A deep-learning-based two-compartment predictive model (PKRNN-2CM) for vancomycin therapeutic drug monitoring

Abstract

AbstractObjectiveVancomycin is a widely used antibiotic that requires therapeutic drug monitoring (TDM) for optimized individual dosage. The deep learning-based model PKRNN-1CM has shown the advantage of leveraging time series electronic health record (EHR) data for individualized estimation of vancomycin pharmacokinetic (PK) parameters. While one-compartment (1CM) PK models are commonly used because of their simplicity and previous trough-based clinical practices for dose adjustment, the pre-deep learning literature suggests the superiority of two-compartment models (2CM). Motivated by this, we introduce a novel deep-learning-based approach, PKRNN-2CM, for vancomycin TDM.MethodsPKRNN-2CM combines RNN-driven PK parameter estimation with a 2CM PK model to predict vancomycin concentration trajectories. Training on both simulated data and real-world EHR data allows for a comprehensive evaluation of its performance.ResultsExperiments based on simulated data highlight PKRNN-2CM’s superiority over the simpler 1CM model PKRNN-1CM (PKRNN-2CM RMSE=1.30, PKRNN-1CM RMSE=2.50). Application to real data showcases significant improvement over PKRNN-1CM (PKRNN-2CM RMSE=5.62, PKRNN-1CM RMSE=5.84, two-sample unpaired t-test p-value=0.01), with potential further gains expected with non-trough level measurements.ConclusionPKRNN-2CM is an important improvement in vancomycin TDM, demonstrating enhanced accuracy and performance compared to the PKRNN-1CM model. This deep learning model holds potential for future individualized vancomycin TDM optimization and broader application in diverse clinical scenarios.