Task Force Report on the Validation of Diagnosis Codes and Other Outcome Definitions in the Japanese Receipt Data / 薬剤疫学

Although the recent revision of the ministerial ordinance on Good Post-marketing Study Practice (GPSP) included the utilization of medical information databases for post-marketing surveillance, there has been limited research on the validity of diagnosis codes and other outcome definitions in Japanese databases such as administrative claims (“receipt”) database. This task force proposed how to conduct good validations studies, based on the narrative review on around 100 published papers around the world. The established check list consists of ： (ⅰ) understanding the type of the database (e.g. administrative claims data, electronic health records, disease registry) ； (ii) understanding the setting of the validation study (e.g. “population-based” or not) ； (iii) defining the study outcome ； (iv) determining the way of linkage between databases ； (v) defining the gold standard ； (vi) selecting the sampling method (e.g. using the information of all patients in the database or a hospital, random sampling from all patients, random sampling from patients satisfying the outcome definition, random sampling from patients satisfying and not satisfying the outcome definition, “all possible cases” method) and sample size ； (vii) calculating the measures of validity (e.g. sensitivity, specificity, positive predictive value, negative predictive value) ； and (viii) discussing how to use the result for future studies. In current Japan, where the linkage between databases is logistically and legally difficult, most validation studies would to be conducted on a hospital basis. In such a situation, detailed description of hospital and patient characteristics is important to discuss the generalizability of the validation study result to the entire database. This report is expected to encourage and help to conduct appropriate validation studies.

Pharmacoepidemiology. Past, Present and Future. ―From Big Data to Knowledge― / 薬剤疫学

Epidemiological methods have been applied to investigate drug problems such as past drug disasters, and the academic field called pharmacoepidemiology was created. The first international conference of pharmacoepidemiology was held in 1985, and the first Japanese conference was in 1995. Therefore it is the relatively new field. Recently, pharmacoepidemiology has gained a lot of attention because of US sentinel initiative, recommendations by the Ministry of Health, Labor, and Welfare in Japan, and revision of GPSP for analyzing medical databases with epidemiological methods. In the future of pharmacoepidemiology, it is expected that the quality and quantity improvements of medical databases, and signal detection based on IoX and AI innovation. In addition, genomic data will be also more available and pharmacoepidemiology gets much closer to genomic epidemiology. It would be also possible to linkage between clinical data and patient registries, and improve analytical methods. Also, I would like to hope that pharmacoepidemiology gets more attention due to not merely big data, but creating knowledge on the safety of medicines.

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.

<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>

<b>5．Practical Use of Medical Database for Risk Management Plan (RMP)</b> / 薬剤疫学

The notification of RMP was released in 2012 and has been adapted for new drug submission since 2013. However, most cases are usual post-marketing surveillance studies. According to the ICH E2E guideline, various risk managements could be possible, especially using medical database. Recently, large database has been developed. There are two kinds of database, hospital information system including electronic medical records, and claim data. Activities of using medical database in Japan, US, and Europe are various. Based on FDA amendment acts, FDA launched Sentinel Initiative in 2008 and REMS works effectively. The Mini-Sentinel and OMOP published Common Data Model respectively. FDA also released guidance for pharmacoepidemiologic studies using electronic health data. In Europe, RMP has been implemented in 2005 and about 36% are epidemiologic studies. ENCePP which was established in 2006 provides register of pharmacoepidemiologic and pharmacovigilance studies, checklist for protocols and guide on methodological standards in pharmacoepidemiology. In Japan, PMDA provides guideline for pharmacoepidemiologic studies using medical database. Also, “MID-NET” which is the standardized medical database has been developed. As a notable activity, PMDA has conducted pilots as MIHARI project and itʼs quite promising.

<b>Checklist and Guidance of Scientific Approach to Developing Pharmacovigilance Plan (PVP) in Japan: A Report from a Task Force of JSPE</b> / 薬剤疫学

A Task Force team consisting of members from pharmaceutical companies --a central player to develop and implement RMP (Risk Management Plan)-- as well as health care professionals and members from academia was established in JSPE. The Task Force developed guidance for scientific approach to practical and ICH-E2E-compliant Pharmacovigilance Plan (PVP) stated in Japanese Risk Management Plan issued in April 2012 by the Ministry of Health, Labour and Welfare. The guidance contains the following topics.<br>1．Introduction: JSPE's activities and this task force's objectives for pharmacovigilance activities<br>2．How to select Safety Specification (SS) and describe its characteristics<br>・Selection of SS<br>・Characterization of SS<br>・Association with Research Questions (RQ)<br>3．How to define and describe RQ<br>・What is RQ ?<br>・RQ interpretation in other relevant guidelines<br>・Methodology to develop RQ for PVP with examples<br>・Best approach to integrating PVP for whole aspects of safety concern<br>4．How to optimize PVP for specific RQ<br>・Routine PVP or additional PVP ?<br>・Additional PVP design (RQ and study design, RQ structured with PICO or GPP's research objectives, specific aims, and rationale)<br>・Checklist to help develop PVP<br>5．Epilogue:<br>・What can/should be “Drug use investigation” in the context of ICH-E2E-compliant PVP.<br>・Significance of background incidence rate and needs for comparator group<br>・Infrastructure for the future PVP activities<br>6．Appendix: Checklist to help develop PVP activities in RMP<br>The task force team is hoping that this guidance help develop and conduct SS and PVP in accordance with ICH E2E, as stated in Japanese Risk Management Plan Guideline.

