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

Autor: Chang CH; Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan., Nguyen PA; Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei City, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei City, Taiwan; Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei City, Taiwan; Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan., Huang CC; Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan; Department of Emergency Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan; Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan., Liu CF; Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan., Melisa S; International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei City, Taiwan., Chen CJ; Department of Information Systems, Chi Mei Medical Center, Tainan, Taiwan., Hsu CC; Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan., Lin HJ; Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan; Department of Emergency Medicine, Taipei Medical University, Taipei, Taiwan., Hsu MH; Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan., Shih CM; Department of Cardiology, Taipei Medical University Hospital, Taipei Medical University, Taipei City, Taiwan., Liu JC; Department of Cardiology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan., Yang HY; Department of Cardiology, Taipei Municipal Wanfang Hospital, Taipei City, Taiwan., Hsu JC; Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei City, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei City, Taiwan; Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei City, Taiwan; International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei City, Taiwan. Electronic address: jasonhsu@tmu.edu.tw.
Jazyk: angličtina
Zdroj: International journal of medical informatics [Int J Med Inform] 2025 Jan; Vol. 193, pp. 105683. Date of Electronic Publication: 2024 Nov 01.
DOI: 10.1016/j.ijmedinf.2024.105683
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Jason C. Hsu reports financial support was provided by Chi Mei Medical Group and Taipei Medical University Academic Collaborative Research Project. Jason C. Hsu reports financial support was provided by Taiwan Ministry of Science and Technology Council. Jason C. Hsu reports financial support was provided by Industry-Academia Collaboration between Taipei Medical University and AstraZeneca. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper].
(Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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