Artificial Intelligence Enabled Rapid Identification of ST-Elevation Myocardial Infarction With Electrocardiogram - ARISE
Contribution To Literature:
The ARISE trial showed that AI-ECG interpretation shortens the time from ECG acquisition to arriving in the cath lab.
Description:
The goal of the trial was to evaluate artificial intelligence (AI)-interpreted electrocardiogram (ECG) (AI-ECG) compared with usual care among patients in the emergency department who received an ECG.
Study Design
- Randomized
- Parallel
- Blinded
Patients presenting to the emergency department were randomized to AI-ECG (n = 21,989) vs. usual care (n = 22,005).
- Total number of enrollees: 43,994
- Duration of follow-up: hospitalization
- Mean patient age: 60 years
- Percentage female: 50%
Inclusion criteria:
- Patients ≥18 years of age who presented to emergency department or inpatient department
- Patients who received ≥1 ECG without history of coronary angiography within 3 days
Principal Findings:
The primary outcome, time from ECG to the cath lab, was 43.3 minutes in the AI-ECG group vs. 52.3 minutes in the usual care group (p = 0.003).
Secondary outcomes:
- Ejection fraction: 45.0% in the AI-ECG group vs. 47.5% in the usual care group (p = 0.49)
- Length of hospitalization: 5.0 days in the AI-ECG group vs. 5 days in the usual care group (p = 0.85)
- Positive predictive value: 88.0%
- Negative predictive value: 99.9%
Interpretation:
Among patients who received an ECG in the emergency department, AI-ECG shortens the time from ECG acquisition to arrival in the cath lab. Ejection fraction and length of hospitalization was similar between treatment groups.
References:
Presented by Dr. Chin-Sheng Lin at the American Heart Association Scientific Sessions, Philadelphia, PA, November 13, 2023.
Clinical Topics: Arrhythmias and Clinical EP, Acute Coronary Syndromes
Keywords: AHA23, Artificial Intelligence, Electrocardiography
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