title: Assessment tools for unrecognized myocardial infarction: a cross-sectional analysis of the REasons for Geographic and Racial Differences in Stroke population.
abstract: BACKGROUND
  Routine electrocardiograms (ECGs) are not recommended for asymptomatic patients because the potential harms are thought to outweigh any benefits. Assessment tools to identify high risk individuals may improve the harm versus benefit profile of screening ECGs. In particular, people with unrecognized myocardial infarction (UMI) have elevated risk for cardiovascular events and death.
  
  
  METHODS
  Using logistic regression, we developed a basic assessment tool among 16,653 participants in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study using demographics, self-reported medical history, blood pressure, and body mass index and an expanded assessment tool using information on 51 potential variables. UMI was defined as electrocardiogram evidence of myocardial infarction without a self-reported history (n = 740).
  
  
  RESULTS
  The basic assessment tool had a c-statistic of 0.638 (95% confidence interval 0.617-0.659) and included age, race, smoking status, body mass index, systolic blood pressure, and self-reported history of transient ischemic attack, deep vein thrombosis, falls, diabetes, and hypertension. A predicted probability of UMI > 3% provided a sensitivity of 80% and a specificity of 30%. The expanded assessment tool had a c-statistic of 0.654 (95% confidence interval 0.634-0.674). Because of the poor performance of these assessment tools, external validation was not pursued.
  
  
  CONCLUSIONS
  Despite examining a large number of potential correlates of UMI, the assessment tools did not provide a high level of discrimination. These data suggest defining groups with high prevalence of UMI for targeted screening will be difficult.
authors.affiliation: Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA. elevitan@uab.edu
authors.forename: Emily B
authors.initials: EB
authors.lastname: Levitan
authors.forename: Monika M
authors.initials: MM
authors.lastname: Safford
authors.forename: Meredith L
authors.initials: ML
authors.lastname: Kilgore
authors.forename: Elsayed Z
authors.initials: EZ
authors.lastname: Soliman
authors.forename: Stephen P
authors.initials: SP
authors.lastname: Glasser
authors.forename: Suzanne E
authors.initials: SE
authors.lastname: Judd
authors.forename: Paul
authors.initials: P
authors.lastname: Muntner
mesh_terms: D001741:Black or African American; D000368:Aged; D016009:Chi-Square Distribution; D003430:Cross-Sectional Studies; D003661:Decision Support Techniques; D004562:Electrocardiography; D005260:Female; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000453:epidemiology* / Q000208:ethnology; D016017:Odds Ratio; D018579:Patient Selection; D011237:Predictive Value of Tests; D015995:Prevalence; D011446:Prospective Studies; D012111:Residence Characteristics; D018570:Risk Assessment; D012307:Risk Factors; D020521:Stroke / Q000453:epidemiology* / Q000208:ethnology; D014481:United States / Q000453:epidemiology; D044465:White People