Atrial Fibrillation Paroxysmal Clinical Trial
— PROVISION-AFOfficial title:
Prospective Validation Study of Artificial Intelligence-based Prediction Algorithm for the Prediction of Paroxysmal Atrial Fibrillation
Verified date | February 2023 |
Source | Ewha Womans University Mokdong Hospital |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
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.
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. |
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 |
Lead Sponsor | Collaborator |
---|---|
Ewha Womans University Mokdong Hospital | Ewha Womans University Seoul Hospital |
Korea, Republic of,
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
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 |
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