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Prediction of Obstructive CAD and Prognosis in Stable Angina
Study Questions:
What is the prognostic ability of a new diagnostic model to predict obstructive coronary artery disease (CAD)?
Methods:
The investigators included 3,903 consecutive patients free of CAD and heart failure and suspected of angina, who were referred to a single center for assessment in 2012-2015. Obstructive CAD was defined from invasive angiography as a lesion requiring revascularization, >70% stenosis, or fractional flow reserve <0.8. Patients were followed (mean follow-up, 33 months) for myocardial infarction, unstable angina, heart failure, stroke, and death. The authors used standard time-to-first-event Cox regression analyses for the composite endpoint to assess the prognostic abilities of the prediction models.
Results:
The updated Diamond–Forrester (D-F) prediction model overestimated probability considerably. Mean pretest probability was 31.4%, while only 274 (7%) were diagnosed with obstructive CAD. A basic prediction model with age, gender, and symptoms demonstrated good discrimination with C-statistics of 0.86 (95% confidence interval, 0.84–0.88), while a clinical prediction model adding diabetes, family history, and dyslipidemia slightly improved the C-statistic to 0.88 (0.86–0.90) (p for difference between models < 0.0001). Quartiles of probability of CAD from the clinical prediction model provided good diagnostic and prognostic stratification. In the lowest quartiles, there were no cases of obstructive CAD and cumulative risk of the composite endpoint was <3% at 2 years.
Conclusions:
The authors concluded that the pretest probability model recommended in current European Society of Cardiology (ESC) guidelines substantially overestimates the likelihood of CAD when applied to a contemporary, unselected, all-comer population.
Perspective:
This study reports that a basic diagnostic model built on age, gender, and symptom characteristics performed well in predicting obstructive CAD, however, at much lower probability rates in all groups than expected from the updated D-F model. The diagnostic model was slightly improved by adding pretest clinical factors and powerfully discriminated between patients with low and moderate probability of obstructive CAD. Individuals in the lowest quartile of pretest probability did not have obstructive CAD and had a very low composite endpoint at 2-year follow-up of <3%. The results suggest that the currently recommended ESC guideline updated D-F pretest probability algorithm may be further downgraded and that noninvasive and invasive testing of patients with stable angina may be safely deferred in low-risk subgroups.
Keywords: Angina, Stable, Angina, Unstable, Angiography, Constriction, Pathologic, Coronary Artery Disease, Diabetes Mellitus, Dyslipidemias, Heart Failure, Myocardial Infarction, Myocardial Ischemia, Myocardial Revascularization, Secondary Prevention, Stroke
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