Establishment of a new initial dose plan for vancomycin using the generalized linear mixed model.

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.

Development of the Virtual Physical Assessment Learning Material That Allows the Learners to Check Drug Efficacy and Early Detection of Adverse Effects through Virtual Experience.

We utilized the information and communication technology to develop the physical assessment (PA) learning materials in the virtual experience type. This learning material consists of two parts which include case learning and basic learning. We created example scenarios about various conditions that a pharmacist may experience in medical scenes such as in a hospital ward, community pharmacy, home, and drugstore. Illustrations of a virtual patient's avatar before and after taking the medicines were incorporated in the learning materials. The virtual training includes a stethoscope that was used in examining sounds (heart, pulmonary and bowel sounds) that served as evidences in the confirmation of drug efficacy and its possible adverse effects. In addition, we included the images of each body part, the 24 format question items, the palpation (rate and rhythm) of the radial artery, brachial artery and pedal artery, the clinical data obtained from several medical equipment, the pupillary reflex, and the urine dipstick test. This way, learners are able to experience PA with reference to the subjective and objective data from patient reception and questions. The virtual patient's avatar displayed on the monitor features auscultatory sounds on the stethoscope. It also features clinical data obtained from other medical equipment that can give the learners an interactive way of learning about various medical conditions. For evaluation, we gave out questionnaires on the virtual PA to pharmacy students. As a result, a high evaluation was reflected in terms of the degree of usefulness for both case learning and basic learning.