基于人工神经网络模型预测华法林服药者国际标准化比值

Prediction of International Normalized Ratio of Warfarin Users Based on Artificial Neural Network Model

目的探究CYP2C9*2、CYP2C9*3、CYP4F2、VKORC11173C>T基因多态性与华法林维持剂量之间的相关性,建立华法林服用者用药后国际标准化比值(international normalized ratio,INR)的人工神经网络预测模型,提高稳定剂量预测准确性。方法回顾性研究2019—2021年收集的214例服用华法林达到稳定抗凝患者的临床资料与华法林药物基因数据,分析临床因素与各基因型对患者华法林稳态剂量的影响;建立机器学习预测模型,采用模拟输入患者华法林剂量计算INR靶值的方式来预测稳态剂量,与直接剂量预测方法以及多元回归模型对比准确性。结果多元回归模型对数据集中患者稳态剂量的预测最佳准确度56.4%,机器学习的预测模型输入稳态剂量预测INR值时的平均绝对误差(mean absolute error,MAE)为0.40,R2为0.81,直接预测剂量时MAE为0.52,R2为0.68,在进行分组训练后误差能够降低20.4%,准确率提高7.3%。结论通过模拟输入药物剂量预测INR的人工神经网络华法林模型能够更准确地预测患者稳态剂量,有利于实现个体化给药,促进精准医疗发展。

OBJECTIVE To explore the correlation between CYP2C9*2,CYP2C9*3,CYP4F2,and VKORC11173C>T polymorphisms and warfarin maintenance dose,and establish an artificial neural network prediction model for international normalized ratio(INR)values after warfarin administration to improve the accuracy of stable dose prediction.METHODS A retrospective study was conducted by collecting clinical data and warfarin pharmacogenetic data from 214 warfarin-treated patients who achieved a stable anticoagulant state from 2019 to 2021.The impact of clinical factors and various gene phenotypes on the patient’s warfarin steady-state dose was analyzed.A machine learning prediction model was established by simulating the input of the patient’s warfarin dose to calculate the INR target and predict the steady-state dose.The accuracy of the model was compared with the direct dose prediction method and the multiple regression model.RESULTS The multiple regression model had the highest accuracy rate of 56.4%for predicting the patient’s steady state dose in the dataset.The machine learning prediction model had a mean absolute error(MAE)of 0.40 and R2 of 0.81 when inputting the steady state dose to predict the INR value.Directly predicting the dose resulted in a MAE of 0.52 and R2 of 0.68.After group training,the error rate decreased by 20.4%and the accuracy increased by 7.3%.CONCLUSION The artificial neural network model for predicting INR using simulated input of warfarin dose can more accurately predict patient’s steady-state dose,which facilitates individualized dosing and promotes the development of precision medicine.