The External Validity of Prediction Models for the Diagnosis of Obstructive Coronary Artery Disease in Patients With Stable Chest Pain
| Autor: | Daniel B. Mark, Pamela S. Douglas, Udo Hoffmann, Manesh R. Patel, Tessa S. S. Genders, Kerry L. Lee, Adrian Coles, Ewout W. Steyerberg, M. G. Myriam Hunink |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
medicine.medical_specialty
medicine.diagnostic_test business.industry CAD 030204 cardiovascular system & hematology Chest pain medicine.disease Confidence interval Coronary artery disease External validity 03 medical and health sciences Stenosis 0302 clinical medicine Internal medicine medicine Cardiology Radiology Nuclear Medicine and imaging cardiovascular diseases 030212 general & internal medicine Radiology medicine.symptom Cardiology and Cardiovascular Medicine business Dyslipidemia Computed tomography angiography |
| Zdroj: | JACC: Cardiovascular Imaging. 11:437-446 |
| ISSN: | 1936-878X |
| DOI: | 10.1016/j.jcmg.2017.02.020 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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