Discussion
We analysed the incremental value of CTCS in the prediction of prevalent obstructive CAD. We showed CTCS to be a significant predictor, independent of other variables included in the model. Furthermore, we confirm that the prediction of prevalent obstructive CAD is mainly determined by age, sex and type of chest pain.
In four of the five models age and sex were significant predictors before the addition of CTCS. However, after including CTCS, both age and sex were no longer significant predictors in each of those models. Type of chest pain was a significant predictor of obstructive CAD, independent of other variables included in the model and independent of whether CTCS was included or not. CTCS proved to be an excellent and significant predictor of CAD with adjusted odds ratios close to 2. Apart from age and sex, most risk factors did not result in significant odds ratios which suggests that these risk factors are of minor importance in the prediction of prevalent obstructive CAD. Furthermore, the risk factors that were significant in the models without CTCS lost their significance after including CTCS.
Analysis of reclassification showed that adding CTCS yields reclassification of 34–47% of patients, most of which was correct. The reclassification calibration statistic demonstrated a lack of fit for all models, which decreased when CTCS was added in all models except for Shaw’s.
Numerous studies have previously reported on the incremental value of CTCS in the prediction of cardiovascular events and mortality in asymptomatic individuals [10–13]. Some studies have reported on the incremental value of CTCS in the prediction of prevalent obstructive CAD.
Both Guerci et al. [1] and Kennedy et al. [2] studied the relationship between obstructive CAD and CTCS, adjusting for various risk factors. Both studies found highly significant odds ratios for the CTCS in predicting the presence of obstructive CAD, but the odds ratios were lower compared to what we found. Budoff et al. [4] evaluated the value of CTCS in diagnosing CAD and showed that the addition of CTCS increased the AUC from 0.672 to 0.842. However, the models included only age and sex as clinical variables. Considering the fact that the type of chest pain was not taken into account, an increase in AUC with CTCS can be expected. However, the resulting area under the ROC curve for the model including CTCS was similar to what we found. Schmermund et al. [3] previously studied the value of CTCS in predicting the extent of CAD. They showed an independent and incremental value for CTCS in multiple linear regression in predicting the total number of segments per patient with ≥50% stenosis.

Study limitations
Our study assessed the prediction of ≥50% stenosis in at least one vessel. One could argue that physicians are primarily interested in diagnosing severe CAD, as these patients would be eligible for revascularisation whereas others can be adequately treated medically. Likewise, physicians might be primarily interested in predicting future cardiovascular adverse events. However, we did not consider prognosis in this analysis.
All patients in our study were referred for CCA based on their presentation or functional testing that suggested the presence of cardiac ischaemia. In this way a high-risk population was selected, which could have biased our results. Unfortunately this limitation is inherent to the study design. Further research is necessary to determine the value of CTCS in other (e.g. lower risk) populations.
Also, risk factors such as type of chest pain, smoking status and family history of CAD were obtained by interviewing the patient. Potentially, this method underestimates their predictive effects as compared to the predictive effect of the CTCS, which was directly measured.
Tables 4 and 5 illustrate how CTCS influences the classification of patients in probability categories. However, the limitations of reclassification measures in the context of this research should be taken into account. Our sample size was too small to reliably assess reclassification. For example, the RCS only uses the cross-classified cells containing at least 20 observations. In Table 4 only four cells contain 20 or more observations, implying that a substantial amount of (correctly) reclassified patients are ignored. Thus, in our study the reclassification percentages, NRI and IDI indices are more reliable than the RCS.
Ideally, the probability categories should be based on clinically relevant cutoffs. However, no well-established clinically relevant probability threshold exists. The probability of CAD is commonly defined as low (<30%), intermediate (≥30–70%) and high (≥70%) [26]. In our view the intermediate category is rather wide, which is why we divided this category into low–intermediate (≥30–50%) and intermediate–high (≥50–70%). It should be noted that the overall percentage of reclassification is highly dependent on the choice and number of probability categories.

Clinical implications
Our results demonstrate that the estimation of the probability of obstructive CAD can be improved by including CTCS. This implies that clinicians can make better decisions as to whether a particular patient would benefit from further testing, for example CTCA or CCA. In low-risk patients, a CTCS of 0 could exclude CAD and avoid further testing using CTCA. Hereby, one also avoids the intravenous administration of contrast agent, the extra radiation exposure, and extra scan time and costs associated with CTCA. In patients with a low CTCS, CCA can be avoided and further non-invasive testing would be preferred. In patients with an intermediate CTCS, a CTCA might be the optimal next step. In patients with a high CTCS, direct CCA might be justified because of the high probability of CAD. All in all, CTCS could be useful as a triage test for patients who are suspected of having CAD.
We confirmed that the prediction of significant CAD is primarily driven by the patient’s symptoms. A detailed history of the patient’s symptoms remains most important in the diagnostic work-up of patients with suspected CAD. However, history taking is difficult and subjective, therefore limiting our ability to accurately predict the presence of CAD. Hence, further diagnostic testing will be important, even in patients with a low to intermediate probability of CAD.
On the other hand, the harms and costs of obtaining CTCS should be considered. Kim [27] studied the radiation dose and cancer risk of CTCS screening (every 5 years) in asymptomatic individuals. They concluded that the excess lifetime cancer risk was 42 (62) per 100,000 men (women). It is important to note that our study assessed the value of a single CTCS in symptomatic patients, for whom the excess lifetime cancer risk will be lower and small compared with the risk of missing a CAD diagnosis. Moreover, CTCS could reduce the use of additional testing in patients with a low CTCS and a low probability of CAD, thereby reducing the total radiation exposure.
Although performing a CTCS measurement is a fast, low-dose and relatively inexpensive procedure, the harms and benefits should be considered in a cost-effectiveness analysis.