<b>Information: Recommendations for developing postmarketing surveys and clinical investigations using SS-MIX standardized storage </b> / 薬剤疫学

The Standardized Structured Medical record Information eXchange (SS-MIX) was started in 2006 as the project supported by the Ministry of Health, Labour and Welfare (MHLW) for promoting the exchange of the standardized medical information. Free soft wares developed in the project allow the storage of medical information to receive HL7 messages for prescription, laboratory test results, diagnoses and patient demographics in the hospital information system (HIS). We encourage the use of the SS-MIX standardized storage for postmarketing surveys and clinical studies. The recommendations consist of the following 7 parts. [1] In surveys and clinical studies, the information of drugs and laboratory test results in the SS-MIX standardized storage can be directly transferred to the electronic questionnaire and the investigators may obtain the information with high accuracy and granularity. [2] The SS-MIX standardized storage works as the backup system for the HIS because it can provide the minimum information essential in patient care even under the disastrous condition like earthquake or unexpected network failure. [3] The SS-MIX standardized storage may be useful to conduct a good pharmacoepidemiology study not only because it provides the information in the storage efficiently but also it can be used to identify “new users” who started the drug after some period of non-use.The “new user” design is often essential to have the unbiased results. [4] When the drug company conducts postmarketing surveys according to the current regulation, the use of the SS-MIX standardized storage will facilitate the fast and efficient collection of data to develop the timely measure to minimize the drug-related risk. With the SS-MIX standardized storage, it is also expected that many types of study design can be employed and the quality of data is improved in the survey. [5] The SS-MIX standardized storage maybe also useful to evaluate the risk minimization action plan by comparing the prescription pattern or incidence of the targeted adverse event between two periods before and after the implementation of the action plan. [6] In planning clinical trials, the SS-MIX standardized storage may be used to estimate the size of eligible patients. The storage may also allow conducting cross-sectional studies to know characteristics of diseases or drug treatment. In addition, cohorts of those who had coronary artery angiography, new users of a drug and those with a rare disease may be readily identified. Using such cohorts, investigators can initiate a case-control study nested within the cohort, pharmacogenomic studies and comparative effectiveness researches. [7] The SS-MIX standardized storage may be used as the formal data source in clinical trials in the future when some conditions are satisfied. For instance, the formal agreement should be reached between industry, government and academia on the use of standards of data structure in Clinical Data Interchange Standards Consortium (CDISC) and on the operation of computerized system validation (CSV) in the clinical trials.

<b>4. Large, Automated Administrative and Clinical Databases Available for Pharmacoepidemiology Studies in Japan </b> / 薬剤疫学

In this summary, we reviewed Japanese large databases available as pharmacoepidemiology data sources. In addition to the National Claims Database, two commercially available insurance claims databases are widely used: Japan Medical Data Center(JMCD) and JammNet.Three large pharmacy claims databases are also reviewed.The pharmacy claims database has unique characteristics in Japan because a prescription is valid only for four days and therefore the prescription records are believed to be almost identical to the dispensing records. Two large hospital-based databases are also available.In order to properly use these databases for the pharmacoepidemiological research questions, we need to learn first the medical practice and medical systems in Japan to have a better understanding for data source and data items. Automated large databases can be a powerful tool for pharmacoepidemiology studies by learning strengths and limitations of each database. (Jpn J Pharmacoepidemiol 2012; 17(2): 135-144)

Methodological Issues of Pharmacoepidemiology Studies Using Hospital-based Database in Japan / 薬剤疫学

