Atrial Fibrillation Paroxysmal
Conditions
Keywords
artificial intelligence, electrocardiogram
Brief 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.
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.
Interventions
DEVICEMobiCare
It is a 9.2g wearable electrocardiogram device, mobiCARE, in the form of a patch, and the model name is MC200M.
Sponsors
Ewha Womans University Seoul Hospital
Ewha Womans University Mokdong Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Occurrence of paroxysmal AF | 1 week | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Performance verification of AI prediction model | 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. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Predictive capabilities of AI prediction model compared to expert cardiologists | 10 minute | 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. |
Countries
South Korea
Outcome results
None listed