Abstract：
OBJECTIVE: Although there are various model-based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients with periprosthetic joint infection (PJI). This work attempted to develop a machine learning (ML)-based model for predicting VCM trough concentration in adult PJI patients.
METHODS: The dataset of 287 VCM trough concentrations from 130 adult PJI patients was split into a training set (229) and a testing set (58) at a ratio of 8:2, and an independent external 32 concentrations were collected as a validation set. A total of 13 covariates and the target variable (VCM trough concentration) were included in the dataset. A covariate model was respectively constructed by support vector regression, random forest regression and gradient boosted regression trees and interpreted by SHapley Additive exPlanation (SHAP).
RESULTS: The SHAP plots visualized the weight of the covariates in the models, with estimated glomerular filtration rate and VCM daily dose as the 2 most important factors, which were adopted for the model construction. Random forest regression was the optimal ML algorithm with a relative accuracy of 82.8% and absolute accuracy of 67.2% (R2 =.61, mean absolute error = 2.4, mean square error = 10.1), and its prediction performance was verified in the validation set.
CONCLUSIONS: The proposed ML-based model can satisfactorily predict the VCM trough concentration in adult PJI patients. Its construction can be facilitated with only 2 clinical parameters (estimated glomerular filtration rate and VCM daily dose), and prediction accuracy can be rationalized by SHAP values, which highlights a profound practical value for clinical dosing guidance and timely treatment.
METHODS: The dataset of 287 VCM trough concentrations from 130 adult PJI patients was split into a training set (229) and a testing set (58) at a ratio of 8:2, and an independent external 32 concentrations were collected as a validation set. A total of 13 covariates and the target variable (VCM trough concentration) were included in the dataset. A covariate model was respectively constructed by support vector regression, random forest regression and gradient boosted regression trees and interpreted by SHapley Additive exPlanation (SHAP).
RESULTS: The SHAP plots visualized the weight of the covariates in the models, with estimated glomerular filtration rate and VCM daily dose as the 2 most important factors, which were adopted for the model construction. Random forest regression was the optimal ML algorithm with a relative accuracy of 82.8% and absolute accuracy of 67.2% (R2 =.61, mean absolute error = 2.4, mean square error = 10.1), and its prediction performance was verified in the validation set.
CONCLUSIONS: The proposed ML-based model can satisfactorily predict the VCM trough concentration in adult PJI patients. Its construction can be facilitated with only 2 clinical parameters (estimated glomerular filtration rate and VCM daily dose), and prediction accuracy can be rationalized by SHAP values, which highlights a profound practical value for clinical dosing guidance and timely treatment.
摘要：
目的：尽管有多种基于模型的个体化万古霉素（VCM）给药方法，对于患有假体周围关节感染（PJI）的成年患者，很少有报道。这项工作试图开发一种基于机器学习（ML）的模型，用于预测成年PJI患者的VCM谷浓度。
方法：将来自130名成年PJI患者的287个VCM谷浓度的数据集以8：2的比例分为训练集（229个）和测试集（58个），并收集独立的外部32个浓度作为验证集。数据集中包含总共13个协变量和目标变量(VCM谷浓度)。通过支持向量回归分别构建了协变量模型，随机森林回归和梯度增强回归树，并由SHapley加法扩展(SHAP)解释。
结果：SHAP图可视化了模型中协变量的权重，以估计的肾小球滤过率和VCM日剂量为2个最重要的因素，用于模型构建。随机森林回归是最优的ML算法，相对精度为82.8%，绝对精度为67.2%（R2=.61，平均绝对误差=2.4，均方误差=10.1），并在验证集中验证了其预测性能。
结论：提出的基于ML的模型可以令人满意地预测成年PJI患者的VCM谷浓度。它的构建可以促进只有2个临床参数（估计的肾小球滤过率和VCM日剂量），预测精度可以通过SHAP值来合理化，对临床用药指导和及时治疗具有深远的实用价值。
方法：将来自130名成年PJI患者的287个VCM谷浓度的数据集以8：2的比例分为训练集（229个）和测试集（58个），并收集独立的外部32个浓度作为验证集。数据集中包含总共13个协变量和目标变量(VCM谷浓度)。通过支持向量回归分别构建了协变量模型，随机森林回归和梯度增强回归树，并由SHapley加法扩展(SHAP)解释。
结果：SHAP图可视化了模型中协变量的权重，以估计的肾小球滤过率和VCM日剂量为2个最重要的因素，用于模型构建。随机森林回归是最优的ML算法，相对精度为82.8%，绝对精度为67.2%（R2=.61，平均绝对误差=2.4，均方误差=10.1），并在验证集中验证了其预测性能。
结论：提出的基于ML的模型可以令人满意地预测成年PJI患者的VCM谷浓度。它的构建可以促进只有2个临床参数（估计的肾小球滤过率和VCM日剂量），预测精度可以通过SHAP值来合理化，对临床用药指导和及时治疗具有深远的实用价值。
