Myocardial Infarction or Chest Pain
Conditions
Keywords
Deep Learning
Brief summary
This study is a prospective multicenter observational study for external validation and model advancement of a deep learning based 12-lead electrocardiogram analysis algorithm targeting adult patients presenting to the emergency department with chest pain and acute myocardial infarction equivalent symptoms. About 9,000 adult patients will be enrolled at 20 emergency medical centers in Korea. Artificial intelligence algorithms are manufactured by Medical AI Co., Ltd. It is an advanced version based on the model developed and published in 2020. It had the diagnostic performance of area under the receiver operating curve 0.901 and 0.951 for acute myocardial infarction and ST-segment elevation myocardial infarction, respectively. The primary endpoint is a diagnosis of acute myocardial infarction on the day of the emergency center visit, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration will begin at centers that have been approved by the Institutional Review Board. This is the first prospective multicenter emergency department validation study for a 12-lead electrocardiogram artificial intelligence algorithm to diagnose acute myocardial infarction. This study will give insight into the direction of future development by verifying whether the deep learning algorithm works well for patients visiting the real-world adult emergency medical center.
Interventions
None listed
Sponsors
Medical AI Co., Ltd
CHA University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Adults over 18 years of age with suspected chest pain and acute myocardial infarction * The onset or worsening of the symptom occurs within 24 hours
Exclusion criteria
* Out-of-hospital cardiac arrest (OHCA): patients with sustained (\>20 minutes) return-of-spontaneous-circulation are not excluded * Patients in whom acute myocardial infarction can be clearly excluded, such as pneumothorax and traumatic chest pain
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnosis of acute myocardial infarction (Type 1, 2) | Index admission | Accuracy metrics include area under the receiver operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value, along with a 95% confidence interval. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Major adverse cardiovascular event (MACE) | 30-day after index admission | MACE is defined as death, myocardial infarction, stroke, target-vessel revascularization, or stent thrombosis occurring within 30 days of index visit. Accuracy metrics include area under the receiver operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value, along with a 95% confidence interval. |
Other
| Measure | Time frame | Description |
|---|---|---|
| AI ECG analysis versus clinical risk score (HEART score) | Index admission, 30-day after index admission | HEART score is an acute coronary syndrome risk calculation tool introduced in recent guidelines, and consists of history, electrocardiogram, age, risk factor, and troponin. On a scale of 0 to 10, the higher the score, the higher the risk. Generally, a score of 7 or higher is classified as high risk. The prediction performance for the primary and secondary endpoints of the HEART score will be analyzed by comparing it with AI-ECG analysis. |
| AI ECG analysis versus clinical risk score (GRACE 2.0 score) | Index admission, 30-day after index admission | GRACE 2.0 score is a tool for estimating short- and long-term risk in acute coronary syndrome. A low score indicates low risk, and a high score indicates a high risk group. Based on recent guidelines, patients with acute coronary syndrome are categorized as low (≤108 GRACE score), medium (109-140 GRACE score) and high risk (\>140 GRACE score). The prediction performance for the primary and secondary endpoints of the GRACE 2.0 score will be analyzed by comparing it with AI-ECG analysis. |
| AI ECG analysis versus cardiac biomarker | Index admission | The performance of cardiac biomarker (hs-troponin I or T) for the diagnosis of acute myocardial infarction during index visit will be compared with AI ECG analysis diagnostic performance. |
| AI ECG analysis versus physician's ECG score | Index admission | The attending physician determines a score between 0 and 10 for the probability of acute myocardial infarction based on the results of the initial patient assessment and the first 12-lead ECG. A score of 0 indicates that the probability of acute myocardial infarction is 0%, and a score of 10 indicates a probability of 100%. The diagnostic performance of this score for acute myocardial infarction will be compared with AI ECG analysis. |
Countries
South Korea
Outcome results
None listed