title: Development and validation of algorithms for the detection of statin myopathy signals from electronic medical records.
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.
authors.affiliation: Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research, Singapore.
authors.forename: S L
authors.initials: SL
authors.lastname: Chan
authors.affiliation: Vigilance and Compliance Branch, Health Products Regulation Group, Health Sciences Authority, Singapore.
authors.forename: M Y
authors.initials: MY
authors.lastname: Tham
authors.affiliation: Vigilance and Compliance Branch, Health Products Regulation Group, Health Sciences Authority, Singapore.
authors.forename: S H
authors.initials: SH
authors.lastname: Tan
authors.affiliation: Vigilance and Compliance Branch, Health Products Regulation Group, Health Sciences Authority, Singapore.
authors.forename: C
authors.initials: C
authors.lastname: Loke
authors.affiliation: Vigilance and Compliance Branch, Health Products Regulation Group, Health Sciences Authority, Singapore.
authors.forename: Bpq
authors.initials: B
authors.lastname: Foo
authors.affiliation: Vigilance and Compliance Branch, Health Products Regulation Group, Health Sciences Authority, Singapore.|Genome Institute of Singapore, Singapore.
authors.forename: Y
authors.initials: Y
authors.lastname: Fan
authors.affiliation: Vigilance and Compliance Branch, Health Products Regulation Group, Health Sciences Authority, Singapore.
authors.forename: P S
authors.initials: PS
authors.lastname: Ang
authors.affiliation: Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research, Singapore.|Department of Medicine, Center for Heart and Lung Innovation, University of British Columbia, Canada.
authors.forename: L R
authors.initials: LR
authors.lastname: Brunham
authors.affiliation: Vigilance and Compliance Branch, Health Products Regulation Group, Health Sciences Authority, Singapore.|Duke-NUS Medical School, Singapore.
authors.forename: C
authors.initials: C
authors.lastname: Sung
mesh_terms: D000465:Algorithms; D003402:Creatine Kinase / Q000097:blood; D057225:Data Mining; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000009:adverse effects*; D008297:Male; D008875:Middle Aged; D009135:Muscular Diseases / Q000139:chemically induced* / Q000453:epidemiology*; D060735:Pharmacovigilance; D011237:Predictive Value of Tests; D012206:Rhabdomyolysis / Q000139:chemically induced / Q000453:epidemiology
chemical_list: D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D003402:Creatine Kinase