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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Atrial Fibrillation Paroxysmal

Intervention / Treatment
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* Device: MobiCare


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 20 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Low risk group for paroxysmal atrial fibrillation<br>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. | Device: MobiCare<br>* It is a 9.2g wearable electrocardiogram device, mobiCARE, in the form of a patch, and the model name is MC200M.<br>|
| High risk group for paroxysmal atrial fibrillation<br>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. | Device: MobiCare<br>* It is a 9.2g wearable electrocardiogram device, mobiCARE, in the form of a patch, and the model name is MC200M.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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artificial intelligence, electrocardiogram