Asymptomatic coronary artery disease in a Norwegian cohort with type 2 diabetes: a prospective angiographic study with intravascular ultrasound evaluation.

AIMS: The prevalence of asymptomatic coronary artery disease (CAD) in type 2 diabetes (T2D) is unclear. We investigated the extent and prevalence of asymptomatic CAD in T2D patients by utilizing invasive coronary angiography (ICA) and intravascular ultrasound (IVUS), and whether CAD progression, evaluated by ICA, could be modulated with a multi-intervention to reduce cardiovascular (CV) risk. METHODS: Fifty-six T2D patients with ≥ 1 additional CV risk factor participated in a 2 year randomized controlled study comparing hospital-based multi-intervention (multi, n = 30) versus standard care (stand, n = 26), with a pre-planned follow-up at year seven. They underwent ICA at baseline and both ICA and IVUS at year seven. ICA was described by conventional CAD severity and extent scores. IVUS was described by maximal intimal thickness (MIT), percent and total atheroma volume and compared with individuals without T2D and CAD (heart transplant donors who had IVUS performed 7-11 weeks post-transplant, n = 147). RESULTS: Despite CV risk reduction in multi after 2 years intervention, there was no between-group difference in the progression of CAD at year seven. Overall, the prevalence of CAD defined by MIT ≥ 0.5 mm in the T2DM subjects was 84%, and as compared to the non-T2DM controls there was a significantly higher atheroma burden (mean MIT, PAV and TAV in the T2D population were 0.75 ± 0.27 mm, 33.8 ± 9.8% and 277.0 ± 137.3 mm3 as compared to 0.41 ± 0.19 mm, 17.8 ± 7.3% and 134.9 ± 100.6 mm3 in the reference population). CONCLUSION: We demonstrated that a 2 year multi-intervention, despite improvement in CV risk factors, did not influence angiographic progression of CAD. Further, IVUS revealed that the prevalence of asymptomatic CAD in T2D patients is high, suggesting a need for a broader residual CV risk management using alternative approaches. Trial registration Clinical trials.gov id: NCT00133718 ( https://clinicaltrials.gov/ct2/show/NCT00133718 ).

Individual patient data meta-analysis for the clinical assessment of coronary computed tomography angiography: protocol of the Collaborative Meta-Analysis of Cardiac CT (CoMe-CCT).

BACKGROUND: Coronary computed tomography angiography has become the foremost noninvasive imaging modality of the coronary arteries and is used as an alternative to the reference standard, conventional coronary angiography, for direct visualization and detection of coronary artery stenoses in patients with suspected coronary artery disease. Nevertheless, there is considerable debate regarding the optimal target population to maximize clinical performance and patient benefit. The most obvious indication for noninvasive coronary computed tomography angiography in patients with suspected coronary artery disease would be to reliably exclude significant stenosis and, thus, avoid unnecessary invasive conventional coronary angiography. To do this, a test should have, at clinically appropriate pretest likelihoods, minimal false-negative outcomes resulting in a high negative predictive value. However, little is known about the influence of patient characteristics on the clinical predictive values of coronary computed tomography angiography. Previous regular systematic reviews and meta-analyses had to rely on limited summary patient cohort data offered by primary studies. Performing an individual patient data meta-analysis will enable a much more detailed and powerful analysis and thus increase representativeness and generalizability of the results. The individual patient data meta-analysis is registered with the PROSPERO database (CoMe-CCT, CRD42012002780). METHODS/DESIGN: The analysis will include individual patient data from published and unpublished prospective diagnostic accuracy studies comparing coronary computed tomography angiography with conventional coronary angiography. These studies will be identified performing a systematic search in several electronic databases. Corresponding authors will be contacted and asked to provide obligatory and additional data. Risk factors, previous test results and symptoms of individual patients will be used to estimate the pretest likelihood of coronary artery disease. A bivariate random-effects model will be used to calculate pooled mean negative and positive predictive values as well as sensitivity and specificity. The primary outcome of interest will be positive and negative predictive values of coronary computed tomography angiography for the presence of coronary artery disease as a function of pretest likelihood of coronary artery disease, analyzed by meta-regression. As a secondary endpoint, factors that may influence the diagnostic performance and clinical value of computed tomography, such as heart rate and body mass index of patients, number of detector rows, and administration of beta blockade and nitroglycerin, will be investigated by integrating them as further covariates into the bivariate random-effects model. DISCUSSION: This collaborative individual patient data meta-analysis should provide answers to the pivotal question of which patients benefit most from noninvasive coronary computed tomography angiography and thus help to adequately select the right patients for this test.