Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency
Ziwei Lin1, Tar Choon Aw2, Laurel Jackson3
1Department of Emergency Medicine, Sengkang General Hospital, Singapore.
Insights
The myocardial-ischaemic-injury-index (MI3) algorithm accurately identifies type 1 myocardial infarction in emergency department patients. MI3 demonstrates higher sensitivity and similar negative predictive value compared to existing strategies for ruling out myocardial infarction.
Area of Science:
- Cardiology
- Medical Diagnostics
- Machine Learning in Healthcare
Background:
- Elevated troponin levels are crucial for diagnosing myocardial infarction but can also indicate other conditions.
- Accurate risk stratification is essential for patients presenting with symptoms suggestive of acute coronary syndrome.
- Existing diagnostic algorithms may have limitations in sensitivity and specificity.
Purpose of the Study:
- To evaluate the performance of the myocardial-ischaemic-injury-index (MI3) algorithm in risk-stratifying patients for type 1 myocardial infarction.
- To compare the diagnostic accuracy of MI3 with the European Society of Cardiology (ESC) 0/2-hour algorithm and the 99th percentile upper reference limit (URL) for troponin I (TnI).
Main Methods:
- A prospective study included 1351 adult patients presenting to the emergency department with symptoms suggestive of acute coronary syndrome and no diagnostic ECG changes.
- Serial ECGs and high-sensitivity troponin assays were performed at 0, 2, and 7 hours.
- The primary outcome was the adjudicated diagnosis of type 1 myocardial infarction at 30 days.
Main Results:
- The MI3 algorithm demonstrated high sensitivity (98.9%) and a negative predictive value (NPV) of 99.8% for ruling out type 1 myocardial infarction.
- MI3 outperformed the ESC 0/2-hour algorithm and the 99th percentile URL cut-off strategy in terms of accuracy.
- The 99th percentile URL cut-off strategy exhibited the lowest sensitivity, specificity, positive predictive value, and NPV.
Conclusions:
- The MI3 algorithm is an accurate tool for risk stratification of emergency department patients with suspected myocardial infarction.
- MI3 offers improved diagnostic performance compared to current standard methods, particularly in ruling out myocardial infarction.
- The 99th percentile URL cut-off is the least accurate method for diagnosing myocardial infarction in this patient cohort.
Introduction:
Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers age, sex and cardiac troponin I (TnI) results to risk-stratify patients for type 1 myocardial infarction.
Method:
Patients aged ≥25 years who presented to the emergency department (ED) of Singapore General Hospital with symptoms suggestive of acute coronary syndrome with no diagnostic 12-lead electrocardiogram (ECG) changes were included. Participants had serial ECGs and high-sensitivity troponin assays performed at 0, 2 and 7 hours. The primary outcome was the adjudicated diagnosis of type 1 myocardial infarction at 30 days. We compared the performance of MI3 in predicting the primary outcome with the European Society of Cardiology (ESC) 0/2-hour algorithm as well as the 99th percentile upper reference limit (URL) for TnI.
Results:
There were 1351 patients included (66.7% male, mean age 56 years), 902 (66.8%) of whom had only 0-hour troponin results and 449 (33.2%) with serial (both 0 and 2-hour) troponin results available. MI3 ruled out type 1 myocardial infarction with a higher sensitivity (98.9, 95% confidence interval [CI] 93.4-99.9%) and similar negative predictive value (NPV) 99.8% (95% CI 98.6-100%) as compared to the ESC strategy. The 99th percentile cut-off strategy had the lowest sensitivity, specificity, positive predictive value and NPV.
Conclusion:
The MI3 algorithm was accurate in risk stratifying ED patients for myocardial infarction. The 99th percentile URL cut-off was the least accurate in ruling in and out myocardial infarction compared to the other strategies.
More Related Videos
Related Concept Videos
01:29Electrocardiogram
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
01:28Electrocardiogram Fundamentals
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
01:26Exercise Stress Test
Exercise stress testing, commonly known as a treadmill test, is a noninvasive procedure used to evaluate cardiovascular function and diagnose heart conditions.
Definition
An exercise stress test measures the heart's response to exertion using a treadmill or stationary bicycle. Chest electrodes record the heart's electrical activity through an ECG, and blood pressure is monitored regularly.
Purposes
01:17Imaging Studies for Cardiovascular System I:Echocardiography
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...