5 ． Taking Advantage of the Global RWD Standard ―OMOP CDM and OHDSI― / 薬剤疫学

Real World Data (RWD) has various types of data sources, but each source has a different format and terminology code, which makes analysis process cumbersome and repetitive. The OMOP Common Data Model (CDM) is an open standard for analysis of RWD on a global scale, and the OHDSI community is responsible for its maintenance and development. What sets the OMOP CDM apart from other data standards is the way in which it has created a structure for integrating and handling terminology globally, and the way in which analysis is conducted without exposing individual patient information outside. Such features facilitate international collaboration. The method of not releasing patient data outside is expected to be widely utilized in future because it is highly compatible with Japan's pseudonymously processed information (PPI) based on the personal information protection act, in which PPI data cannot be provided to any third party but the purpose of use can be easily changed. There are many advantages not only for international collaboration, but also for domestic collaboration or in-house use. Epidemiologists and data scientists will be able to handle data in the same model they are accustomed to both domestically and internationally. That will be of great benefit to students, personnel, and their organizations especially when they study abroad, return home, or transfer internationally. Globally, collaborators from more than 70 countries are working on this project. Data on more than 800 million people after eliminating estimated duplicates, or 10％ of the world's population, has been converted to the OMOP CDM. More than 250 related published articles have been registered with PubMed. On the other hand in Japan, there are many issues to be solved, such as support system and terminology mapping. To catch up with international levels, strong cooperation from a wide range of fields is needed.

<b>Signal Detection of Adverse Drug Reactions through LASSO Logistic Regression </b><b>Using an Electronic Health Records Database</b>：<b>A Case-Control Study </b> / 薬剤疫学

<p><b>Objective</b>：The objective of this study was to apply Least Absolute Shrinkage and Selection Operator （LASSO）logistic regression to detection of adverse drug reaction （ADR） signals using an electronic health records database as a comprehensive and quantitative method to supplement the current pharmacovigilance activities in Japan.</p><p><b>Design</b>：case-control study</p><p><b>Methods</b>：We analyzed data from 40767 inpatients using a single-institution hospital database and identified two ADRs, suspected pancreatitis and thrombocytopenia, using abnormal laboratory test results. LASSO logistic regression analysis was applied to detect ADR signals with adjustment for age, sex, comorbidities and medical procedures. The positive predictive value （PPV） was calculated using reference standard of known drug-ADR associations based on drug product labels.</p><p><b>Results</b>：The number of case group was 6735 for suspected pancreatitis and 11561 for thrombocytopenia. The number of ADR signals detected using LASSO logistic regression was 27 for suspected pancreatitis and 40 for thrombocytopenia. The calculated PPV was 3.7% for suspected pancreatitis and 55.0% for thrombocytopenia.</p><p><b>Conclusion</b>：LASSO logistic regression analysis efficiently detects ADR signals by adjusting for confounding factors such as comorbidities and medical procedures. The false positive signals may contain unknown signals and further signal assessment will be needed.</p><p></p>

Signal Detection of Adverse Drug Reactions through LASSO Logistic Regression Using an Electronic Health Records Database：A Case-Control Study / 薬剤疫学

Objective：The objective of this study was to apply Least Absolute Shrinkage and Selection Operator （LASSO）logistic regression to detection of adverse drug reaction （ADR） signals using an electronic health records database as a comprehensive and quantitative method to supplement the current pharmacovigilance activities in Japan.Design：case-control studyMethods：We analyzed data from 40767 inpatients using a single-institution hospital database and identified two ADRs, suspected pancreatitis and thrombocytopenia, using abnormal laboratory test results. LASSO logistic regression analysis was applied to detect ADR signals with adjustment for age, sex, comorbidities and medical procedures. The positive predictive value （PPV） was calculated using reference standard of known drug-ADR associations based on drug product labels.Results：The number of case group was 6735 for suspected pancreatitis and 11561 for thrombocytopenia. The number of ADR signals detected using LASSO logistic regression was 27 for suspected pancreatitis and 40 for thrombocytopenia. The calculated PPV was 3.7% for suspected pancreatitis and 55.0% for thrombocytopenia.Conclusion：LASSO logistic regression analysis efficiently detects ADR signals by adjusting for confounding factors such as comorbidities and medical procedures. The false positive signals may contain unknown signals and further signal assessment will be needed.