Chest Pain and Predicting CAD: Time to Change the Guidelines?
Chest Pain and Predicting CAD: Time to Change the Guidelines?
Dr. Hunink is senior author of:
Genders TS, Steyerberg EW, Hunink MG, et al. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ. 2012;344:e3485.
M.G. Myriam Hunink, MD, PhD, currently holds appointments as Professor of Clinical Epidemiology and Radiology at Erasmus University Medical Center Rotterdam, The Netherlands, and Adjunct Professor of Health Decision Science at the Harvard School of Public Health, Boston, Massachusetts. Dr. Hunink directs the Assessment of Radiological Technology (ART) program in Rotterdam.
Dr. Hunink's research focuses on the assessment of diagnostic imaging and image-guided therapeutic technologies, using techniques from clinical epidemiology, meta-analysis, decision modeling, and cost-effectiveness analysis. Her main interest is in the evaluation of technologies for the management of cardiovascular disease. Her special interests in methodological issues include the evaluation of diagnostic imaging and stochastic modeling.
Dr. Hunink was lead author on the textbook Decision-Making in Health and Medicine: Integrating Evidence and Values (Cambridge University Press, 2001). She is a past president of the Society for Medical Decision Making.
Dr. Hunink and coauthors have developed and validated a new prediction model, based on clinical presentation and cardiovascular risk factors, to improve the estimate for the probability of obstructive coronary artery disease (CAD) in patients with new-onset chest pain. The updated model, which is based on clinical presentation and cardiovascular risk factors, is intended to better identify patients who should undergo further diagnostic investigation. Current guidelines, including those issued by the American College of Cardiology (ACC)/American Heart Association (AHA), the European Society of Cardiology (ESC), and the National Institute for Health and Clinical Excellence (NICE) in the United Kingdom, all recommend using the Diamond and Forrester model or the Duke clinical score to estimate the pretest probability of CAD in patients with chest pain. However, recent data have suggested that the Diamond and Forrester model overestimates the probability, and neither this model nor the Duke clinical score have been validated in populations outside the United States.
To develop the new model, Dr. Hunink and colleagues at medical centers throughout Europe and the United States undertook a retrospective pooled analysis of individual patient data from 18 hospitals. Patients were eligible for the analysis if they had presented with stable chest pain and no history of CAD and if they were referred for catheter-based or CT-based coronary angiography (≥ 64 slice). Three prediction models were tested: a basic model including age, sex, symptoms, and setting; a clinical model consisting of the basic model plus diabetes, hypertension, dyslipidemia, and smoking; and an extended model including all of those clinical variables plus the CT coronary calcium score.
The analysis included 5677 patients (3283 men and 2394 women), of whom 1634 were found to have obstructive CAD (≥ 50% diameter stenosis in ≥ 1 vessel on catheter-based coronary angiography). In the clinical model, all potential predictors except body mass index were significantly associated with obstructive CAD. The clinical model improved prediction compared with the basic model (35% net reclassification improvement). Abnormal exercise ECG was found to have limited predictive value, but the coronary calcium score was a major predictor (102% net reclassification improvement). After addition of the coronary calcium score, most predictor effects decreased and age, dyslipidemia and smoking were no longer significant.
Cross-validation analysis indicated that the predictor effects in the clinical model were similar across data sets. Validation of the Duke clinical score in the same data sets showed significant overestimation of CAD.
Dr. Hunink spoke with Linda Brookes, MSc, for Medscape, to discuss the results of the study and the implications for diagnosis of CAD in patients who present with stable chest pain.
Best Evidence Interview With M.G. Myriam Hunink, MD, PhD
The Study
Dr. Hunink is senior author of:
Genders TS, Steyerberg EW, Hunink MG, et al. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ. 2012;344:e3485.
About the Interviewee
M.G. Myriam Hunink, MD, PhD, currently holds appointments as Professor of Clinical Epidemiology and Radiology at Erasmus University Medical Center Rotterdam, The Netherlands, and Adjunct Professor of Health Decision Science at the Harvard School of Public Health, Boston, Massachusetts. Dr. Hunink directs the Assessment of Radiological Technology (ART) program in Rotterdam.
Dr. Hunink's research focuses on the assessment of diagnostic imaging and image-guided therapeutic technologies, using techniques from clinical epidemiology, meta-analysis, decision modeling, and cost-effectiveness analysis. Her main interest is in the evaluation of technologies for the management of cardiovascular disease. Her special interests in methodological issues include the evaluation of diagnostic imaging and stochastic modeling.
Dr. Hunink was lead author on the textbook Decision-Making in Health and Medicine: Integrating Evidence and Values (Cambridge University Press, 2001). She is a past president of the Society for Medical Decision Making.
Background to the Interview
Dr. Hunink and coauthors have developed and validated a new prediction model, based on clinical presentation and cardiovascular risk factors, to improve the estimate for the probability of obstructive coronary artery disease (CAD) in patients with new-onset chest pain. The updated model, which is based on clinical presentation and cardiovascular risk factors, is intended to better identify patients who should undergo further diagnostic investigation. Current guidelines, including those issued by the American College of Cardiology (ACC)/American Heart Association (AHA), the European Society of Cardiology (ESC), and the National Institute for Health and Clinical Excellence (NICE) in the United Kingdom, all recommend using the Diamond and Forrester model or the Duke clinical score to estimate the pretest probability of CAD in patients with chest pain. However, recent data have suggested that the Diamond and Forrester model overestimates the probability, and neither this model nor the Duke clinical score have been validated in populations outside the United States.
To develop the new model, Dr. Hunink and colleagues at medical centers throughout Europe and the United States undertook a retrospective pooled analysis of individual patient data from 18 hospitals. Patients were eligible for the analysis if they had presented with stable chest pain and no history of CAD and if they were referred for catheter-based or CT-based coronary angiography (≥ 64 slice). Three prediction models were tested: a basic model including age, sex, symptoms, and setting; a clinical model consisting of the basic model plus diabetes, hypertension, dyslipidemia, and smoking; and an extended model including all of those clinical variables plus the CT coronary calcium score.
The analysis included 5677 patients (3283 men and 2394 women), of whom 1634 were found to have obstructive CAD (≥ 50% diameter stenosis in ≥ 1 vessel on catheter-based coronary angiography). In the clinical model, all potential predictors except body mass index were significantly associated with obstructive CAD. The clinical model improved prediction compared with the basic model (35% net reclassification improvement). Abnormal exercise ECG was found to have limited predictive value, but the coronary calcium score was a major predictor (102% net reclassification improvement). After addition of the coronary calcium score, most predictor effects decreased and age, dyslipidemia and smoking were no longer significant.
Cross-validation analysis indicated that the predictor effects in the clinical model were similar across data sets. Validation of the Duke clinical score in the same data sets showed significant overestimation of CAD.
Dr. Hunink spoke with Linda Brookes, MSc, for Medscape, to discuss the results of the study and the implications for diagnosis of CAD in patients who present with stable chest pain.