Predictive value of 16-slice multidetector spiral computed tomography to detect significant obstructive coronary artery disease in patients at high risk for coronary artery disease: patient-versus segment-based analysis.
ABSTRACT In this study, we investigated the diagnostic value and limitations of multidetector computed tomography (MDCT)-based noninvasive detection of significant obstructive coronary artery disease (CAD) in a consecutive high-risk patient population with inclusion of all coronary segments.
In a prospective, blinded, standard cross-sectional technology assessment, a cohort of 33 consecutive patients with a positive stress test result underwent 16-slice MDCT and selective coronary angiography for the detection of significant obstructive CAD. We assessed the diagnostic accuracy of MDCT in a segment-based and a patient-based model and determined the impact of stenosis location and the presence of calcification on diagnostic accuracy in both models. Analysis of all 530 coronary segments demonstrated moderate sensitivity (63%) and excellent specificity (96%) with a moderate positive predictive value of 64% and an excellent negative predictive value (NPV) of 96% for the detection of significant coronary stenoses. Assessment restricted to either proximal coronary segments or segments with excellent image quality (83% of all segments) led to an increase in sensitivity (70% and 82%, respectively), and high specificities were maintained (94% and 93%, respectively). In a patient-based model, the NPV of MDCT for significant CAD was limited to 75%. Coronary calcification was the major cause of false-positive findings (94%).
For all coronary segments included, 16-slice MDCT has moderate diagnostic value for the detection of significant obstructive coronary artery stenosis in a population with a high prevalence of CAD. The moderate NPV of patient-based detection of CAD suggests a limited impact on clinical decision-making in high-risk populations.
- SourceAvailable from: PubMed Central[show abstract] [hide abstract]
ABSTRACT: Coronary artery calcification (CAC) scores are widely used to determine risk for Coronary Artery Disease (CAD). A CAC score does not have the diagnostic accuracy needed for CAD. This work uses a novel efficient approach to predict CAD in patients with low CAC scores. The study group comprised 86 subjects who underwent a screening health examination, including laboratory testing, CAC scanning, and cardiac angiography by 64-slice multidetector computed tomographic angiography. Eleven physiological variables and three personal parameters were investigated in proposed model. Logistic regression was applied to assess the sensitivity, specificity, and accuracy of when using individual variables and CAC score. Meta-analysis combined physiological and personal parameters by logistic regression. The diagnostic sensitivity of the CAC score was 14.3% when the CAC score was ≤30. Sensitivity increased to 57.13% using the proposed model. The statistically significant variables, based on beta values and P values, were family history, LDL-c, blood pressure, HDL-c, age, triglyceride, and cholesterol. The CAC score has low negative predictive value for CAD. This work applied a novel prediction method that uses patient information, including physiological and society parameters. The proposed method increases the accuracy of CAC score for predicting CAD.TheScientificWorldJOURNAL 01/2012; 2012:907062. · 1.73 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: BACKGROUND: The purpose of this study was to determine the cut-off values of Hounsfield units (HU) for the discrimination of plaque components and to evaluate the feasibility of measurement of the volume of plaque components using multi-detector row computed tomography (MDCT). Methods: Coronary lesions (125 lesions in 125 patients) were visualized by both integrated backscatter intravascular ultrasound (IB-IVUS) and 64-slice MDCT at the same site. The IB values were used as a gold standard to determine the cut off values of HU for the discrimination of plaque components. Results: Plaques were classified as lipid pool (n =50), fibrosis (n =65) or calcification (n =35) by IB-IVUS. The HU of lipid pool, fibrosis and calcification were 18 +/- 18 HU (-19 to 58 HU), 95 +/- 24 HU (46 to 154 HU) and 378 +/- 99 HU (188 to 605 HU), respectively. Using receiver operating characteristic curve analysis, a threshold of 50 HU was the optimal cutoff values to discriminate lipid pool from fibrosis. Lipid volume measured by MDCT was correlated with that measured by IB-IVUS (r =0.66, p <0.001), whereas fibrous volume was not (r =0.21, p =0.059). Conclusion: Lipid volume measured by MDCT was moderately correlated with that measured by IB-IVUS. MDCT may be useful for volumetric assessment of the lipid volume of coronary plaques, whereas the assessment of fibrosis volume was unstable.Cardiovascular Ultrasound 08/2012; 10(1):33. · 1.32 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: To determine whether a 3×2 table, using an intention to diagnose approach, is better than the "classic" 2×2 table at handling transparent reporting and non-evaluable results, when assessing the accuracy of a diagnostic test. Based on a systematic search for diagnostic accuracy studies of coronary computed tomography (CT) angiography, full texts of relevant studies were evaluated to determine whether they could calculate an alternative 3×2 table. To quantify an overall effect, we pooled diagnostic accuracy values according to a meta-analytical approach. Medline (via PubMed), Embase (via Ovid), and ISI Web of Science electronic databases. ELIGIBILITY CRITERIA : Prospective English or German language studies comparing coronary CT with conventional coronary angiography in all patients and providing sufficient data for a patient level analysis. 120 studies (10 287 patients) were eligible. Studies varied greatly in their approaches to handling non-evaluable findings. We found 26 studies (including 2298 patients) that allowed us to calculate both 2×2 tables and 3×2 tables. Using a bivariate random effects model, we compared the 2×2 table with the 3×2 table, and found significant differences for pooled sensitivity (98.2 (95% confidence interval 96.7 to 99.1) v 92.7 (88.5 to 95.3)), area under the curve (0.99 (0.98 to 1.00) v 0.93 (0.91 to 0.95)), positive likelihood ratio (9.1 (6.2 to 13.3) v 4.4 (3.3 to 6.0)), and negative likelihood ratio (0.02 (0.01 to 0.04) v 0.09 (0.06 to 0.15); (P<0.05)). Parameters for diagnostic performance significantly decrease if non-evaluable results are included by a 3×2 table for analysis (intention to diagnose approach). This approach provides a more realistic picture of the clinical potential of diagnostic tests.BMJ (Clinical research ed.). 01/2012; 345:e6717.