Acute myocardial infarction risk prediction in emergency chest pain patients: An external validation study

Background

Chest pain is a common symptom that presents to the emergency department (ED), and its causes range from minor illnesses to serious diseases such as acute coronary syndrome. Accurate and timely diagnosis is essential for the efficient management and treatment of these patients.

Objective

This study aims to expand on a model previously developed by the Chi Mei Medical Group (CMMG) Emergency Department in 2020 to predict adverse cardiac events in patients with chest pain. The main goal is to evaluate the accuracy and generalizability of the model through external validation using data from other hospitals.

Methods

The initial model for this study was developed using data from three CMMG-affiliated hospitals in southern Taiwan. We utilized four supervised machine learning algorithms, namely random forest, logistic regression, support-vector clustering, and K-nearest neighbor, to predict the risk of acute myocardial infarction within a one month for emergency chest pain patients. The study used the model with the best area under the curve (AUC), recall and precision for external validation. The external validated data source was data collected from three hospitals associated with Taipei Medical University (TMU) in northern Taiwan. Results: The original best model constructed by CMMG exhibited an AUC of 0.822, an accuracy of 0.740, a recall of 0.741, a precision of 0.566, a specificity of 0.740, and an NPV of 0.861. Subsequently, during the external validation phase, CMMG’s top-performing model demonstrated acceptable validation result with TMU’s data, achieving an AUC of 0.63, an accuracy of 0.661, a recall of 0.593, a precision of 0.243, a specificity of 0.691, and an NPV of 0.900. While the results indicate that the model’s performance varied across different datasets and are not outstanding, the model is still acceptable for clinical application as a preliminary decision-support tool.

Conclusion

This study highlights the importance of external validation to confirm the applicability of the previously developed predictive model in other hospital settings. Although the model shows potential in assessing chest pain patients in the ED, its broad clinical application requires further validation to ensure it can improve patient outcomes and optimize healthcare resource allocation.