title: Optimal strategies for identifying patients with myocardial infarction in general practice.
abstract: BACKGROUND
  In order to provide evidence-based secondary prevention of coronary heart disease (CHD) in general practice, eligible patients need to be identified. The optimal strategy is one in which all appropriate patients are identified with the least effort.
  
  
  OBJECTIVE
  The purpose of the study was to determine the optimal strategy to identify subjects with a myocardial infarction (MI) from general practice records using different search criteria.
  
  
  METHODS
  The study was a cross-sectional survey of 10 general practices in Tayside, Scotland. A random sample of all subjects aged over 35 (n = 5061) and registered with the general practices was obtained. The main outcome measures were sensitivity, specificity, positive predictive value (PPV) and yield (the number of records that need to be examined to detect a "true case").
  
  
  RESULTS
  Of the sample of 5061, 207 (4.1%) were defined to have had a "gold standard" MI. A Read code for ischaemic heart disease (IHD) had the highest sensitivity (95%) but with a poor PPV (52%). All searches had high specificities. The addition of a record of hospitalization for MI to the Read code for MI gave 100% sensitivity and high yield (1 in 1.11). In situations where the Read coding is of poor quality, the alternative search strategy of a hospital record of MI or receiving aspirin or nitrates was optimum.
  
  
  CONCLUSIONS
  Patients who had experienced an MI can be easily identified from a combination of a Read code for MI and a record of hospitalization for an MI giving 100% sensitivity and specificity with a yield of 1 in 1.11.
authors.affiliation: Tayside Centre for General Practice, Kirsty Semple Way, University of Dundee, Dundee DD2 4BF, Scotland, UK. p.t.donnan@dundee.ac.uk
authors.forename: Peter T
authors.initials: PT
authors.lastname: Donnan
authors.forename: Hamish T
authors.initials: HT
authors.lastname: Dougall
authors.forename: Frank M
authors.initials: FM
authors.lastname: Sullivan
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D003327:Coronary Disease / Q000517:prevention & control*; D003430:Cross-Sectional Studies; D004562:Electrocardiography; D005194:Family Practice; D005260:Female; D006801:Humans; D008297:Male; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000453:epidemiology; D011237:Predictive Value of Tests; D011897:Random Allocation; D012606:Scotland / Q000453:epidemiology; D012680:Sensitivity and Specificity