| 摘要: |
| 目的 探究CYP2C9*2、CYP2C9*3、CYP4F2、VKORC1 1173C>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。 |
| 关键词: 华法林 人工神经网络 基因多态性 预测国际标准化比值 |
| DOI：10.13748/j.cnki.issn1007-7693.20230227 |
| 分类号:R969.3 |
| 基金项目:上海市杨浦区医学重点学科基金(YP19ZB03)；上海市科技计划项目(22692116400) |
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| Prediction of International Normalized Ratio of Warfarin Users Based on Artificial Neural Network Model |
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MAO Delong1, ZHUANG Wenfang2
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1.School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;2.Medical Laboratory, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai 200438, China
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| Abstract: |
| OBJECTIVE To explore the correlation between CYP2C9*2, CYP2C9*3, CYP4F2, and VKORC1 1173C>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. |
| Key words: warfarin artificial neural network genetic polymorphism predict international normalized ratio |
