Acute myocardial infarction risk prediction in emergency chest pain patients: An external validation study
Ching-Hung Chang1, Phung-Anh Nguyen2, Chien-Cheng Huang3
1Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan.
Insights
A predictive model for chest pain patients was externally validated in new hospitals. While performance varied, the model remains a potentially useful preliminary tool for emergency departments.
Area of Science:
- Cardiology
- Emergency Medicine
- Artificial Intelligence in Healthcare
Background:
- Chest pain is a frequent emergency department (ED) complaint with diverse causes, necessitating accurate diagnosis.
- Effective management of chest pain patients relies on timely and precise diagnostic tools.
Purpose of the Study:
- To externally validate a machine learning model developed by Chi Mei Medical Group (CMMG) for predicting adverse cardiac events in chest pain patients.
- To assess the generalizability and accuracy of the CMMG model using data from hospitals outside its network.
Main Methods:
- Four supervised machine learning algorithms were employed to develop an initial model predicting acute myocardial infarction risk in ED chest pain patients.
- The best-performing model (based on AUC, recall, precision) was selected for external validation using data from Taipei Medical University (TMU) affiliated hospitals.
- External validation involved comparing the model's predictive performance on the TMU dataset against its performance on the original CMMG dataset.
Main Results:
- The original CMMG model achieved an AUC of 0.822 and accuracy of 0.740.
- External validation with TMU data yielded an AUC of 0.63 and accuracy of 0.661, indicating reduced performance.
- Despite performance variations, the model's results were deemed acceptable for preliminary clinical decision support in emergency settings.
Conclusions:
- External validation is crucial for confirming the real-world applicability of predictive models across different healthcare settings.
- The validated model shows potential for assisting in the assessment of chest pain patients in the ED.
- Further validation is recommended to enhance the model's clinical utility and optimize resource allocation.
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.
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