Tessa S S Genders1, Adrian Coles1, Udo Hoffmann2, Manesh R Patel1, Daniel B Mark1, Kerry L Lee1, Ewout W Steyerberg3, M G Myriam Hunink4, Pamela S Douglas5. 1. Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina. 2. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. 3. Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands. 4. Department of Epidemiology and Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts. 5. Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina. Electronic address: pamela.douglas@duke.edu.
Abstract
OBJECTIVES: This study sought to externally validate prediction models for the presence of obstructive coronary artery disease (CAD). BACKGROUND: A better assessment of the probability of CAD may improve the identification of patients who benefit from noninvasive testing. METHODS: 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) were included. 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 3 models: basic model (age, sex, 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 to 0.72), 0.72 (95% CI: 0.69 to 0.74), and 0.86 (95% CI: 0.85 to 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 2 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: Existing clinical prediction models can identify patients with a low probability of obstructive CAD. Obstructive CAD on ICA was imputed for 61% of patients; hence, further validation is necessary.
OBJECTIVES: This study sought to externally validate prediction models for the presence of obstructive coronary artery disease (CAD). BACKGROUND: A better assessment of the probability of CAD may improve the identification of patients who benefit from noninvasive testing. METHODS: Stable chest painpatients from the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial with computed tomography angiography (CTA) or invasive coronary angiography (ICA) were included. 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 3 models: basic model (age, sex, 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 to 0.72), 0.72 (95% CI: 0.69 to 0.74), and 0.86 (95% CI: 0.85 to 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 2 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: Existing clinical prediction models can identify patients with a low probability of obstructive CAD. Obstructive CAD on ICA was imputed for 61% of patients; hence, further validation is necessary.
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