When used as binary predictors, elevated risk scores on all 3 algorithms were strongly associated with a missed opportunity for treatment of early acute cardiac ischemia (Table 5). When used as continuous predictors, the FRS (odds ratio [OR], 1.19; 95% confidence interval [CI], 1.14-1.24) and the Diamond and Forrester model (OR, 1.03; 95% CI, 1.02-1.04) demonstrated an increase in the odds of a missed opportunity for each 1% increase in risk score. After adjusting for patient sex, the FRS was independently associated with missed-opportunity AMIs, although there was slight attenuation of the effect sizes. Using a binary cutoff FRS of 10% or greater, the sex-adjusted odds ratio was 16.5 (95% CI, 7.8-34.8), and using the FRS as a continuous predictor, the sex-adjusted OR was 1.17 (95% CI, 1.13-1.22). When used as a categorical predictor with FRS less than 10% as the reference group, the sex-adjusted OR for FRS 10% to 19% was 12.4 (95% CI, 5.3-28.6) and for 20% or greater was 20.8 (95% CI, 9.1-47.7). Similar patterns of modest attenuation of effect sizes for the Diamond and Forrester model and the Goldman prediction tool were noted after adjusting for sex (data not shown). We determined the sensitivity and specificity of these instruments using different risk score thresholds to better define their utility in the clinical setting (Table 6). Using a moderate risk score as the cutoff point, the sensitivities of the FRS, Diamond and Forrester probability, and Goldman risk tool were 85%, 90%, and 43%, respectively, and the specificities were 75%, 52%, and 91%.