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

Genomewide Association Study of Statin-Induced Myopathy in Patients Recruited Using the UK Clinical Practice Research Datalink

Statins can be associated with myopathy. We have undertaken a genomewide association study (GWAS) to discover and validate genetic risk factors for statin‐induced myopathy in a “real‐world” setting. One hundred thirty‐five patients with statin myopathy recruited via the UK Clinical Practice Research Datalink were genotyped using the Illumina OmniExpress Exome version 1.0 Bead Chip and compared with the Wellcome Trust Case‐Control Consortium (n = 2,501). Nominally statistically significant single nucleotide polymorphism (SNP) signals in the GWAS (P < 5 × 10⁻⁵) were further evaluated in several independent cohorts (comprising 332 cases and 449 drug‐tolerant controls). Only one (rs4149056/c.521C>T in the SLCO1B1 gene) SNP was genomewide significant in the severe myopathy (creatine kinase > 10 × upper limit of normal or rhabdomyolysis) group (P = 2.55 × 10⁻⁹; odds ratio 5.15; 95% confidence interval 3.13–8.45). The association with SLCO1B1 was present for several statins and replicated in the independent validation cohorts. The data highlight the role of SLCO1B1 c.521C>T SNP as a replicable genetic risk factor for statin myopathy. No other novel genetic risk factors with a similar effect size were identified.

A common missense variant of LILRB5 is associated with statin intolerance and myalgia

Aims

A genetic variant in LILRB5 (leukocyte immunoglobulin-like receptor subfamily-B) (rs12975366: T > C: Asp247Gly) has been reported to be associated with lower creatine phosphokinase (CK) and lactate dehydrogenase (LDH) levels. Both biomarkers are released from injured muscle tissue, making this variant a potential candidate for susceptibility to muscle-related symptoms. We examined the association of this variant with statin intolerance ascertained from electronic medical records in the GoDARTS study.

Methods and results

In the GoDARTS cohort, the LILRB5 Asp247 variant was associated with statin intolerance (SI) phenotypes; one defined as having raised CK and being non-adherent to therapy [odds ratio (OR) 1.81; 95% confidence interval (CI): 1.34-2.45] and the other as being intolerant to the lowest approved dose of a statin before being switched to two or more other statins (OR 1.36; 95% CI: 1.07-1.73). Those homozygous for Asp247 had increased odds of developing both definitions of intolerance. Importantly the second definition did not rely on CK elevations. These results were replicated in adjudicated cases of statin-induced myopathy in the PREDICTION-ADR consortium (OR1.48; 95% CI: 1.05-2.10) and for the development of myalgia in the JUPITER randomized clinical trial of rosuvastatin (OR1.35, 95% CI: 1.10-1.68). A meta-analysis across the studies showed a consistent association between Asp247Gly and outcomes associated with SI (OR1.34; 95% CI: 1.16-1.54).

Conclusion

This study presents a novel immunogenetic factor associated with statin intolerance, an important risk factor for cardiovascular outcomes. The results suggest that true statin-induced myalgia and non-specific myalgia are distinct, with a potential role for the immune system in their development. We identify a genetic group that is more likely to be intolerant to their statins.

Supplementing electronic health records through sample collection and patient diaries: A study set within a primary care research database

PURPOSE

To describe a novel observational study that supplemented primary care electronic health record (EHR) data with sample collection and patient diaries.

METHODS

The study was set in primary care in England. A list of 3974 potentially eligible patients was compiled using data from the Clinical Practice Research Datalink. Interested general practices opted into the study then confirmed patient suitability and sent out postal invitations. Participants completed a drug-use diary and provided saliva samples to the research team to combine with EHR data.

RESULTS

Of 252 practices contacted to participate, 66 (26%) mailed invitations to patients. Of the 3974 potentially eligible patients, 859 (22%) were at participating practices, and 526 (13%) were sent invitations. Of those invited, 117 (22%) consented to participate of whom 86 (74%) completed the study.

CONCLUSIONS

We have confirmed the feasibility of supplementing EHR with data collected directly from patients. Although the present study successfully collected essential data from patients, it also underlined the requirement for improved engagement with both patients and general practitioners to support similar studies.

