Author + information
- Received January 18, 2017
- Revision received February 13, 2017
- Accepted February 15, 2017
- Published online March 5, 2018.
- Tessa S.S. Genders, MD, PhDa,
- Adrian Coles, PhDa,
- Udo Hoffmann, MD, MPHb,
- Manesh R. Patel, MDa,
- Daniel B. Mark, MD, MPHa,
- Kerry L. Lee, PhDa,
- Ewout W. Steyerberg, PhDc,
- M.G. Myriam Hunink, MD, PhDd,e,
- Pamela S. Douglas, MDa,∗ (, )
- on behalf of the CAD Consortium and the PROMISE Investigators
- aDuke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
- bDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- cDepartment of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands
- dDepartment of Epidemiology and Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- eDepartment of Health Policy and Management, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
- ↵∗Address for correspondence:
Dr. Pamela S. Douglas, Duke Clinical Research Institute, 7022 North Pavilion DUMC, P.O. Box 17969, Durham, North Carolina 27715.
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.
- computed tomography angiography
- coronary artery disease
- invasive coronary angiography
- prediction models
The PROMISE trial was supported by grants R01HL098237, R01HL098236, R01HL98305, and R01HL098235 from the National Heart, Lung, and Blood Institute. The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of this paper, and its final contents. This paper does not necessarily represent the official views of National Heart, Lung, and Blood Institute. Dr. Hoffmann has received research grants (significant) from the American College of Radiology Imaging Network, HeartFlow, Siemens Healthcare, Pfizer, and Genzyme. Dr. Patel has received research grants (significant) from AstraZeneca, Janssen, and HeartFlow; and has served on the advisory board for AstraZeneca, Janssen, Bayer, and Genzyme. Dr. Mark has served as a consultant for Medtronic, CardioDx, and St. Jude Medical; and has received research grants (significant) from Eli Lilly and Company, Medtronic, Bristol-Myers Squibb, AstraZeneca, Merck & Company, Oxygen Therapeutics, and Gilead. Dr. Hunink has received royalties from Cambridge University Press; grants and nonfinancial support from the European Society of Radiology; and nonfinancial support from the European Institute for Biomedical Imaging Research. Dr. Douglas has received research grants (significant) from GE and HeartFlow. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received January 18, 2017.
- Revision received February 13, 2017.
- Accepted February 15, 2017.
- 2018 American College of Cardiology Foundation
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