Earticle

초록

영어

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’ 95% confidence interval was greater than 0 and RR’ 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 develop appropriate data-mining measure.

목차

Abstract
 서론
 연구방법
  연구대상
  자료의 출처
  약물상호작용 발생여부의 판단기준
  폐색성 상호작용과 연결되는 KCD 코드의 분류
  약물상호작용 시그널의 정의
  데이터마이닝 방법의 검증
 결과
 고찰
 참고문헌

키워드

저자

• 김진형 [ Jinhyung Kim | 서울대학교 약학대학 ]
• 최청암 [ Chungam Choi | 서울대학교 약학대학 ]
• 오정미 [ Jung Mi Oh | 서울대학교 약학대학 ]
• 손성호 [ Sung Ho Son | 경북대학교병원 약제부 ]
• 신완균 [ Wan Gyoon Shin | 서울대학교 약학대학 ] Correspondence

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국임상약학회지 [Korean Journal of Clinical Pharmacy]

• 간기

  계간

• pISSN

  1226-6051

• 수록기간

  1991~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 518 DDC 615