heart Assoc. 2022 Feb;11(3):e023391. doi: 10.1161/JAHA.121.023391. Epub 2022 Jan 19. — View Citation

Ribeiro AH, Ribeiro MH, Paixao GMM, Oliveira DM, Gomes PR, Canazart JA, Ferreira MPS, Andersson CR, Macfarlane PW, Meira W Jr, Schon TB, Ribeiro ALP. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020 Apr 9;11(1):1760. doi: 10.1038/s41467-020-15432-4. Erratum In: Nat Commun. 2020 May 1;11(1):2227. — View Citation

Willems S, Borof K, Brandes A, Breithardt G, Camm AJ, Crijns HJGM, Eckardt L, Gessler N, Goette A, Haegeli LM, Heidbuchel H, Kautzner J, Ng GA, Schnabel RB, Suling A, Szumowski L, Themistoclakis S, Vardas P, van Gelder IC, Wegscheider K, Kirchhof P. Systematic, early rhythm control strategy for atrial fibrillation in patients with or without symptoms: the EAST-AFNET 4 trial. Eur Heart J. 2022 Mar 21;43(12):1219-1230. doi: 10.1093/eurheartj/ehab593. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Occurrence of paroxysmal AF	The AI prediction algorithm classifies patients into high-risk and low-risk categories for predicting paroxysmal atrial fibrillation within a week, based on ECG recordings of those with normal sinus rhythm. The accuracy of the prediction will be assessed through the use of a wearable device that records occurrence of paroxysmal atrial fibrillation over the course of a week.	1 week
Secondary	Performance verification of AI prediction model	The artificial intelligence prediction algorithm categorizes patients into high-risk and low-risk groups when predicting paroxysmal atrial fibrillation within one week based on normal sinus rhythm ECG data. The AI prediction algorithm's performance is assessed based on the data obtained from the primary outcome, which involves confirming whether atrial fibrillation recorded through a week-long use of a wearable device. We will gauge the algorithm's effectiveness by evaluating its predictive abilities, encompassing sensitivity, specificity, positive predictive rate, negative predictive rate, and the F1 score.	1 week