Ghajar Mobina, Ghafourian Mandana Sadat, Tarkiani Sara, Tork Atousa Naser, Ramezani Amin, Zolfaghari Behrouz, Palangi Mohammad Ghasemi
Department of Cardiology, Zanjan University of Medical Science, Zanjan, Iran.
Electrical Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
Cardiovasc Drugs Ther. 2025 May 31. doi: 10.1007/s10557-025-07690-5.
To evaluate the impact of statin therapy on warfarin dose requirements in diabetic patients and to assess the performance of various machine learning algorithms in predicting optimal warfarin dosing.
The datasets available for total participants of 628 (216 diabetics and 412 non-diabetic patients) were analyzed. We categorized the patients according to height, weight, gender, race, and age, plasma international normalized ratio (INR) on reported therapeutic dose of warfarin, target INR, warfarin dose, statin therapy, and indications for warfarin. Various models were tested on data of patients from the International Warfarin Pharmacogenetics Consortium (IWPC). Data preprocessing involves structuring and handling missing values. Six predictive models, including least absolute shrinkage and selection operator (LASSO), k-nearest neighbors (KNN), support vector regression (SVR), linear regression (LR), decision tree, and random forest (RF), were employed in predicting optimal warfarin dosage. The best dose for each patient will be predicted using one of the six regression models.
This comparative study showed that the mean (and the standard deviation) of warfarin dose for diabetic and non-diabetic patients were 38.73 (15.37) and 34.50 (18.27) mg per week, respectively. Furthermore, the impact of various statin they use is considered and patient undergoing atorvastatin and rosuvastatin therapy against the necessity of high dose warfarin if the diabetic patients use lovastatin and fluvastatin.
Diabetic patients under statin therapy, considering the specific statin used, require different warfarin dose. Through the application of advanced machine learning, models as dosing predictors may attenuate the adverse effects of warfarin.
评估他汀类药物治疗对糖尿病患者华法林剂量需求的影响，并评估各种机器学习算法在预测华法林最佳剂量方面的性能。
分析了628名参与者（216名糖尿病患者和412名非糖尿病患者）的可用数据集。我们根据身高、体重、性别、种族、年龄、华法林报告治疗剂量时的血浆国际标准化比值（INR）、目标INR、华法林剂量、他汀类药物治疗以及华法林的适应证对患者进行分类。对来自国际华法林药物遗传学联盟（IWPC）患者的数据进行了各种模型测试。数据预处理包括构建和处理缺失值。采用六种预测模型，包括最小绝对收缩和选择算子（LASSO）、k近邻（KNN）、支持向量回归（SVR）、线性回归（LR）、决策树和随机森林（RF）来预测华法林的最佳剂量。将使用六种回归模型之一预测每位患者的最佳剂量。
这项比较研究表明，糖尿病患者和非糖尿病患者华法林剂量的平均值（及标准差）分别为每周38.73（15.37）毫克和34.50（18.27）毫克。此外，考虑了他们使用的各种他汀类药物的影响，以及如果糖尿病患者使用洛伐他汀和氟伐他汀，接受阿托伐他汀和瑞舒伐他汀治疗的患者对抗高剂量华法林必要性的影响。
接受他汀类药物治疗的糖尿病患者，考虑到所使用的特定他汀类药物，需要不同的华法林剂量。通过应用先进的机器学习，作为剂量预测器的模型可能会减轻华法林的不良反应。
