| アブストラクト | The purpose of this study was to develop and validate sensitive algorithms to detect hospitalized statin-induced myopathy (SIM) cases from electronic medical records (EMRs). We developed four algorithms on a training set of 31,211 patient records from a large tertiary hospital. We determined the performance of these algorithms against manually curated records. The best algorithm used a combination of elevated creatine kinase (>4x the upper limit of normal (ULN)), discharge summary, diagnosis, and absence of statin in discharge medications. This algorithm achieved a positive predictive value of 52-71% and a sensitivity of 72-78% on two validation sets of >30,000 records each. Using this algorithm, the incidence of SIM was estimated at 0.18%. This algorithm captured three times more rhabdomyolysis cases than spontaneous reports (95% vs. 30% of manually curated gold standard cases). Our results show the potential power of utilizing data and text mining of EMRs to enhance pharmacovigilance activities. |
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| ジャーナル名 | Clinical pharmacology and therapeutics |
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| Pubmed追加日 | 2016/10/6 |
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| 投稿者 | Chan, S L; Tham, M Y; Tan, S H; Loke, C; Foo, Bpq; Fan, Y; Ang, P S; Brunham, L R; Sung, C |
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| 組織名 | Translational Laboratory in Genetic Medicine, Agency for Science, Technology and;Research, Singapore.;Vigilance and Compliance Branch, Health Products Regulation Group, Health;Sciences Authority, Singapore.;Genome Institute of Singapore, Singapore.;Department of Medicine, Center for Heart and Lung Innovation, University of;British Columbia, Canada.;Duke-NUS Medical School, Singapore. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/27706800/ |
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