Why the study?
Does the addition of coronary calcium scores to clinical prediction models improve the estimation of pretest probability of coronary artery disease in low prevalence populations?
Does the addition of coronary calcium scores to clinical prediction models improve the estimation of pretest probability of coronary artery disease in low prevalence populations?
Adding coronary calcium scores to clinical prediction models improves the accuracy of estimating the pretest probability of coronary artery disease in low-prevalence populations.
May support refined pretest probability estimates in low-prevalence settings; extends models but remains hypothesis-generating without prospective validation.
Updated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates.
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Genders et al. (2012) studied this question.