Objective: To attain optimal blood concentration rapidly, it is needed to perform initial dose setting appropriately when vancomycin (VCM) used.  In order to design initial dose settings of VCM more currently, we compared the predictive performance of two types of VCM therapeutic drug monitoring (TDM) analysis software retrospectively.
Method: We utilized two TDM analysis software, SHIONOGI-VCM-TDM ver.2009 (VCM-TDM) and “Vancomycin MEEK TDM analysis software Ver. 2.0” (MEEK), based on patient’s background. 112 patients who received VCM and performed TDM were analyzed during the period from October 2011 through September 2012 and compared the actual trough level with the predictive trough level.  The predictive performance was evaluated by calculating ME (mean prediction error), MAE (mean absolute prediction error), and RMSE (root mean squared error).  Age, gender, and a renal function were evaluated as patient’s background.
Results: VCM-TDM gave good predictive performance for patients overall.  When classified patient’s background complexly (sex, age, and renal function), as for male patients, VCM-TDM showed good predictive performance except for the group over 65 years old and CCr over 85 mL/min.  For female patients, the difference of predictive performance was not accepted by all groups.
Conclusion: These results suggest, for male patients, we should use VCM-TDM for initial dose settings except for the group over 65 years old and over CCr 85 mL/min.  For the other patients, we consider that both of software can be used.  These new findings seem to contribute to proper dosage settings of VCM.

Objective: It is very important that, to avoid, pharmacists-check medication being taken by patient.  In the Hokkaido University Hospital we used commercial drug identification software at the start of outpatient prescription identification duty and reported the outcome.  Furthermore, we filled in another hand-written check sheet with the drug’s name, whether or not it is used in our hospital, alternative drugs, and the dosage and administration.  Because of the risk of drugs being entered by mistake, we built a database for drug identification and distinguished the outpatient’s prescriptions.  With this system it is possible integrate identification reports and check sheet using one style, automatically.  We also to smoothly rationalize duties by planning correct communication between the medical staff.  At the same time, we analyzed the case that was able to intervene in reasonable use of medical supplies with a past identification report as a result of pharmacists distinguishing outpatient prescriptions.
Design and Methods: This system was constructed using Microsoft® Access, which is a general-purpose database software.  Also, the medical supply database that we used for this system uses “Drugs in Japan Ethical Drugs DB (supervised by Drugs in Japan Forum)” published by JIHO Co., Ltd.
Results: By using this system, we were able to reduce the time required to identify the drugs and make the report.  The result of a questionnaire carried out on doctors and a nurses and medical staff revealed that more than 90% of the respondents claimed, “the report is easy to refer.”  Likewise, we analyzed a report of the previous year and recognized that medical staff could not find the inappropriate use of prescriptions for outpatients in about 17.5%.
Conclusion: This system improved the efficiency of outpatient prescriptions practices, and it became clear that it could be used convincingly as a tool to share appropriate drug information between medical staff and pharmacists, more precisely.  In addition, feedback from medical staff suggested that it might prevent the risk of problems surrounding outpatient prescriptions, from the viewpoint of the pharmacist.

Objective: Hematological toxicity, including neutropenia and thrombocytopenia, is a typical side effect of ganciclovir (GCV). We previously developed a risk-prediction model for GCV-induced neutropenia using decision tree (DT) analysis. By employing the DT model, which is a flowchart-like framework, users can predict the combination of factors that may increase neutropenia risk. However, a risk-prediction model for thrombocytopenia has not been established. Here, we aimed to identify the risk factors associated with GCV-induced thrombocytopenia and construct risk-prediction models.Method: We retrospectively evaluated the medical records of 386 patients who received GCV between April 2008 and March 2018 at Hokkaido University Hospital. Thrombocytopenia is defined as a decrease in the platelet count (PLT) to ＜50,000 cells/mm3 and to a ＜75% decrease. Risk factors of thrombocytopenia were extracted from the medical records using a multiple logistic regression analysis. Moreover, we employed chi-squared automatic interaction detection (CHAID) and classification and regression tree (CRT) algorithms to develop the DT models. The accuracies of the established models were evaluated to assess their reliability.Results: Thrombocytopenia occurred in 47 (12.2%) patients. In the multiple logistic regression analysis, data of patients with white blood cells ＜7,000 cells/mm3,PLT＜101,000 cells/mm3 and total bilirubin ≥ 0.8 mg/dL were extracted. Two risk-prediction models were constructed, and patients were divided into six and seven subgroups. In both algorithms, data on hematopoietic stem cell transplantations, PLT ＜101,000 cells/mm3, serum albumin ＜ 2.8 g/dL, total bilirubin ≥ 0.8 mg/dL, and residence in intensive care unit were extracted. The predictive accuracy of both the CHAID algorithm and the logistic regression models was 87.8% and that of the CRT algorithm was 88.3%, indicating they were reliable.Conclusion: We successfully identified the factors associated with GCV-induced thrombocytopenia and constructed useful flowchartlike risk-prediction models.