LASSO Regression-Based Diagnosis of Acute ST-Segment Elevation Myocardial Infarction (STEMI) on Electrocardiogram
Lin Wu1,2, Bin Zhou1, Dinghui Liu1
1Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.
Insights
A machine learning model using Least Absolute Shrinkage and Selection Operator (LASSO) effectively diagnoses ST-segment elevation myocardial infarction (STEMI) from electrocardiogram (ECG) data. This AI tool shows high accuracy, aiding early STEMI detection and identifying affected arteries.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Electrocardiogram (ECG) is crucial for diagnosing ST-segment elevation myocardial infarction (STEMI).
- Machine learning (ML) applications for diagnosing complex STEMI cases, including arrhythmias and infarct-related arteries, are still emerging, especially using real-world data.
- Existing diagnostic tools require further enhancement for accuracy and efficiency.
Purpose of the Study:
- To develop and validate a machine learning (ML) model utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) for automated STEMI diagnosis based on ECG features.
- To assess the model's performance in identifying STEMI and differentiating infarct-related arteries, specifically the left anterior descending (LAD) artery.
- To compare the diagnostic accuracy of the LASSO model against various levels of medical professionals.
Main Methods:
- A LASSO regression model was developed using 180 automatic ECG features from 318 STEMI patients and 502 controls.
- The model was trained, validated internally, and tested on internal and external datasets.
- Performance was evaluated using Area Under the Curve (AUC) and compared with cardiologists, residents, interns, and emergency physicians.
Main Results:
- The LASSO model achieved high accuracy in identifying STEMI (AUCs of 0.94 and 0.93 in internal and external testing, respectively).
- Its performance in STEMI diagnosis was comparable to experienced cardiologists (AUC: 0.92) and superior to less experienced physicians.
- The model demonstrated strong performance in identifying LAD artery involvement (AUCs of 0.92 and 0.98).
Conclusions:
- The developed LASSO regression model offers a highly accurate, automated diagnostic tool for STEMI based on ECG features.
- This ML approach shows potential for improving the early diagnosis of STEMI and identifying LAD artery disease.
- The model serves as a valuable assisting diagnostic tool, potentially enhancing clinical decision-making in emergency settings.
Abstract:
Electrocardiogram (ECG) is an important tool for the detection of acute ST-segment elevation myocardial infarction (STEMI). However, machine learning (ML) for the diagnosis of STEMI complicated with arrhythmia and infarct-related arteries is still underdeveloped based on real-world data. Therefore, we aimed to develop an ML model using the Least Absolute Shrinkage and Selection Operator (LASSO) to automatically diagnose acute STEMI based on ECG features. A total of 318 patients with STEMI and 502 control subjects were enrolled from Jan 2017 to Jun 2019. Coronary angiography was performed. A total of 180 automatic ECG features of 12-lead ECG were input into the model. The LASSO regression model was trained and validated by the internal training dataset and tested by the internal and external testing datasets. A comparative test was performed between the LASSO regression model and different levels of doctors. To identify the STEMI and non-STEMI, the LASSO model retained 14 variables with AUCs of 0.94 and 0.93 in the internal and external testing datasets, respectively. The performance of LASSO regression was similar to that of experienced cardiologists (AUC: 0.92) but superior (p < 0.05) to internal medicine residents, medical interns, and emergency physicians. Furthermore, in terms of identifying left anterior descending (LAD) or non-LAD, LASSO regression achieved AUCs of 0.92 and 0.98 in the internal and external testing datasets, respectively. This LASSO regression model can achieve high accuracy in diagnosing STEMI and LAD vessel disease, thus providing an assisting diagnostic tool based on ECG, which may improve the early diagnosis of STEMI.
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