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Invited Commentary | ONLINE FIRST

Gone Fishing!: On the “Real-World” Accuracy of Computed Tomographic Coronary Angiography Comment on the “Ontario Multidetector Computed Tomographic Coronary Angiography Study”

George A. Diamond, MD; Sanjay Kaul, MD
Arch Intern Med. 2011;171(11):1029-1031. doi:10.1001/archinternmed.2011.75.
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In this issue of the Archives, Chow and colleagues describe a multicenter “field evaluation” of computed tomographic coronary angiography (CTCA) in 169 patients undergoing conventional CA among 594 candidates with suspected coronary artery disease and report that its sensitivity, specificity, and predictive accuracy varied widely from center to center.

There are numerous reasons for this variability. For example, test likelihoods are well known to vary with the severity of disease (the greater the severity, the higher the sensitivity and the lower the specificity) and with the threshold for categorical interpretation (the greater the threshold, the lower the sensitivity and the higher the specificity). Accordingly, if we wish to interpret the particular response in a particular patient, we need to know the sensitivity and specificity of that particular response rather than of some arbitrary spectrum of responses. Also, conventional diagnostic assessment is often highly subjective, even for the verification procedure itself. With respect to CA as a diagnostic standard, for example, a given patient can be considered severely diseased by one observer and entirely normal by another.1

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Potential variability in the performance of computed tomographic coronary angiography. A, Relationship between sensitivity and specificity (test likelihood) vs the magnitude of verification bias (the unobserved prevalence of positive responders among the entire 594 candidate population in OMCAS). The sensitivity and specificity values (adjusted for verification bias) are calculated from the raw “patient-based ≥50% stenosis” data in Table 3 of the OMCAS paper (1) using a previously published computer algorithm based on Bayes' theorem3:

Adjusted sensitivity = PPA × p(R)/[PPA × p(R) + NPA × (1 − p(R)],


Adjusted specificity = 1 − (1 − PPA) × p(R)/[(1 − PPA) × p(R) + (1 − NPA) × (1 − p(R)],

where p(R) is the overall prevalence of positive test responders (total positive test results/total patients tested), PPA is the positive predictive accuracy (true-positive test results/total positive test results, and NPA is the negative predictive accuracy (false-negative test results/total negative test results). Sensitivity is directly related and specificity is inversely related to the magnitude of verification bias.4 B, The upper and lower bounds for appropriate test use (based on sensitivity, specificity, and prior probability of disease) as a function of the magnitude of verification bias (the unobserved prevalence of positive responders among the entire 594 candidate population in OMCAS). The lower bound is the point below which false-positive responses exceed true-positive responses, and the upper bound is the point above which false-negative responses exceed true-negative responses. Only within the intermediate range defined by these bounds are all test responses more likely to be true than false.

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