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Diagnostic Prediction Models for Coronary Artery Disease
Study Questions:
What is the validity of the coronary artery disease (CAD) consortium prediction models for the presence of obstructive CAD in chest pain patients from the United States?
Methods:
The investigators included stable chest pain patients from the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial with computed tomography angiography (CTA) or invasive coronary angiography (ICA). Of the 4,996 patients assigned to the anatomic testing (CTA) strategy in the PROMISE trial, 3,468 patients presented with chest pain and underwent CTA, ICA, or both and were analyzed. The authors assumed that patients with CTA showing 0% stenosis and a coronary artery calcium (CAC) score of 0 were free of obstructive CAD (≥50% stenosis) on ICA, and they multiply imputed missing ICA results based on clinical variables and CTA results. Predicted CAD probabilities were calculated using published coefficients for three models: basic model (age, sex, and chest pain type), clinical model (basic model + diabetes, hypertension, dyslipidemia, and smoking), and clinical + CAC score model. The authors assessed discrimination and calibration, and compared published effects with observed predictor effects.
Results:
In 3,468 patients (1,805 women; mean 60 years of age; 779 [23%] with obstructive CAD on CTA), the models demonstrated moderate-good discrimination, with C-statistics of 0.69 (95% confidence interval [CI], 0.67-0.72), 0.72 (95% CI, 0.69-0.74), and 0.86 (95% CI, 0.85-0.88) for the basic, clinical, and clinical + CAC score models, respectively. Calibration was satisfactory, although typical chest pain and diabetes were less predictive and CAC score was more predictive than was suggested by the models. Among the 31% of patients for whom the clinical model predicted a low (≤10%) probability of CAD, actual prevalence was 7%; among the 48% for whom the clinical + CAC score model predicted a low probability, the observed prevalence was 2%. In two sensitivity analyses excluding imputed data, similar results were obtained using CTA as the outcome, whereas in those who underwent ICA, the models significantly underestimated CAD probability.
Conclusions:
The authors concluded that existing clinical prediction models can identify patients with a low probability of obstructive CAD.
Perspective:
This study reports that the CAD consortium prediction models provided fairly accurate estimates of the pretest probability in the PROMISE trial, even though some predictors (i.e., typical chest pain and diabetes) appeared to be less predictive and the CAC score appeared to be more predictive than expected. The models can therefore be used to estimate the pretest probability in patients with chronic stable angina who are considered for noninvasive imaging tests. Overall, clinical prediction models for the presence of CAD provide well-calibrated predictions and can help clinicians to identify patients with a low probability of CAD. Since the primary analysis relied on imputed data for the presence of obstructive CAD on ICA, further confirmatory studies are indicated.
Keywords: Angina, Stable, Calibration, Chest Pain, Constriction, Pathologic, Coronary Angiography, Coronary Artery Disease, Diabetes Mellitus, Diagnostic Imaging, Dyslipidemias, Hypertension, Plaque, Atherosclerotic, Predictive Value of Tests, Smoking, Tomography, X-Ray Computed
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