title: Defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation.
abstract: BACKGROUND
  National 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.
  
  
  METHODS
  Potentially 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.
  
  
  RESULTS
  The 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.
  
  
  CONCLUSIONS
  A 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.
authors.affiliation: Farr Institute of Health Informatics Research, University College London, London, United Kingdom, and Clinical Epidemiology, Department of Epidemiology and Public Health, University College London, London, United Kingdom; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Melbourne School of Global and Population Health, The University of Melbourne, Melbourne, Australia.
authors.forename: Katherine I
authors.initials: KI
authors.lastname: Morley
authors.affiliation: Farr Institute of Health Informatics Research, University College London, London, United Kingdom, and Clinical Epidemiology, Department of Epidemiology and Public Health, University College London, London, United Kingdom.
authors.forename: Joshua
authors.initials: J
authors.lastname: Wallace
authors.affiliation: Farr Institute of Health Informatics Research, University College London, London, United Kingdom, and Clinical Epidemiology, Department of Epidemiology and Public Health, University College London, London, United Kingdom.
authors.forename: Spiros C
authors.initials: SC
authors.lastname: Denaxas
authors.affiliation: Barts NIHR Biomedical Research Unit, Queen Mary University London, London, United Kingdom.
authors.forename: Ross J
authors.initials: RJ
authors.lastname: Hunter
authors.affiliation: Farr Institute of Health Informatics Research, University College London, London, United Kingdom, and Clinical Epidemiology, Department of Epidemiology and Public Health, University College London, London, United Kingdom; The Heart Hospital, University College London NHS Trust, London, United Kingdom.
authors.forename: Riyaz S
authors.initials: RS
authors.lastname: Patel
authors.affiliation: Farr Institute of Health Informatics Research, University College London, London, United Kingdom, and Clinical Epidemiology, Department of Epidemiology and Public Health, University College London, London, United Kingdom; London School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: Pablo
authors.initials: P
authors.lastname: Perel
authors.affiliation: Farr Institute of Health Informatics Research, University College London, London, United Kingdom, and Clinical Epidemiology, Department of Epidemiology and Public Health, University College London, London, United Kingdom.
authors.forename: Anoop D
authors.initials: AD
authors.lastname: Shah
authors.affiliation: Barts NIHR Biomedical Research Unit, Queen Mary University London, London, United Kingdom.
authors.forename: Adam D
authors.initials: AD
authors.lastname: Timmis
authors.affiliation: Barts NIHR Biomedical Research Unit, Queen Mary University London, London, United Kingdom.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Schilling
authors.affiliation: Farr Institute of Health Informatics Research, University College London, London, United Kingdom, and Clinical Epidemiology, Department of Epidemiology and Public Health, University College London, London, United Kingdom.
authors.forename: Harry
authors.initials: H
authors.lastname: Hemingway
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000453:epidemiology / Q000473:pathology; D015331:Cohort Studies; D057286:Electronic Health Records; D006761:Hospitals; D006801:Humans; D015994:Incidence; D008875:Middle Aged; D010641:Phenotype; D016016:Proportional Hazards Models; D012307:Risk Factors