背景:万古霉素谷浓度与临床疗效和毒性密切相关。由于成熟期间显著的个体间变异性和快速的生理变化,预测儿科患者中的万古霉素谷浓度是具有挑战性的。
目的:本研究旨在开发一种机器学习模型来预测万古霉素谷浓度,并使用ML算法确定4岁以下儿童患者的最佳给药方案。
方法:2017年1月至2020年3月进行单中心回顾性观察性研究。纳入接受静脉注射万古霉素并接受治疗药物监测的儿科患者。七个ML模型[线性回归,梯度增强决策树,支持向量机,决策树,随机森林,装袋,和极端梯度提升(XGBoost)]是使用31个变量开发的。性能指标,包括R平方(R2),均方误差(MSE),均方根误差(RMSE),和平均绝对误差(MAE)进行了比较,并对重要特征进行了排名。
结果:该研究包括来自112名患者的120个合格的谷浓度测量。其中,84次测量用于训练,36次用于测试。在测试的七个算法中,XGBoost表现出最好的性能,具有较低的预测误差和较高的拟合优度(MAE=2.55,RMSE=4.13,MSE=17.12,R2=0.59)。血尿素氮,血清肌酐,和肌酐清除率被确定为万古霉素谷浓度的最重要预测因子。
结论:开发了一种XGBoostML模型来预测万古霉素谷浓度,并作为决策支持技术帮助药物治疗预测。
BACKGROUND: Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting
vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiological changes during maturation.
OBJECTIVE: This study aimed to develop a machine learning model to predict
vancomycin trough concentrations and determine optimal dosing regimens for pediatric patients < 4 years of age using ML algorithms.
METHODS: A single-center retrospective observational study was conducted from January 2017 to March 2020. Pediatric patients who received intravenous
vancomycin and underwent therapeutic drug monitoring were enrolled. Seven ML models [linear regression, gradient boosted decision trees, support vector machine, decision tree, random forest, Bagging, and extreme gradient boosting (XGBoost)] were developed using 31 variables. Performance metrics including R-squared (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were compared, and important features were ranked.
RESULTS: The study included 120 eligible trough concentration measurements from 112 patients. Of these, 84 measurements were used for training and 36 for testing. Among the seven algorithms tested, XGBoost showed the best performance, with a low prediction error and high goodness of fit (MAE = 2.55, RMSE = 4.13, MSE = 17.12, and R2 = 0.59). Blood urea nitrogen, serum creatinine, and creatinine clearance rate were identified as the most important predictors of vancomycin trough concentration.
CONCLUSIONS: An XGBoost ML model was developed to predict
vancomycin trough concentrations and aid in drug treatment predictions as a decision-support technology.