PMDA started MIHARI project in FY2009 to enhance drug safety assessment by developing ways to utilize electronic medical information as additional data sources to spontaneous adverse drug reaction reports. The project has been established according to PMDA's second midterm plan. In this article, we will introduce latest two studies (pilot studies No. 4 and 5) using data of standardized electronic medical record(EMR) called SS-MIX (standardized structured medical record information exchange) data out of our several pilot studies. SS-MIX is a standard specification published by the Ministry of Health, Labour and Welfare. In these studies, anonymized SS-MIX data were provided by six collaborative hospitals respectively. In pilot study No. 4, we explored approaches for evaluating the impact of regulatory action which instructed relevant manufacturers to revise package inserts of sitagliptin phosphate hydrate (sitagliptin) to call physician's attention. The revision was about reducing dose of sulfonylurea (SU) to avoid serious hypoglycemia when it is prescribed concomitantly with sitagliptin. As indicators of the impact, we evaluated changes in proportion of concomitant use and average SU dose before and after the action and estimated the risk of hypoglycemia in concomitant users compared to SU alone users before and after the action. In conclusion, evaluating impact of the regulatory action using SS-MIX data was technically feasible; however, it was difficult to analyze with adequate accuracy due to limited size of the data. In pilot study No. 5, we examined validity of outcome definitions for hyperthyroidism which were applied to combinations of some data elements of SS-MIX data to identify the patients. Three types of outcome definitions were prepared; 1) definitive diagnosis of hyperthyroidism, 2) prescription of medication for hyperthyroidism, 3) prescription of medication for hyperthyroidism in or after the month in which definitive diagnosis of hyperthyroidism was given. Criteria for case ascertainment were determined according to relevant clinical guidelines. After the cases were ascertained, positive predictives values were calculated. The results suggested that using information on prescription of medication improves validity of definition of outcome. The findings from pilot studies in MIHARI project have been utilized in another project which PMDA is carrying forward now (EMR network project). The findings would be also helpful when we use data from this network. (Jpn J Pharmacoepidemiol 2013；18(1)：23-29)

　　The platelet counts documented in most of the studies using the fully automated hematology analyzer XE-5000 are values measured by impedance technology (PLT-I). If blood specimens showing an anomalous particle-size distribution curve in the area where platelet counts are low are used (exceptionally low platelet count samples), the counting method is automatically switched over to an optical method (PLT-O). In the present study, we examined the usefulness of the XE-5000 by comparing PLT-I with PLT-O, using blood samples with low platelet counts collected from patients who visited our hospital between January 1 and March 31, 2012. Dilution linearity left nothing to be desired in either of the two, but simultaneous reproducibility was higher in PLT-O than in PLT-I. The correlations of PLT-I and PLT-O with visual counts were high, working out at r＝0.889～0.984. In the exceptionally low platelet count samples, the correlation coefficient was high in PLT-O than in PLT-I. The cases showing low platelet counts frequently presented giant platelets and/or red cell fragments. Therefore, measuring the samples with low platelet counts requires a high degree of precision. In the samples with exceptionally low platelet counts, PLT-O exceeded PLT-I in simultaneous reproducibility and correlation with visual counts. Thus, our study demonstrated the usefulness of the XE-5000 that could enumerate PLT-O automatically and speedily.

BACKGROUND/AIMS: We recently identified recessive mutations in the solute carrier organic anion transporter family member 2A1 gene (SLCO2A1) as causative variants of chronic nonspecific multiple ulcers of the small intestine (chronic enteropathy associated with SLCO2A1, CEAS). The aim of this study was to investigate the gastroduodenal expression of the SLCO2A1 protein in patients with CEAS and Crohn’s disease (CD). METHODS: Immunohistochemical staining for SLCO2A1 was performed with a polyclonal antibody, HPA013742, on gastroduodenal tissues obtained by endoscopic biopsy from four patients with CEAS and 29 patients with CD. RESULTS: The expression of SLCO2A1 was observed in one of four patients (25%) with CEAS and in all 29 patients (100%) with CD (p < 0.001). The three patients with CEAS without SLCO2A1 expression had a homozygous splice-site mutation in SLCO2A1, c.1461+1G>C (exon 7) or c.940+1G>A (exon 10). The remaining one CEAS patient with positive expression of SLCO2A1 had compound heterozygous c.664G>A and c.1807C>T mutations. CONCLUSIONS: Immunohistochemical staining for SLCO2A1 in gastroduodenal tissues obtained by endoscopic biopsy is considered useful for the distinction of CEAS from CD.
Biopsy ; Crohn Disease* ; Humans ; Immunohistochemistry ; Intestine, Small ; Ulcer

In this study, we investigated the effects of severe hemolysis (hemoglobin [Hb] > 500 mg/dL) in blood specimens by classifying them into non-hemolysis, hemolysis (Hb ≤ 500 mg/dL), and severe hemolysis. Investigated items were total protein (TP), albumin (ALB), total bilirubin (T-Bil), direct bilirubin (D-Bil), aspartate transaminase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LD), alkaline phosphatase (ALP), gamma-glutamyl transferase (γ-GT), creatine kinase (CK), amylase (AMY), cholinesterase (ChE), blood urea nitrogen (BUN), creatinine (Cre), sodium (Na), potassium (K), chloride (Cl), calcium (Ca), uric acid (UA), triglycerides (TG), total cholesterol (T-Cho), high-density lipoprotein cholesterol (HDL-C), lowdensity lipoprotein cholesterol (LDL-C), and C-reactive protein (CRP), and actual clinical test results were used. Based on the results, we were able to classify the error relationships into three groups according to hemolysis status. Group A shows an error between non-hemolysis and hemolysis, and an even stronger error in severe hemolysis (T-Bil, AST, LD, Na, K, Ca, and UA). Group B showed no error between non-hemolysis and hemolysis, but errors in strong hemolysis (ALB, D-Bil, ALT, γ-GT, CK, AMY, TG, T-Cho, HDL-C, and LDL-C). Group C shows no errors in either hemolysis or strong hemolysis (TP, ALP, ChE, BUN, Cre, Cl, and CRP). Among these, the Group B classification was a new finding. In situations where the measurement of hemolyzed specimens is unavoidable, it is important that clinical laboratory technologists be aware of its impact and provide the results in a way that can be used in clinical practice.