| Autor: |
Chan, SL, Tham, MY, Tan, SH, Loke, C, Foo, BPQ, Fan, Y, Ang, PS, Brunham, LR, Sung, C |
| Předmět: |
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| Zdroj: |
Clinical Pharmacology & Therapeutics; May2017, Vol. 101 Issue 5, p667-674, 8p |
| Abstrakt: |
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 (>4× 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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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