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Published on: May 28, 2019
An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain
Chieh-Chen Wu1, Wen-Ding Hsu2, Md Mohaimenul Islam1
1Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
Insights
This study developed an artificial intelligence model to accurately predict non-ST-elevation myocardial infarction (NSTEMI) in chest pain patients, improving diagnosis and reducing misdiagnosis in emergency settings.
Area of Science:
- Cardiology
- Artificial Intelligence in Medicine
- Diagnostic Tools
Background:
- Rising hospital admissions for chest pain globally.
- Lack of specific risk scores to differentiate NSTEMI from non-cardiogenic chest pain.
- Subjectivity and variability in emergency department chest pain diagnosis.
Purpose of the Study:
- To develop an artificial intelligence (AI) approach for predicting stable non-ST-elevation myocardial infarction (NSTEMI).
- To provide valuable insights for reducing misdiagnosis of NSTEMI in clinical settings.
Main Methods:
- Collected data from 268 chest pain patients (aged <20 years) admitted between December 2016 and February 2017.
- Excluded patients with STEMI, prior ACS, or out-of-hospital cardiac arrest.
- Developed and evaluated an artificial neural network (ANN) model using accuracy, sensitivity, specificity, and ROC curve analysis.
Main Results:
- The ANN model achieved 92.86% accuracy and 98.4% AUROC.
- Sensitivity and specificity for NSTEMI prediction were 90.91% and 93.33%, respectively.
- Identified cardiac risk factors, SBP, hemoglobin, QTc, PR interval, AST, ALT, and troponin as independently associated with stable NSTEMI.
Conclusions:
- The developed AI prediction model demonstrates high accuracy in identifying NSTEMI patients.
- This model holds potential for improving disease detection, monitoring, and prognosis in patients with chest pain at risk of AMI.
Background And Aims:
Hospital admission rate for the patients with chest pain has already been increased worldwide but no existing risk score has been designed to stratify non-ST-elevation myocardial infarction (NSTEMI) from non-cardiogenic chest pain. Clinical diagnosis of chest pain in the emergency department is always highly subjective and variable. We, therefore, aimed to develop an artificial intelligence approach to predict stable NSTEMI that would give valuable insight to reduce misdiagnosis in the real clinical setting.
Methods:
A standard protocol was developed to collect data from chest pain patients who had visited the emergency department between December 2016 and February 2017. All the chest pain patients with aged <20 years were primarily included in this study. However, STEMI, previous history of ACS, and out-of-hospital cardiac arrest were excluded from our study. An artificial neural network (ANN) model was then developed to predict NSTEMI patients. The accuracy, sensitivity, specificity, and receiver operating characteristic curve was used to measure the performance of this model.
Results:
A total of 268 chest pain patients were included in this study; of those, 47 (17.5%) was stable NSTEMI, and 221 (82.5%) was unstable angina patients. Serval risk factors such as cardiac risk factor, systolic blood pressure, hemoglobin, corrected QT interval (QTc), PR interval, glutamic-oxaloacetic transaminase, glutamic pyruvic transaminase and troponin were independently associated with stable NSTEMI. The area under the receiver operating characteristic (AUROC) and accuracy of ANN were 98.4, and 92.86. Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value of the ANN model was 90.91, 93.33, 76.92, and 97.67 respectively.
Conclusion:
Our prediction model showed a higher accuracy to predict NSTEMI patients. This model has a potential application in disease detection, monitoring, and prognosis of chest pain at risk of AMI.
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