A machine learning model that emulates experts’ decision making in vancomycin initial dose planning

加药 万古霉素 治疗药物监测 医学 列线图 肾毒性 重症监护医学 药品 金黄色葡萄球菌 药理学 内科学 毒性 遗传学 生物 细菌
作者
Tetsuo Matsuzaki,Yoshiaki Kato,Hideaki Mizoguchi,Kiyofumi Yamada
出处
期刊:Journal of Pharmacological Sciences [Elsevier BV]
卷期号:148 (4): 358-363 被引量:8
标识
DOI:10.1016/j.jphs.2022.02.005
摘要

Vancomycin is a glycopeptide antibiotic that is a primary treatment for methicillin-resistant Staphylococcus aureus infections. To enhance its clinical effectiveness and prevent nephrotoxicity, therapeutic drug monitoring (TDM) of trough concentrations is recommended. Initial vancomycin dosing regimens are determined based on patient characteristics such as age, body weight, and renal function, and dosing strategies to achieve therapeutic concentration windows at initial TDM have been extensively studied. Although numerous dosing nomograms for specific populations have been developed, no comprehensive strategy exists for individually tailoring initial dosing regimens; therefore, decision making regarding initial dosing largely depends on each clinician's experience and expertise. In this study, we applied a machine-learning (ML) approach to integrate clinician knowledge into a predictive model for initial vancomycin dosing. A dataset of vancomycin initial dose plans defined by pharmacists experienced in vancomycin TDM (i.e., experts) was used to build the ML model. Although small training sets were used, we established a predictive model with a target attainment rate comparable to those of experts, another ML model, and commonly used vancomycin dosing software. Our strategy will help develop an expert-like predictive model that aids in decision making for initial vancomycin dosing, particularly in settings where dose planning consultations are unavailable.
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