|Title||Development and validation of algorithms for the detection of statin myopathy signals from electronic medical records|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Chan, SL, Tham, MY, Tan, SH, Loke, C, Foo, B, Fan, Y, Ang, PS, Brunham, LR, Sung, C|
|Journal||Clinical Pharmacology and Therapeutics|
|ISSN||Print: 0009-9236; Online: 1532-6535|
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.