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Development and Validation of Algorithms for the Detection of Statin Myopathy Signals From Electronic Medical Records
Author(s) -
Chan SL,
Tham MY,
Tan SH,
Loke C,
Foo BPQ,
Fan Y,
Ang PS,
Brunham LR,
Sung C
Publication year - 2017
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1002/cpt.526
Subject(s) - myopathy , statin , hydroxymethylglutaryl coa reductase inhibitors , algorithm , medical record , computer science , medicine
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.

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