Health Data for Public Health: Towards New Ways of Combining Data Sources to Support Research Efforts in Europe

Objectives: To present the European landscape regarding the re-use of health administrative data for research. Methods: We present some collaborative projects and solutions that have been developed by Nordic countries, Italy, Spain, France, Germany, and the UK, to facilitate access to their health data for research purposes. Results: Research in public health is transitioning from siloed systems to more accessible and re-usable data resources. Following the example of the Nordic countries, several European countries aim at facilitating the re-use of their health administrative databases for research purposes. However, the ecosystem is still a complex patchwork, with different rules, policies, and processes for data provision. Conclusion: The challenges are such that with the abundance of health administrative data, only a European, overarching public health research infrastructure, is able to efficiently facilitate access to this data and accelerate research based on these highly valuable resources.

Development and validation of algorithms for the detection of statin myopathy signals from electronic medical records

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.

Data Resource Profile: Clinical Practice Research Datalink (CPRD)

The Clinical Practice Research Datalink (CPRD) is an ongoing primary care database of anonymised medical records from general practitioners, with coverage of over 11.3 million patients from 674 practices in the UK. With 4.4 million active (alive, currently registered) patients meeting quality criteria, approximately 6.9% of the UK population are included and patients are broadly representative of the UK general population in terms of age, sex and ethnicity. General practitioners are the gatekeepers of primary care and specialist referrals in the UK. The CPRD primary care database is therefore a rich source of health data for research, including data on demographics, symptoms, tests, diagnoses, therapies, health-related behaviours and referrals to secondary care. For over half of patients, linkage with datasets from secondary care, disease-specific cohorts and mortality records enhance the range of data available for research. The CPRD is very widely used internationally for epidemiological research and has been used to produce over 1000 research studies, published in peer-reviewed journals across a broad range of health outcomes. However, researchers must be aware of the complexity of routinely collected electronic health records, including ways to manage variable completeness, misclassification and development of disease definitions for research.

Phenotyping Adverse Drug Reactions: Statin-Related Myotoxicity

It is unclear the extent to which best practices for phenotyping disease states from electronic medical records (EMRs) translate to phenotyping adverse drug events. Here we use statin-induced myotoxicity as a case study to identify best practices in this area. We compared multiple phenotyping algorithms using administrative codes, laboratory measurements, and full-text keyword matching to identify statin-related myopathy from EMRs. Manual review of 300 deidentified EMRs with exposure to at least one statin, created a gold standard set of 124 cases and 176 controls. We tested algorithms using ICD-9 billing codes, laboratory measurements of creatine kinase (CK) and keyword searches of clinical notes and allergy lists. The combined keyword algorithms produced were the most accurate (PPV=86%, NPV=91%). Unlike in most disease phenotyping algorithms, addition of ICD9 codes or laboratory data did not appreciably increase algorithm accuracy. We conclude that phenotype algorithms for adverse drug events should consider text based approaches.

Phenotype standardization for statin-induced myotoxicity

Statins are widely used lipid-lowering drugs that are effective in reducing cardiovascular disease risk. Although they are generally well tolerated, they can cause muscle toxicity, which can lead to severe rhabdomyolysis. Research in this area has been hampered to some extent by the lack of standardized nomenclature and phenotypic definitions. We have used numerical and descriptive classifications and developed an algorithm to define statin-related myotoxicity phenotypes, including myalgia, myopathy, rhabdomyolysis, and necrotizing autoimmune myopathy.

Information technology for clinical, translational and comparative effectiveness research. Findings from the section clinical research informatics

OBJECTIVE

To select and summarize key contributions to current research in the field of Clinical Research Informatics (CRI).

METHOD

A bibliographic search using a combination of MeSH and free terms search over PubMed was performed followed by a blinded review.

RESULTS

The review process resulted in the selection of four papers illustrating various aspects of current research efforts in the area of CRI. The first paper tackles the challenge of extracting accurate phenotypes from Electronic Healthcare Records (EHRs). Privacy protection within shared de-identified, patient-level research databases is the focus of the second selected paper. Two other papers exemplify the growing role of formal representation of clinical data - in metadata repositories - and knowledge - in ontologies - for supporting the process of reusing data for clinical research.

CONCLUSIONS

The selected articles demonstrate how concrete platforms are currently achieving interoperability across clinical research and care domains and have reached the evaluation phase. When EHRs linked to genetic data have the potential to shift the research focus from research driven patient recruitment to phenotyping in large population, a key issue is to lower patient re-identification risks for biomedical research databases. Current research illustrates the potential of knowledge engineering to support, in the coming years, the scientific lifecycle of clinical research.