Defining Disease Phenotypes Using National Linked Electronic Health Records: A Case Study of Atrial Fibrillation

Abstract

BackgroundNational electronic health records (EHR) are increasingly used for research but identifying disease cases is challenging due to differences in information captured between sources (e.g. primary and secondary care). Our objective was to provide a transparent, reproducible model for integrating these data using atrial fibrillation (AF), a chronic condition diagnosed and managed in multiple ways in different healthcare settings, as a case study.MethodsPotentially relevant codes for AF screening, diagnosis, and management were identified in four coding systems: Read (primary care diagnoses and procedures), British National Formulary (BNF; primary care prescriptions), ICD-10 (secondary care diagnoses) and OPCS-4 (secondary care procedures). From these we developed a phenotype algorithm via expert review and analysis of linked EHR data from 1998 to 2010 for a cohort of 2.14 million UK patients aged ≥30 years. The cohort was also used to evaluate the phenotype by examining associations between incident AF and known risk factors.ResultsThe phenotype algorithm incorporated 286 codes: 201 Read, 63 BNF, 18 ICD-10, and four OPCS-4. Incident AF diagnoses were recorded for 72,793 patients, but only 39.6% (N = 28,795) were recorded in primary care and secondary care. An additional 7,468 potential cases were inferred from data on treatment and pre-existing conditions. The proportion of cases identified from each source differed by diagnosis age; inferred diagnoses contributed a greater proportion of younger cases (≤60 years), while older patients (≥80 years) were mainly diagnosed in SC. Associations of risk factors (hypertension, myocardial infarction, heart failure) with incident AF defined using different EHR sources were comparable in magnitude to those from traditional consented cohorts.ConclusionsA single EHR source is not sufficient to identify all patients, nor will it provide a representative sample. Combining multiple data sources and integrating information on treatment and comorbid conditions can substantially improve case identification.

Prognostic burden of heart failure recorded in primary care, acute hospital admissions, or both: a population‐based linked electronic health record cohort study in 2.1 million people

“…We included all patients with incident HF from 1 January 1997 to 26 March 2010 (when all record sources were concurrent). The diagnosis of HF was based on Read codes for CPRD data and International Classification of Diseases (ICD)‐9 or −10 codes in HES and ONS, using a phenotyping approach previously described (details on algorithms are available on http://www.caliberresearch.org/portal/ and Supplementary material online, Table S2 ) . We excluded patients under 30 years of age and those not registered during the study period at a CPRD practice, or whose CPRD practices did not submit data for at least 1 year before the diagnosis of HF.…”

Increasing knowledge of mental illness through secondary research of electronic health records: opportunities and challenges

“…One common approach is to link/merge data from different sources to gain more complete and detailed health records (see, e.g. Morley et al, 2014). However, data linkage research raises issues of its own that must be addressed, including interoperability, as outlined further below.…”

UK phenomics platform for developing and validating EHR phenotypes: CALIBER

“…Countries may also differ in the opportunities for validation: e.g. in the UK cross-referencing against multiple EHR sources, prognostic validation and risk factor validation are all made possible by nationwide population-based records [28][29][30][31][32]. In contrast with the US, only recently have scalable methods been developed to access the entire hospital record for expert review [33] and text corpora are not available at scale [34].…”