JMIR Publications

ABSTRACT

Background:

Vancomycin 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).

Objective:

This study introduces PKRNN-2CM, a novel deep learning-based model designed to improve vancomycin TDM by integrating a two-compartment PK framework.

Methods:

PKRNN-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.

Results:

Experiments based on simulated data highlight PKRNN-2CM's superiority over the simpler 1CM model PKRNN-1CM in predicting vancomycin concentration measurements (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. Our simulation also indicates that PKRNN-2CM offers a better estimate of the average area under the concentration-time curve (AUC) to minimum inhibitory concentration (MIC) ratio, a more clinically relevant measure.

Conclusions:

PKRNN-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.