Major adverse cardiovascular events (MACEs), such as stroke and myocardial infarction (MI), are the most common cause of death worldwide (the second leading cause in Japan), and their prevention is critical in healthcare. 1-3 Hypertension, hyperlipidemia, and diabetes mellitus are known risk factors for MACEs, and a number of pharmaceutical agents have been developed to control the risk

S95 database compared with the patient diagnosis based on patient medical charts. Claims and administrative data were extracted for fiscal years 2013 (2013/4/1 to 2014/3/31) from Jichi-Medical school hospital in Japan. One hundred potential cases for each of the three cardiovascular events were randomly selected using the ICD-10 code. An independent clinical event committee (iCEC) reviewed the identified potential cases with medical charts to determine whether the cases met pre-specified criteria for the events. We selected the potential event data set based on algorithms combining conditions in the ICD-10 code, medical treatment and medication data, and calculated the positive predictive values (PPVs) of each outcome. Of the 100 patients for each cardiovascular event, all medical records were adjudicated by three physicians (two cardiologists and one neurologist) independently. RESULTS: Preliminary results showed the algorithm PPVs based only on the ICD-10 code were 81.6% (95%CI: 72.5-88.7%) for AMI, 31.0% (95%CI: 22.8-40.3%) for IS and 34.4% (95%CI: 26.1-43.6%) for HS. The PPV value for AMI was higher than previous studies in the US. Additional research is needed for IS and HS to identify the combination of conditions which demonstrate higher PPVs. CONCLUSIONS: This is the first study in Japan to evaluate PPV for cardiovascular events identified using the ICD-10 code based algorithms. The results indicate that using the ICD-10 code, it is possible to identify AMI cases from Japanese claims database.

S35treated with IDet and 357(25.0%) patients treated with rIGlar. The overall rate of all hypoglycemia was 109.4 events/100 patient-year. The rate of all hypoglycemia among patients treated with IGlar, IDet and rIGlar were 117.06, 82.97, and 114.06 respectively. The overall rate of severe hypoglycemia was 12.84 events/100 patientyear. The rate of severe hypoglycemia among patients treated with IGlar, IDet and rIGlar were 4.39, 0.84 and 46.23 respectively. The overall rate of non-severe hypoglycemia was 96.56 events/100 patient-year. The rate of non-severe hypoglycemia among patients treated with IGlar, IDet and rIGlar were 112.66, 82.13 and 67.84 respectively. The rates of all hypoglycemia and severe hypoglycemia were significantly less with IDet than IGlar and rIGlar (all P< 0.05), based on comparison between IDet and IGlar, and comparison between IDet and rIGlar. CONCLUSIONS: Hypoglycemic events in patients treated with basal insulin analogues in China was common. There were differences in rate of hypoglycemic events depending on the basal insulin analogue used, with lowest hypoglycemic risk with IDet.

Background The diagnostic likelihood ratio (DLR) and its utility are well-known in the field of medical diagnostic testing. However, its use has been limited in the context of an outcome validation study. We considered that wider recognition of the utility of DLR would enhance the practices surrounding database studies. This is particularly timely and important since the use of healthcare-related databases for pharmacoepidemiology research has greatly expanded in recent years. In this paper, we aimed to advance the use of DLR, focusing on the planning of a new database study. Methods Theoretical frameworks were developed for an outcome validation study and a comparative cohort database study; these two were combined to form the overall relationship. Graphical presentations based on these relationships were used to examine the implications of validation study results on the planning of a database study. Additionally, novel uses of graphical presentations were explored using some examples. Results Positive DLR was identified as a pivotal parameter that connects the expected positive-predictive value (PPV) with the disease prevalence in the planned database study, where the positive DLR is equal to sensitivity/(1-specificity). Moreover, positive DLR emerged as a pivotal parameter that links the expected risk ratio with the disease risk of the control group in the planned database study. In one example, graphical presentations based on these relationships provided a transparent and informative summary of multiple validation study results. In another example, the potential use of a graphical presentation was demonstrated in selecting a range of positive DLR values that best represented the relevant validation studies. Conclusions Inclusion of the DLR in the results section of a validation study would benefit potential users of the study results. Furthermore, investigators planning a database study can utilize the DLR to their benefit. Wider recognition of the full utility of the DLR in the context of a validation study would contribute meaningfully to the promotion of good practice in planning, conducting, analyzing, and interpreting database studies.