What question did this study set out to answer?

To enhance vancomycin therapeutic drug monitoring by developing a deep learning-based two-compartment model.

June 3, 2026Open Access

Improving Vancomycin Therapeutic Drug Monitoring With a Deep Learning–Based Two-Compartment Predictive Model: Development and Validation Study

Key Points

To enhance vancomycin therapeutic drug monitoring by developing a deep learning-based two-compartment model.
Developed the pharmacokinetic recurrent neural network–2 compartment model (PKRNN-2CM) for predicting vancomycin concentrations.
Trained the model using simulated and real-world electronic health record data from 5483 patients.
Evaluated model performance by comparing root mean square errors against a simpler 1-compartment model (PKRNN-1CM).
PKRNN-2CM achieved a root mean square error of 3.04 compared to 4.50 for PKRNN-1CM using simulated data.
In real data, PKRNN-2CM showed a root mean square error of 5.55, while PKRNN-1CM had 5.65; P=0.01.
PKRNN-2CM provided a more accurate estimate of the area under the concentration-time curve to minimum inhibitory concentration ratio.

Abstract

Abstract Background Vancomycin is a widely used antibiotic that requires therapeutic drug monitoring (TDM) for optimized individual dosage. A deep learning–based model, pharmacokinetic recurrent neural network–1 compartment model (PKRNN-1CM), has shown the advantage of leveraging time-series electronic health record data for individualized estimation of vancomycin pharmacokinetic (PK) parameters. While 1-compartment 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 2-compartment models. Objective This study introduces the pharmacokinetic recurrent neural network–2 compartment model (PKRNN-2CM), a novel deep learning–based model designed to improve vancomycin TDM by integrating a 2-compartment PK framework. Methods PKRNN-2CM combines recurrent neural network–driven PK parameter estimation with a 2-compartment PK model to predict vancomycin concentration trajectories. Training on both simulated data and real-world electronic health record data allows for a comprehensive evaluation of its performance. Results Experiments based on simulated data highlight PKRNN-2CM’s superiority over the simpler 1-compartment model, PKRNN-1CM, in predicting vancomycin concentration measurements (root mean square error 3.04 vs 4.50). Application to a real dataset from 5483 patients showcases significant improvement over PKRNN-1CM (root mean square error 5.55 vs 5.65; 2-sample 2-tailed unpaired t test; P =.01), with potential further gains expected with nontrough level measurements. Our simulation also indicates that PKRNN-2CM offers a better estimate of the average area under the concentration-time curve to minimum inhibitory concentration 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 applications in diverse clinical scenarios.

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