TY - JOUR
T1 - An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain
AU - Wu, Chieh Chen
AU - Hsu, Wen Ding
AU - Islam, Md Mohaimenul
AU - Poly, Tahmina Nasrin
AU - Yang, Hsuan Chia
AU - Nguyen, Phung Anh (Alex)
AU - Wang, Yao Chin
AU - Li, Yu Chuan (Jack)
N1 - Funding Information:
We would like to thank our college who is a native speaker for English checking and edition. This work was financially supported by the “TMU Research Center of Cancer Translational Medicine” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and Ministry of Health and Welfare ( TMU DP2-107-21121-01-A-06 ).
Publisher Copyright:
© 2019
PY - 2019/5/1
Y1 - 2019/5/1
N2 - 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.
AB - 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.
KW - Acute coronary syndrome
KW - Artificial neural network
KW - Chest pain
KW - Non-ST elevated MI
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U2 - 10.1016/j.cmpb.2019.01.013
DO - 10.1016/j.cmpb.2019.01.013
M3 - Article
C2 - 31046985
AN - SCOPUS:85063111784
SN - 0169-2607
VL - 173
SP - 109
EP - 117
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
ER -