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A novel machine learning model to predict high on-treatment platelet reactivity on clopidogrel in Asian patients after percutaneous coronary intervention

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Abstract

Background

Various genetic and nongenetic variables influence the high on-treatment platelet reactivity (HTPR) in patients taking clopidogrel.

Aim

This study aimed to develop a novel machine learning (ML) model to predict HTPR in Chinese patients after percutaneous coronary intervention (PCI).

Method

This cohort study collected information on 507 patients taking clopidogrel. Data were randomly divided into a training set (90%) and a testing set (10%). Nine candidate Machine learning (ML) models and multiple logistic regression (LR) analysis were developed on the training set. Their performance was assessed according to the area under the receiver operating characteristic curve, precision, recall, F1 score, and accuracy on the test set. Model interpretations were generated using importance scores by transforming model variables into scaled features and representing in radar plots. Finally, we established a prediction platform for the prediction of HTPR.

Results

A total of 461 patients (HTPR rate: 19.52%) were enrolled in building the prediction model for HTPR. The XGBoost model had an optimized performance, with an AUC of 0.82, a precision of 0.80, a recall of 0.44, an F1 score of 0.57, and an accuracy of 0.87, which was superior to those of LR. Furthermore, the XGBoost method identified 7 main predictive variables. To facilitate the application of the model, we established an XGBoost prediction platform consisting of 7 variables and all variables for the HTPR prediction.

Conclusion

A ML-based approach, such as XGBoost, showed optimum performance and might help predict HTPR on clopidogrel after PCI and guide clinical decision-making. Further validated studies will strengthen this finding.

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Acknowledgements

Authors are grateful to all participants who participated in this study.

Funding

This work was supported by Ren Ji Boost Project of National Natural Science Foundation of China (RJTJ-JX-001), and Cultivation fund of clinical research of Ren Ji Hospital (RJPY-LX-008).

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Author notes

  1. Lan-Ping Ding, Ping Li, and Xiao-Yan Li contributed equally to this work, and should be considered as co-first authors.

Authors and Affiliations

  1. Department of Pharmacy, Jiangsu Province Hospital, The First Affiliated Hospital with Nanjing Medical University, Nanjing, 210009, China

    Lan-Ping Ding

  2. Department of Pharmacy, Women and Children’s Hospital, Qingdao University, Qingdao, 266034, China

    Ping Li

  3. Department of Pharmacy, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China

    Li-Rong Yang, Mang-Mang Pan, Chi Zhang, Yi-Dan Yan, Hou-Wen Lin & Zhi-Chun Gu

  4. Nanjing Ericsson Panda Communication Co. Ltd.,, Nanjing, 211100, China

    Min Zhou

  5. Department of Pharmacy, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Xiao-Ye Li

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  1. Lan-Ping Ding
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Ding, LP., Li, P., Yang, LR. et al. A novel machine learning model to predict high on-treatment platelet reactivity on clopidogrel in Asian patients after percutaneous coronary intervention. Int J Clin Pharm 46, 90–100 (2024). https://doi.org/10.1007/s11096-023-01638-1

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