title: Electrocardiogram abnormalities predict angiographic coronary artery disease in women with chest pain: results from the NHLBI WISE Study.
abstract: OBJECTIVE
  Noninvasive risk stratification for coronary artery disease (CAD) isless accurate in women than in men. Based on recent reports that gender-specific exercise electrocardiogram (ECG) parameters predict CAD, we evaluated the independent predictive value of the resting ECG for angiographic CAD in women with chest pain.
  
  
  METHODS
  Women (n = 850, mean age 58 years) with chest pain in the NHLBI Women's Ischemia Syndrome Evaluation (WISE) underwent 12-lead ECG testing and quantitative coronary angiography.
  
  
  RESULTS
  Significant angiographic CAD (> or = 50% stenosis in > or = 1 coronary) was present in 39% of women. Q waves in < or = 2 contiguous ECG leads were present in 107 women (13%), including 49 of 657 (7%) without history of infarction. Among 585 women without prior infarction orrevascularization, 48% of those with Q waves in contiguous leads versus 26% of others, had significant CAD (p = 0.003; odds ratio [OR] = 2.5, 95% confidence interval [CI] = 1.3-4.8). Women with Q waves in < or = 2 inferior ECG leads were particularly likely to have CAD (63 vs. 26% of others, p < 0.001; OR = 4.6,95% CI = 2.0-10.8). Other ECG findings predictive of CAD were any ST-T abnormality (OR = 1.9,95% CI = 1.3-2.8) and T-wave inversion (OR = 2.4, 95% CI = 1.3-4.2). In risk-adjusted analysis, inferior Q waves and T-wave inversion independently predicted significant CAD. When considered together with radionuclide perfusion test results, T-wave inversion on resting ECG added significant independent predictive value (OR = 2.8, 95% CI = 1.1-7.2, p = 0.03).
  
  
  CONCLUSIONS
  Selected resting ECG parameters independently predict angiographic CAD in women with chest pain, including women who have also undergone radionuclide stress testing. Prospective studies should consider resting ECG parameters in diagnostic algorithms for CAD in women.
authors.affiliation: Epidemiology Data Center, Department of Epidemiology, University of Pittsburgh, Graduate School of Public Health, Pittsburgh, Pennsylvania 15213, USA.
authors.forename: Richard
authors.initials: R
authors.lastname: Holubkov
authors.forename: Carl J
authors.initials: CJ
authors.lastname: Pepine
authors.forename: Cheryl
authors.initials: C
authors.lastname: Rickens
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Reichek
authors.forename: William J
authors.initials: WJ
authors.lastname: Rogers
authors.forename: Barry L
authors.initials: BL
authors.lastname: Sharaf
authors.forename: George
authors.initials: G
authors.lastname: Sopko
authors.forename: C Noel Bairey
authors.initials: CN
authors.lastname: Merz
authors.forename: Sheryl F
authors.initials: SF
authors.lastname: Kelsey
authors.forename: Marian
authors.initials: M
authors.lastname: Olson
authors.forename: Karen M
authors.initials: KM
authors.lastname: Smith
authors.forename: Steven E
authors.initials: SE
authors.lastname: Reis
mesh_terms: D000465:Algorithms; D002637:Chest Pain / Q000209:etiology*; D017023:Coronary Angiography; D003327:Coronary Disease / Q000175:diagnosis*; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008875:Middle Aged; D011237:Predictive Value of Tests; D015995:Prevalence; D018570:Risk Assessment