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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.7
- 수록면
- 199 - 207 (9page)
- DOI
- https://doi.org/10.4258/hir.2017.23.3.199
이용수
초록· 키워드
오류제보하기
Objectives: To enable early detection of adverse drug reactions (ADRs) in patients using HMG-CoA reductase inhibitors (statins), we developed an algorithm that automatically detects liver injury caused by statins from Electronic Medical Record (EMR) data. We verified the performance of our algorithm through manual ADR assessment and a direct chart review.
Methods: The subjects in this study were patients who had been prescribed a statin for the first time among outpatients in Seoul St. Mary’s Hospital in Korea between January 2009 and December 2012. We extracted basic information about the patients, including laboratory information, underlying disease, diagnosis information, prescription information, and concomitant drugs. We developed an automatic ADR detection algorithm by using EMR data. We validated the results of the algorithm through a chart review. Results: We developed the algorithm to assess ADR occurrences based on alanine transaminase (ALT) and alkaline phosphatase (ALP) levels. According to the proposed algorithm, any of these result options could be attained: ADR-free, little association, strong association, and weak association or indeterminable. The results of the ADR assessments obtained using the proposed algorithm showed that the data of 126 patients (1.4% of all 9,241 patients) included suspicious figures, thus indicating the possibility of an ADR. In the EMR chart review for verifying the algorithm, ADRs of 33 patients were not associated with statin use; therefore, the ADR occurrence rate was found to be 1.0% (93/9,241). Therefore, the positive predictive value was calculated to be 73.8% (93/126; 95% confidence interval, 69.2%?77.6%). No differences were observed between statin types (p = 0.472). Conclusions: For early detection of statin-induced liver injury, we developed an automatic ADR assessment algorithm. We expect that algorithms that are more reliable can be developed if we conduct supplement clinical studies with a focus on adverse drug effects.
목차
등록된 정보가 없습니다.
참고문헌 (30)
참고문헌 신청
Shepherd G / 2012 / Adverse drug reaction deaths reported in United States vital statistics, 1999-2006 / Ann Pharmacother 46(2):169~175
Brown JS / 2007 / Early detection of adverse drug events within population-based health networks : application of sequential testing methods / Pharmacoepidemiol Drug Saf 16(12):1275~1284
Park IW / 2014 / Onset time of hyperkalaemia after angiotensin receptor blocker initiation: when should we start serum potassium monitoring? / J Clin Pharm Ther 39(1):61~68
Jha AK / 1998 / Identifying adverse drug events : development of a computer-based monitor and comparison with chart review and stimulated voluntary report / J Am Med Inform Assoc 5(3):305~314
Haerian K / 2012 / Detection of pharmacovigilance-related adverse events using electronic health records and automated methods / Clin Pharmacol Ther 92(2):228~234
Brown JS / 2007 / Early detection of adverse drug events within population-based health networks : application of sequential testing methods / Pharmacoepidemiol Drug Saf 16(12):1275~1284
Park IW / 2014 / Onset time of hyperkalaemia after angiotensin receptor blocker initiation: when should we start serum potassium monitoring? / J Clin Pharm Ther 39(1):61~68
Jha AK / 1998 / Identifying adverse drug events : development of a computer-based monitor and comparison with chart review and stimulated voluntary report / J Am Med Inform Assoc 5(3):305~314
Haerian K / 2012 / Detection of pharmacovigilance-related adverse events using electronic health records and automated methods / Clin Pharmacol Ther 92(2):228~234
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