Electronic health records for biological sample collection: feasibility study of statin-induced myopathy using the Clinical Practice Research Datalink

Abstract

AIMS: Electronic healthcare records (EHRs) are increasingly used to store clinical information. A secondary benefit of EHRs is their use, in an anonymized form, for observational research. The Clinical Practice Research Datalink (CPRD) contains EHRs from primary care in the UK and, despite 1083 peer-reviewed research publications, has never been used to obtain pharmacogenetic samples. Using a statin-induced myopathy paradigm, we evaluated using the CPRD to obtain patient samples for a pharmacogenetic study targeting 250 cases and 500 controls from UK general practitioner (GP) practices. METHODS: The CPRD identified potential patients fitting specific case-definition criteria (active rhabdomyolysis or creatine phosphokinase > four times the upper limit of normal), and corresponding GP practices were asked to invite patient participation. Consenting patients were requested to provide either saliva or blood samples and to complete an ethnicity questionnaire. Control subjects were recruited from the same GP practice (saliva) or a small number of practices (blood). Samples were forwarded for DNA extraction. RESULTS: Thirty-six months of recruitment yielded DNA samples from 149 statin-induced myopathy cases and 587 tolerant controls. Data show that contacting patients through their GP is a reliable method for obtaining samples without compromising anonymity. Saliva collection directly from patients was considerably less effective than blood sampling. After 10 months of recruitment, saliva sampling was suspended in favour of blood sampling. CONCLUSIONS: We demonstrate the potential of EHRs for identifying accurately phenotyped cases and controls for pharmacogenetic studies. Recruitment was successful only because of the willingness of GP practices to participate and the existence of strong doctor-patient relationships. The present study provides a model that can be implemented in future genetic analyses using EHRs.