title: Coding, recording and incidence of different forms of coronary heart disease in primary care.
abstract: OBJECTIVE
  To evaluate the coding, recording and incidence of coronary heart disease (CHD) in primary care electronic medical records.
  
  
  METHODS
  Data were drawn from the UK General Practice Research Database. Analyses evaluated the occurrence of 271 READ medical diagnostic codes, including categories for 'Angina', 'Myocardial Infarction', 'Coronary Artery Bypass Grafting' (CABG), 'percutaneous transluminal coronary angioplasty' (PCTA) and 'Other Coronary Heart Disease'. Time-to-event analyses were implemented to evaluate occurrences of different groups of codes after the index date.
  
  
  RESULTS
  Among 300,020 participants aged greater than 30 years there were 75,197 unique occurrences of coronary heart disease codes in 24,244 participants, with 12,495 codes for incident events and 62,702 for prevalent events. Among incident event codes, 3,607 (28.87%) were for angina, 3,262 (26.11%) were for MI, 514 (4.11%) for PCTA, 161 (1.29%) for CABG and 4,951 (39.62%) were for 'Other CHD'. Among prevalent codes, 20,254 (32.30%) were for angina, 3,644 (5.81%) for MI, 34,542 (55.09%) for 'Other CHD' and 4,262 (6.80%) for CABG or PCTA. Among 3,685 participants initially diagnosed exclusively with 'Other CHD' codes, 17.1% were recorded with angina within 5 years, 5.6% with myocardial infarction, 6.3% with CABG and 8.6% with PCTA. From 2000 to 2010, the overall incidence of CHD declined, as did the incidence of angina, but the incidence of MI did not change. The frequency of CABG declined, while PCTA increased.
  
  
  CONCLUSIONS
  In primary care electronic records, a substantial proportion of coronary heart disease events are recorded with codes that do not distinguish between different clinical presentations of CHD. The results draw attention to the need to improve coding practice in primary care. The results also draw attention to the importance of code selection in research studies and the need for sensitivity analyses using different sets of codes.
authors.affiliation: Department of Primary Care and Public Health Sciences, King's College London, London, United Kingdom. nawaraj.bhattarai@kcl.ac.uk
authors.forename: Nawaraj
authors.initials: N
authors.lastname: Bhattarai
authors.forename: Judith
authors.initials: J
authors.lastname: Charlton
authors.forename: Caroline
authors.initials: C
authors.lastname: Rudisill
authors.forename: Martin C
authors.initials: MC
authors.lastname: Gulliford
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D059019:Clinical Coding / Q000379:methods*; D003327:Coronary Disease / Q000145:classification* / Q000453:epidemiology*; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011320:Primary Health Care / Q000706:statistics & numerical data; D006113:United Kingdom