Prospective Validation Study of AI-based Prediction Algorithm for the Prediction of Paroxysmal Atrial Fibrillation (PROVISION-AF)
September 24, 2024 updated by: Ewha Womans University Mokdong Hospital
Prospective Validation Study of Artificial Intelligence-based Prediction Algorithm for the Prediction of Paroxysmal Atrial Fibrillation
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.
Study Overview
Status
Enrolling by invitation
Conditions
Intervention / Treatment
Detailed 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.
Study Type
Observational
Enrollment (Estimated)
600
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
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Chungbuk, Korea, Republic of, 28644
- Chungbuk National University Hospital
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Gwangju, Korea, Republic of, 61469
- Chonnam National University Hospital
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Gyeonggi-do, Korea, Republic of, 16995
- Yongin Severance Hospital
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Incheon, Korea, Republic of, 21565
- Gachon University Gil Medical Center
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Seoul, Korea, Republic of, 02447
- Kyung Hee University Hospital
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Seoul, Korea, Republic of, 02841
- Korea University Anam Hospital
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Seoul, Korea, Republic of, 06973
- Chung-Ang University Hospital
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Seoul, Korea, Republic of, 07985
- Ewha Womans University Mokdong Hospital
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Seoul, Korea, Republic of, 04763
- Hanyang University Seoul Hospital
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Seoul, Korea, Republic of, 08308
- Korea University Guro Hospital
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Seoul, Korea, Republic of, 07804
- Ewha Womans University Seoul Hospital
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Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
20 years and older (Adult, Older Adult)
Accepts Healthy Volunteers
Yes
Sampling Method
Non-Probability Sample
Study Population
Participants are selected from adults above age of 20 with their consent with a target for patients who come to the hospital with arrhythmia symptoms from an outpatient clinic or who are admitted to a hospital.
Description
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.
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
Low risk group for paroxysmal atrial fibrillation
Subject patients are above 20 in age who are hospitalized in our hospital or outpatients with arrhythmia symptoms after the clinical research approval.
The sinus rhythm electrocardiogram at the time of the patient's participation in the study is put into the artificial intelligence prediction algorithm, and the risk stratification results are blinded and are not informed to both the research director and the subjects.
For the low-risk group, after attaching the wearable electrocardiogram to the subject, the electrocardiogram recorded a week later is analyzed to confirm the occurrence of atrial fibrillation.
|
It is a 9.2g wearable electrocardiogram device, mobiCARE, in the form of a patch, and the model name is MC200M.
|
|
High risk group for paroxysmal atrial fibrillation
Subject patients are above 20 in age who are hospitalized in our hospital or outpatients with arrhythmia symptoms after the clinical research approval.
The sinus rhythm electrocardiogram at the time of the patient's participation in the study is put into the artificial intelligence prediction algorithm, and the risk stratification results are blinded and are not informed to both the research director and the subjects.
For the highrisk group, after attaching the wearable electrocardiogram to the subject, the electrocardiogram recorded a week later is analyzed to confirm the occurrence of atrial fibrillation.
|
It is a 9.2g wearable electrocardiogram device, mobiCARE, in the form of a patch, and the model name is MC200M.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Occurrence of paroxysmal AF
Time Frame: 1 week
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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.
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1 week
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Performance verification of AI prediction model
Time Frame: 1 week
|
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.
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1 week
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Other Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Predictive capabilities of AI prediction model compared to expert cardiologists
Time Frame: 10 minute
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The predictive capabilities of the artificial intelligence prediction algorithm in risk stratification will be compared to the risk stratification proficiency of the experts. Each expert will be required to answer a questionnaire consisting of 30 ECGs to classify them as high risk or low risk. The questionnaire is composed of three components: Q1. Atrial fibrillation/flutter risk prediction based on normal sinus rhythm 12-lead ECG and participant's clinical data (Age, gender, comorbidities, laboratory result, EHRA Symptom Score, etc.). The laboratory result could include BUN/Cr, eGFR, liver function test, lipid profile test. Q2. Further plan required for identification of atrial fibrillation/flutter. Q3. Decisive evidence of atrial fibrillation/flutter risk prediction. The evidence could include normal sinus rhythm 12-lead ECG or participant's clinical data. |
10 minute
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Collaborators
Investigators
- Study Director: Sumi Jung, Ewha Womans University Mokdong Hospital
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- 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.
- 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.
- 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.
- 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.
- 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. doi: 10.1038/s41467-020-16172-1.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
October 14, 2022
Primary Completion (Estimated)
December 31, 2024
Study Completion (Estimated)
December 31, 2025
Study Registration Dates
First Submitted
January 20, 2023
First Submitted That Met QC Criteria
February 2, 2023
First Posted (Actual)
February 13, 2023
Study Record Updates
Last Update Posted (Actual)
September 26, 2024
Last Update Submitted That Met QC Criteria
September 24, 2024
Last Verified
September 1, 2024
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- PROVISION-AF
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
NO
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
product manufactured in and exported from the U.S.
No
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