We developed an artificial intelligence approach to predict stable NSTEMI that would give valuable insight to reduce misdiagnosis in the clinical setting.

•

ANN prediction model showed a higher accuracy to predict NSTEMI patients.

Abstract

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.

References

[1]

C.H. Liu, Y.C. Huang, Comparison of STEMI and NSTEMI patients in the emergency department, J. Acute Med. 1 (2011) 1–4.

Highlights

Abstract

Background and Aims

Methods

Results

Conclusion

References

Cited By

Index Terms

Recommendations

ST-Elevation Myocardial Infarction simulations

Evaluation of Percutaneous Coronary Intervention for Acute ST-Segment Elevation Myocardial Infarction with Hypertension by Dynamic Electrocardiogram Feature Data

Comparison of QT interval variability of coronary patients without myocardial infarction with that of patients with old myocardial infarction

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Share

Share this Publication link

Share on social media

Affiliations