Transversal case–control single centre study including patients referred for assessment of possible CAD using CT. CAC and CTA were performed. Clinical and laboratorial data were collected. This study was approved by our Institution’s Cardiology Department Supervisor and Ethics Committee in December 2008. All patients provided informed consent before undergoing CT.

A total of 154 consecutive patients, without known history of severe aortic stenosis or coronary artery disease, were referred for CT for evaluation of coronary artery disease, in a non-acute setting, between January 8, 2009, and September 30, 2010. Patients over 18 years old were included in the study. Exclusion criteria were contraindication to iodine-based contrast agents, glomerular filtration rate <30 mL/min, pregnancy, inability to sustain a 15-s breath-hold, cardiac arrhythmias or uninterpretable CTA.

An extensive review of clinical records from our outpatient clinic, hospital ward and emergency department admission(s), was performed by 2 co-investigators, and the following data were collected: demographic features, cardiovascular risk factors, cardiovascular risk scores (SCORE [15] and Framingham [16]), previous medical history (including history of CAD and severe aortic stenosis), physical examination (including weight, height, body mass index, blood pressure and heart rate) and analytical study: total cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, serum creatinine and C-reactive protein. GFR was calculated by MDRD (Modification of Diet in Renal Disease) formula.

Hypertension, dyslipidemia and diabetes were defined by self-reporting during history-taking and/or history or use of specific therapy. Family history of CAD was defined as fatal or non-fatal myocardial infarction or coronary revascularisation in first-degree male relatives <55 years or first degree female relatives <65 years. History of cigarette smoking was considered present if a subject was a current or former smoker.

All scans were performed using a 16-slice CT scanner (Brilliance 16; Philips Medical Systems, Eindhoven, the Netherlands). A prospective scan without contrast enhancement was performed to measure CAC and AoVC (sequential scan with 8 × 3 mm collimation, tube current 55mAs at 120 kV, 3 mm width), followed by 16-slice contrast-enhanced spiral scan of the heart performed with ECG gating and retrospective post processing. CTA parameters: 16 × 0.75 mm collimation, 400 ms gantry rotation, pitch of 0.298, tube voltage at 120 kV, maximum current of 600—800 mAs depending on patient size, half-scan reconstruction mode and imaging craniocaudal direction. All patients received 5 mg of sublingual isosorbide dinitrate 5 min before CTA acquisition. Patients with a heart rate >65 bpm received 50-200 mg of oral metoprolol. A bolus (120-130 mL) of iodinated contrast agent (370 mOsm) was intravenously injected (4–5.5 ml/s). A region of interest was placed in the descending thoracic aorta and image acquisition was automatically initiated using bolus tracking (selected threshold: 110 Hounsfield units [HU]). Images were reconstructed in five phases of the cardiac cycle (0, 37.5, 62.5, 75 and 87.5 % of the R-R interval) to minimize motion artifacts. The estimated effective radiation dose used was: per CAC and AoVC - median 0,8 mSv (maximum: 0,32 mSv; minimum: 1.28 mSv); per CTA – median 10,44 mSv, maximum 17,44 mSv, minimum 9,24 mSv (CTA per se: median 10 mSv, maximum 17 mSv, minimum 8,8 mSv; Locator: median 0,44 mSv, maximum 1,28 mSv, minimum 0,32 mSv); per complete scan: median 11 mSv (maximum 18 mSv, minimum 10 mSv).

