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A New Algorithm Optimized for Initial Dose Settings of Vancomycin Using Machine Learning

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Title: A New Algorithm Optimized for Initial Dose Settings of Vancomycin Using Machine Learning
Authors: Imai, Shungo Browse this author
Takekuma, Yoh Browse this author →KAKEN DB
Miyai, Takayuki Browse this author
Sugawara, Mitsuru Browse this author →KAKEN DB
Keywords: machine learning
decision tree analysis
vancomycin
therapeutic drug monitoring
Issue Date: Jan-2020
Publisher: The Pharmaceutical Society of Japan (日本薬学会)
Journal Title: Biological & pharmaceutical bulletin
Volume: 43
Issue: 1
Start Page: 188
End Page: 193
Publisher DOI: 10.1248/bpb.b19-00729
Abstract: This study aimed to construct an optimal algorithm for initial dose settings of vancomycin (VCM) using machine learning (ML) with decision tree (DT) analysis. Patients who were administered intravenous VCM and underwent therapeutic drug monitoring (TDM) at the Hokkaido University Hospital were enrolled. The study period was November 2011 to March 2019. In total, 654 patients were included in the study. Patients were divided into two groups, training (patients who received VCM from November 2011 to December 2017; n = 496) and testing (patients who received VCM from January 2018 to March 2019; n = 158) groups. For the training group, DT analysis of the classification and regression tree algorithm was performed to construct an algorithm (called DT algorithm) for the initial dose settings of VCM. For the testing group, the rates of attaining the VCM therapeutic range (trough value = 10-15 and 10-20 mg/L) with the DT algorithm and three conventional dose-setting methods were compared for model evaluation. The DT algorithm was constructed to be used for patients with estimated glomerular filtration rate >= 50 mL/min and body weight >= 40 kg. As a result, the recommended daily doses ranged from 20.0 to 58.1 mg/kg. In model evaluation, the DT algorithm obtained the highest rates of attaining the VCM therapeutic range compared to conventional dose-setting methods. Therefore, our DT algorithm can be applied to clinical practice. In addition, ML is useful for setting drug doses.
Type: article
URI: http://hdl.handle.net/2115/76723
Appears in Collections:薬学研究院 (Faculty of Pharmaceutical Sciences) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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