Development and validation of algorithms for the detection of statin myopathy signals from electronic medical records. Issue 5 (21st January 2017)

• Record Type: Journal Article
• Authors: Chan, SL; Tham, MY; Tan, SH; Loke, C; Foo, BPQ; Fan, Y; Ang, PS; Brunham, LR; Sung, C
• Is Part Of: Clinical pharmacology & therapeutics. Volume 101:Issue 5(2017)
• Page Start: 667
• Page End: 674
• Publication Date: 2017-01-21
• Subjects: Pharmacology -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1002/cpt.526
• Languages: English
• ISSNs: 0009-9236

Abstract

Abstract : 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.