{Reference Type}: Journal Article
{Title}: Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model.
{Author}: Chen YW;Lin XK;Huang C;Wu W;Lin WW;Chen S;Lu ZX;You YY;Liu ZJ;
{Journal}: Br J Clin Pharmacol
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jun 6
{Factor}: 3.716
{DOI}: 10.1111/bcp.16112
{Abstract}: <B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>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).<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.