CTA image evaluation was performed on a separate 3D workstation (Brilliance workstation, Philips Medical Systems, Eindhoven, the Netherlands) by two experienced reviewers. CTA were analysed by assessment of axial slices, multiplanar reformations (along the vessel axis and cross-sectional images), and the three thin-slab maximum intensity projections. The coronary artery tree was divided into proximal, medial and distal, according to classic angiographic definition. Plaques were classified as obstructive or non-obstructive using a 50 % threshold of luminal narrowing. The presence of obstructive CAD was defined by >50 % lumen narrowing, and was classified according to the number of vessels with obstructive CAD: single-vessel disease (one vessel) or multivessel disease (two or three vessels). Plaques were defined as structures >1 mm² within and/or adjacent to the vessel lumen, distinct from lumen and surrounding tissue. Plaques were classified as: calcified – if they had more than 50 % calcified tissue (density >130HU in native scans), mixed – if composed with <50 % calcium, and non-calcified lesions - without any calcium. After independent assessment, the final diagnosis was obtained by a consensus interpretation of the two reviewers.

CAC and AoVC were measured using a workstation (Aquarius iNtuition, version 4.4, Tera Recon, Inc, San Mateo, CA, USA). CAC was measured for each patient using the automatic calcium detection algorithm of the workstation, according to Agatston method, with a calcium threshold of 130 HU. AoVC was measured and quantified with the same lesion definition, the same software and the same acquisition of CAC. The presence of AoVC was defined as any calcified lesion detected within the aortic valve leaflet area or extending to the aortic root. Calcium within the aortic sinuses or thoracic aorta was excluded from analysis and not measured as AoVC.

The primary endpoint of this study was detection of obstructive CAD by CTA.

The secondary endpoint was the identification of patients with CAC <400 and AoVC >61 or AoVC >454 with obstructive CAD.

Statistical analysis was performed using SPSS, v. 17.0. Baseline characteristics were described with counts and proportions for categorical data; continuous variables with normal distribution were described by mean ± standard deviation, while continuous variables with non-normal distribution were described with median, minimum and maximum value. The Kolmogorov-Smirnov test was used to test the normal distribution of continuous variables. The Chi-square test, Student’s t-test and non-parametric equivalent tests were used when appropriate. Regression estimation techniques were applied to replace missing values whenever the number of missing values was negligible, otherwise cases with missing values would be omitted. P values <0.05 (two-sided) were considered statistically significant.

A comparative analysis was performed to evaluate potential predictors of AoVC. Three cutoffs were used for this purpose: AoVC ≥ 1 (presence of any aortic valve calcification), AoVC > 61 (threshold defined according to the Youden index on ROC curve analysis - optimal sensitivity and specificity: 66.7 % sensitivity and 76.7 % specificity) and AoVC > 454 (cutoff of AoVC with equivalent specificity (95.8 %) to that of CAC > 400 for obstructive CAD in our sample). We also performed a comparative analysis to evaluate a potential association with obstructive CAD. Potential predictors presented as continuous variables were converted into binary variables using as cutoff point the Youden index. Binary logistic regression analysis was performed to identify, among potential associations, the independent predictors of AoVC and obstructive CAD. A predictor model of obstructive CAD was created using CAC and AoVC (PM CAC + AoVC) using continuous variables in binary logistic regression with the method Enter; regression coefficients obtained were then applied to calculate predicted risks according to predictor model. The discriminatory power for predicting obstructive CAD using AoVC, CAC, Framingham, SCORE and the predictor model was then evaluated through receiver operating characteristic (ROC) curves. Comparisons of areas under ROC curves (AUC) were performed using MedCalc for Windows version 9.2.0.1.

Finally, the net reclassification improvement (NRI) was calculated according to the method described by Pencina et al. [17] to quantify the reclassification with CAC > 400 or AOVC > 61/ or >454, comparatively with CAC > 400, as a gatekeeper for CTA. A positive and significant NRI translates a net overall successful reclassification of subjects into more appropriate risk categories (e.g. a patient who reaches the primary endpoint that is reclassified into higher risk groups with CAC and AoVC or a subject who does not reach the primary endpoint that is reclassified into a lower risk category with CAC and AoVC). The amount of overall reclassification is translated by the extent of the NRI (a per cent value). A positive NRI value represents an adequate reclassification into the correct risk category, whereas a negative NRI represents a worse reclassification with the new risk stratification scheme.