정보 성분과 상대위험도를 이용한 clopidogrel의 약물상호작용 시그널 검색 : 건강보험데이터베이스를 대상으로 한 데이터마이닝 연구
Use of Information Component (IC) and Relative Risk (RR) for Signal Detection of Drug Interactions of Clopidogrel : Data-mining Study Using Health Insurance Review & Assessment Service (HIRA) Claims Database
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Health Insurance Review & Assessment Service (HIRA) claims database has a high potential to detect signals of new drug interactions. The aim of this study was to evaluate the usefulness of information component (IC) and relative risk (RR) as a tool for signal detection, and to analyze the possible drug interactions caused by clopidogrel using HIRA claims database. This study was performed in elderly patients over 65 years of age who administered clopidogrel from January 2005 to June 2006 in South Korea. Serious Adverse Events (SAEs) as drug interactions of clopidogrel were defined as any ambulatory hospitalization for ischemic diseases within comcomitant medication period of clopidogrel. Information Component (IC) and Relative Risk (RR) were calculated to compare the proportion of drug-SAE pairs in order to select drug specific SAEs. IC and RR signals of clopidogrel drug interaction were screened when IC’s 95%confidence interval was greater than 0 and RR’s 95% confidence interval was greater than 1 respectively. All detected signals were compared to references such as Micromedex® and 2010 Drug Interaction Facts™. Sensitivity, specificity,positive predicted value and negative predicted value were used to evaluate usefulness of this method. Among 13,252,930 cases of elderly patients who co-administered clopidogrel and other drugs, 47,485 cases were detected as SAE. Of these, one-hundred nine cases were detected by the IC-based data-mining approach and ninety one cases were detected by the RR-based data-mining approach. Total One-hundred sixty three unrecognized signals were detected by IC or RR. Twelve signals from IC-based data-mining (57.1%) were corresponded with drug interactions from references and eight signals from RR-based data-mining (38.1%) were corresponded with drug interactions from references. These signals include proton pump inhibitors, calcium channel blockers and HMG CoA reductase Inhibitors, which were known to affect CYP450 metabolism. Further studies using HIRA claims database are necessary to develope appropriate data-mining measure
Abstract
Health Insurance Review & Assessment Service (HIRA) claims database has a high potential to detect signals of new drug interactions. The aim of this study was to evaluate the usefulness of information component (IC) and relative risk (RR) as a tool for signal detection, and to analyze the possible drug interactions caused by clopidogrel using HIRA claims database. This study was performed in elderly patients over 65 years of age who administered clopidogrel from January 2005 to June 2006 in South Korea. Serious Adverse Events (SAEs) as drug interactions of clopidogrel were defined as any ambulatory hospitalization for ischemic diseases within comcomitant medication period of clopidogrel. Information Component (IC) and Relative Risk (RR) were calculated to compare the proportion of drug-SAE pairs in order to select drug specific SAEs. IC and RR signals of clopidogrel drug interaction were screened when IC’s 95%confidence interval was greater than 0 and RR’s 95% confidence interval was greater than 1 respectively. All detected signals were compared to references such as Micromedex® and 2010 Drug Interaction Facts™. Sensitivity, specificity,positive predicted value and negative predicted value were used to evaluate usefulness of this method. Among 13,252,930 cases of elderly patients who co-administered clopidogrel and other drugs, 47,485 cases were detected as SAE. Of these, one-hundred nine cases were detected by the IC-based data-mining approach and ninety one cases were detected by the RR-based data-mining approach. Total One-hundred sixty three unrecognized signals were detected by IC or RR. Twelve signals from IC-based data-mining (57.1%) were corresponded with drug interactions from references and eight signals from RR-based data-mining (38.1%) were corresponded with drug interactions from references. These signals include proton pump inhibitors, calcium channel blockers and HMG CoA reductase Inhibitors, which were known to affect CYP450 metabolism. Further studies using HIRA claims database are necessary to develope appropriate data-mining measure
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