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

PKRNN-2CM shows a statistically significant improvement in predicting vancomycin concentrations compared to the simpler model, but the clinical relevance of this improvement remains to be established.

Summary and findings

This study evaluates a deep learning-based two-compartment predictive model (PKRNN-2CM) for improving vancomycin therapeutic drug monitoring (TDM). The model was tested on simulated data and a dataset from 5483 patients. The results indicated a root mean square error of 5.55 for PKRNN-2CM compared to 5.65 for the PKRNN-1CM model, with a p-value of 0.01.

Abstract

<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.

Key findings

• Not reported in abstract.

Limitations

• Not reported in abstract.

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