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An Explainable Machine-Learning Model to Analyze the Effects of a PCSK9 Inhibitor on Thrombolysis in STEMI Patients

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Abstract

Purpose

Thrombolysis, a timely and simple reperfusion therapy, does not have a sufficiently high success rate in patients with ST-segment elevation acute myocardial infarction (STEMI). This study aimed to explore the effect of a PCSK9 inhibitor on thrombolysis success rate in patients by applying a machine learning (ML) algorithm.

Methods

In total, 531 patients with STEMI treated with thrombolytic therapy were enrolled. Four commonly used ML algorithms were selected to develop models to predict the risk of thrombolysis failure in patients with STEMI. Cluster-based undersampling (CUS) has been proposed to alleviate the effects of imbalanced data. A comprehensive evaluation revealed the optimal model for further interpretation, and the effect of the PCSK9 inhibitor on thrombolysis in patients with STEMI was analyzed by combining the SHapley Additive exPlanations (SHAP) value.

Results

CUS significantly improved model performance. The accuracy, specificity, G-mean, and area under the receiver operating characteristic curve (AUC) were greater than those of the models trained using random undersampling by 10.63%, 14.32%, 2.66%, and 2.06%, respectively. The optimal model achieved 74.81% accuracy, 73.75% sensitivity, 74.95% specificity, 73.77% G-mean, and 80.99% AUC. The feature importance of the PCSK9 inhibitor was ranked as 7/27 with a negative SHAP value, indicating that it can reduce the risk of thrombolysis failure.

Conclusion

The proposed CUS can effectively alleviate class imbalances in medical data. The combination with a PCSK9 inhibitor has the potential to reduce the risk of thrombolysis failure in patients with STEMI, which has significant clinical implications.

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data availability

The data that supports the findings of this study are available upon reasonable request from the corresponding author. The data are not publicly available due to privacy concerns.

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Acknowledgements

This study was funded by the Tianjin Key Research Program of Traditional Chinese Medicine (2022001), the Tianjin Research Innovation Project for Postgraduate Students (2022BKY105), National Natural Science Foundation of China (62206197), and Applied and Basic Research by Multi-input Foundation of Tianjin (21JCYBJC00820). We would like to thank Editage (www.editage.cn) for English language editing.

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Author notes

  1. Pengyu Zhao and Jia Zhou have contributed equally to this work.

Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Pengyu Zhao & Yonghong Hou

  2. Department of Cardiology, Tianjin Chest Hospital, Tianjin, 300222, China

    Jia Zhou, Zhaoying Li, Ping He, Chunjie Li & Jia Zhao

  3. Department of Emergency, Thoracic Clinical College, Tianjin Medical University, Tianjin, 300070, China

    Chang Liu

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  1. Pengyu Zhao
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Contributions

All authors contributed to the conception and design of this study. Material preparation, data collection, and analyses were performed by PZ, JZ, and YH. The first draft of the manuscript was written by PZ, and all authors commented on the following versions of the manuscript. All authors have read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Pengyu Zhao or Jia Zhao.

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Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Ethical Approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the medical ethics committee of Tianjin Chest Hospital (approval number: 2020KY-007-01).

Consent to Participate

This was a retrospective study that used previous clinical treatment records, without using biological samples, without direct contact with patients, and without involving the personal privacy of patients. Therefore, this study was exempted from informed consent by the Medical Ethics Committee of Tianjin Chest Hospital.

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This study does not contain any individual personal data in any form.

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Zhao, P., Zhou, J., Liu, C. et al. An Explainable Machine-Learning Model to Analyze the Effects of a PCSK9 Inhibitor on Thrombolysis in STEMI Patients. J. Med. Biol. Eng. 43, 339–349 (2023). https://doi.org/10.1007/s40846-023-00796-x

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