Objective : To know how to conduct good pharmacoepidemiology studies using hospital-based database in Japan.<BR>Methods : Medical records during 15 months January 1996 and March 1997 in the University of Tokyo Hospital Information System (HIS) are examined know whether it is possible to conduct pharmacoepidemiology studies similar to previous studies on asthma drugs (Spitzer et al, 1992) and calcium antagonists (Psaty et al, 1995). To know the stability of population covered by HIS, the following two intervals are calculated for ambulatory patients with asthma and hypertension ; 1) average intervals of successive two outpatient visits and 2) intervals between the last day of outpatient visit and the last day of observation.<BR>Results : The size of possible pharmacoepidemiology studies attainable using HIS is judged to be more than 5% of previous studies in Canada and America. Average intervals of successive two outpatient visits are estimated to be 30 days or less for 59% of 693 asthmatics and 77% of 2842 hypertensives. For 48% of asthmatics and 71% of hypertensives, intervals between the last day of outpatient visit and the last day of observation are estimated to be 30 days or less.<BR>Discussion : To attain a size appropriate for pharmacoepidemiology study, researchers must cooperate across hospitals. Although a patient can visit any hospital anywhere under Japanese comprehensive medical care plan, it seems that patients tend to become to visit one particular hospital. However, additional information on medical care in other hospitals is needed for each study subject.<BR>Conclusion : Japanese hospital-based database is suitable for pharmacoepidemiology studies as a record during a long time period is usually available for a large fraction of patients with a particular disease. The study may be free from some of biases closely associated with referral processes known to occur in hospital case-control studies. A design of case-control study selecting patients with long medical records across 5-10 hospitals is probably the most promising when using Japanese hospital-based databases.

Voluntary Reporting on Suspected Drug Reactions Collected via Drug Companies in Japan : Companies' View / 薬剤疫学

Background : In Japan most (>85%) voluntary reports on suspected drug reactions are collected by drug companies.<BR>Objective : To know various aspects of case reports on suspected drug reactions collected by Japanese drug companies.<BR>Methods : Questionnaires were designed by our department and mailed to 96 major drug companies in late March 1997. They were reminded in mid-May and mid-June when not having responded.<BR>Results and Conclusion : Of 96 drug companies, 3 were found to be not eligible (e. g., selling only the OTC drugs) and excluded. Of the remaining 93 companies, 91 (98%) responded. Of all the case reports collected by drug companies (approximately 27, 000/year), 36%of serious or important cases are duly reported to Ministry of Health and Welfare (MHW) within 15 days or 30 days of receipt. In Japan individual case reports collected by drug companies and reported to MHW have been closed. Eleven companies are opposed to disclosing individual case reports while 6 agree unconditionally. Seventy companies agree to disclosing individual case reports with various conditions such as protecting patients' privacy, not disclosing the reporter's identity, and making individual case reports available to medical personnel only. Finally, 20 of 91 drug companies complained that MHW does not let them know individual case reports associated with their own products sent to MHW directly from medical doctors or via other companies. To promote pharmacoepidemiology, disclosing voluntary reports is pivotal and MHW is going to adopt this policy in two years for which however reporters and drug companies must be prepared in advance.

Comparative Study on Data Sources of Detecting Adverse Drug Reactions / 薬剤疫学

Objective : To find the effective means to detect adverse drug reactions (ADRs) from hospital information system, three data sources, i.e. diagnosis data (Dx), laboratory data (Lab), and prescription data (Rx), are compared in diuretics induced hyperuricemia and/or gout (H/G).<BR>Design : Retrospective cohort study.<BR>Methods : Cohort entry period was three months. Hypertensive outpatients who already had H/G prior to that period were excluded. Then, they were surveyed for 9 months. The patients using diuretics were separated into two groups, i.e. Thiazide-treated group, and Loop-treated group.<BR>Controls were randomly selected from non-diuretic-treated hypertensive outpatients matched to each diuretic group by age and sex. Signals of ADRs were the new prescription of drugs employed in the treatment of H/G from Rx, abnormal serum uric acid level from Lab, and diagnosis of H/G from Dx. The interrelationship of them were examined by the Venn diagram and scatter plot. Finally the incidence of ADRs detected by the above signals and relative risks were calculated and compared. Moreover, prevalence of renal disease in each group was surveyed to examine the possibility that renal disease caused H/G.<BR>Results : Eighteen patients in 240 outpatients treated with Thiazide diuretics and 70 patients in 523 outpatients treated with Loop diuretics were found having developed H/G from Dx, Lab, and/or Rx data sources. More than 90% of total patients were detected from Lab while, a few patients were identified from Dx and Rx. It was rare and coincidental that the three data sources agreed with one another.<BR>The risk of Loop diuretics is approximately twice that of Thiazide diuretics. The incidence and risk of H/G in diuretics estimated in the current study were compatible with the prior report. However, the prevalence of renal disease were high (though not statistically significant) in Loop-treated group so that we possibly overestimated the risk of it.<BR>Conclusion : The order of three data sources, arranged according to the number of ADR signals detected, was Lab, Rx, and Dx. It may be possible to assess the risk of ADR even by Lab only. If Lab is not available, Rx and Dx are useful provided that more subjects and longer research period are involved. However it is necessary to combine three data sources, Dx, Lab, and Rx to detect as many suspected adverse events as possible when using the present clinical database.