Establishment of a new initial dose plan for vancomycin using the generalized linear mixed model
| Autor: | Susumu Chiyotanda, Mitsuhiro Kai, Jin Tokunaga, Yasuyuki Kourogi, Kenji Ogata, Norito Takamura, Emi Tanaka, Nao Setoguchi |
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| Rok vydání: | 2017 |
| Předmět: |
Male
0301 basic medicine Initial dose 030106 microbiology Bayesian probability Health Informatics Therapeutic drug monitoring lcsh:Computer applications to medicine. Medical informatics Trough (economics) Communicable Diseases Models Biological Generalized linear mixed model Toxicology 03 medical and health sciences 0302 clinical medicine Vancomycin Modelling and Simulation Statistics Humans 030212 general & internal medicine lcsh:QH301-705.5 Aged Mathematics Aged 80 and over Bayes estimator Dose-Response Relationship Drug Population mean Research Linear model Anti-Bacterial Agents Initial dose planning lcsh:Biology (General) Modeling and Simulation Linear Models lcsh:R858-859.7 Female |
| Zdroj: | Theoretical Biology and Medical Modelling, Vol 14, Iss 1, Pp 1-16 (2017) Theoretical Biology & Medical Modelling |
| ISSN: | 1742-4682 |
| Popis: | Background When administering vancomycin hydrochloride (VCM), the initial dose is adjusted to ensure that the steady-state trough value (Css-trough) remains within the effective concentration range. However, the Css-trough (population mean method predicted value [PMMPV]) calculated using the population mean method (PMM) often deviate from the effective concentration range. In this study, we used the generalized linear mixed model (GLMM) for initial dose planning to create a model that accurately predicts Css-trough, and subsequently assessed its prediction accuracy. Methods The study included 46 subjects whose trough values were measured after receiving VCM. We calculated the Css-trough (Bayesian estimate predicted value [BEPV]) from the Bayesian estimates of trough values. Using the patients’ medical data, we created models that predict the BEPV and selected the model with minimum information criterion (GLMM best model). We then calculated the Css-trough (GLMMPV) from the GLMM best model and compared the BEPV correlation with GLMMPV and with PMMPV. Results The GLMM best model was {[0.977 + (males: 0.029 or females: -0.081)] × PMMPV + 0.101 × BUN/adjusted SCr – 12.899 × SCr adjusted amount}. The coefficients of determination for BEPV/GLMMPV and BEPV/PMMPV were 0.623 and 0.513, respectively. Conclusion We demonstrated that the GLMM best model was more accurate in predicting the Css-trough than the PMM. |
| Databáze: | OpenAIRE |
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