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A model to predict multivessel coronary artery disease from the exercise thallium-201 stress test
S G Pollock1, R D Abbott, C A Boucher
1Department of Medicine, University of Virginia School of Medicine, Charlottesville 22908.
Insights
Nonimaging variables like age and ST depression improve the detection of multivessel coronary artery disease using exercise thallium-201 imaging. A predictive model incorporating these factors accurately identifies disease probability.
Area of Science:
- Cardiology
- Diagnostic Imaging
- Nuclear Cardiology
Background:
- Multivessel coronary artery disease (CAD) is a significant predictor of cardiac events.
- Exercise thallium-201 scintigraphy is a common tool for diagnosing CAD.
- The diagnostic accuracy of imaging alone may be enhanced by incorporating clinical variables.
Purpose of the Study:
- To assess if nonimaging variables improve the detection of multivessel CAD using exercise thallium-201 imaging.
- To develop a predictive model for multivessel CAD based on exercise thallium-201 stress test data.
Main Methods:
- A cohort of 708 patients undergoing cardiac catheterization and exercise thallium-201 stress testing was analyzed.
- Logistic regression was used to identify independent predictors of multivessel disease.
- A predictive model was developed and validated on an independent patient cohort.
Main Results:
- Patient age and ST depression significantly improved multivessel CAD detection at all levels of thallium-201 abnormality.
- Three independent predictors of multivessel disease were identified: thallium-201 defects, ST depression, and age.
- The developed model demonstrated accuracy in predicting multivessel CAD in both the derivation and validation cohorts.
Conclusions:
- Nonimaging variables, specifically age and exercise-induced ST depression, provide independent diagnostic information for multivessel CAD detection.
- A predictive model utilizing exercise thallium-201 stress test data and clinical variables can accurately estimate the probability of multivessel disease.
Purpose:
The aim of this study was to (1) determine whether nonimaging variables add to the diagnostic information available from exercise thallium-201 images for the detection of multivessel coronary artery disease; and (2) to develop a model based on the exercise thallium-201 stress test to predict the presence of multivessel disease.
Patients And Methods:
The study populations included 383 patients referred to the University of Virginia and 325 patients referred to the Massachusetts General Hospital for evaluation of chest pain. All patients underwent both cardiac catheterization and exercise thallium-201 stress testing between 1978 and 1981.
Results:
In the University of Virginia cohort, at each level of thallium-201 abnormality (no defects, one defect, more than one defect), ST depression and patient age added significantly in the detection of multivessel disease. Logistic regression analysis using data from these patients identified three independent predictors of multivessel disease: initial thallium-201 defects, ST depression, and age. A model was developed to predict multivessel disease based on these variables. As might be expected, the risk of multivessel disease predicted by the model was similar to that actually observed in the University of Virginia population. More importantly, however, the model was accurate in predicting the occurrence of multivessel disease in the unrelated population studied at the Massachusetts General Hospital.
Conclusion:
It is, therefore, concluded that (1) nonimaging variables (age and exercise-induced ST depression) add independent information to thallium-201 imaging data in the detection of multivessel disease; and (2) a model has been developed based on the exercise thallium-201 stress test that can accurately predict the probability of multivessel disease in other populations.
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