Construction of a Machine Learning-Based Clopidogrel Resistance Risk Prediction Model.

Journal: Cardiovascular toxicology
Published Date: Jul 14, 2025

Abstract

Clopidogrel is extensively utilized for the prevention and treatment of cardiovascular, cerebrovascular, and other arterial circulation disorders attributed to platelet hyperaggregation. Nevertheless, its antiplatelet efficacy displays substantial individual variability and unpredictability. Our aim was to develop a machine learning model based on clinical data, incorporating various laboratory indicators, to predict the risk of clopidogrel resistance in clinical patients. This study included 1592 cardiovascular disease patients treated with clopidogrel. Potential predictive variables included age, sex, hematological, coagulation, biochemical parameters, and CYP2C19 genetic polymorphisms. Lasso regression and multivariable logistic regression were used for variable selection. Modeling was performed using Logistic Regression, LGBM Classifier, Random Forest Classifier, and SVC machine learning models, followed by model comparison, to ultimately construct the clopidogrel resistance risk prediction model. The clopidogrel resistance rate increased year by year from 2020 to 2022, but decreased slightly in 2023. There was a significant difference in clopidogrel resistance rate among different years (χ = 49.969, P = 0.000). Predictive variables included white blood cell count, hemoglobin level, platelet count, fibrinogen, triglycerides, D-Dimer, mean platelet volume, prothrombin time ratio, uric acid, glycated hemoglobin, and apolipoprotein B. The Random Forest Classifier machine learning method yielded a CR risk prediction model with AUC = 0.8730 and accuracy = 0.8033, demonstrating good predictive capability for identifying the risk of clopidogrel resistance after clinical use of clopidogrel. This study developed a highly predictive clopidogrel resistance risk prediction model, which can assist in clinical decision-making for better treatment strategies.

Authors

  • Ruo-Ying Wang
    Center of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
  • Shui-Di Yan
    Center of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
  • Jian-Qi Zeng
    Department of Neurology, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
  • Tong Mu
    Center of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
  • Ya Yan
    Center of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
  • Yuan-Yi Zhao
    Center of Clinical Laboratory, School of Medicine, Zhongshan Hospital of Xiamen University, Xiamen University, Xiamen, China.
  • Lin Xie
    School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada. xielingtoefl@gmail.com.
  • Li-Li Liu
    State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.

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