Clinical & Experimental Cardiology
Open Access
ISSN: 2155-9880
+44 1300 500008
ISSN: 2155-9880
+44 1300 500008
Sarah Feger
Radiology, Germany
Scientific Tracks Abstracts: J Clin Exp Cardiolog
Coronary computed tomography angiography (CTA) is a non-invasive diagnostic test with high diagnostic accuracy in detecting coronary artery disease (CAD). Due to its very high negative predictive value, it is especially suitable for ruling out CAD in patients with low to intermediate pretest probability. For stable chest pain patients in this pretest probability range, the current European guidelines recommend the use of CTA. Additionally, in patients with unstable chest pain and a low CAD probability, CTA can be considered to detect relevant differential diagnoses. Aim is to test the accuracy of clinical pre-test probability for prediction of obstructive CAD in a pan-European setting. Patients with suspected CAD and stable chest pain who were clinically referred for ICA or CT were included by clinical sites participating in the pilot study of the European multi-centre DISCHARGE trial. Pre-test probability of CAD was determined using the Diamond-Forrester (D+F) prediction model initially introduced in 1979 and the updated D+F model from 2011. 1440 patients (654 female, 786 male) were included at 25 clinical sites from May, 2014 until July, 2017. Of these patients, 725 underwent CT, while 715 underwent ICA. Both prediction models overestimated the prevalence of obstructive CAD identified by ICA and CT (31.7%, 456 of 1440 patients, pre-test probabilities: initial D+F 57.9±31.9%, updated D+F 48.1±16.7%, both P<0.001), but overestimation of disease prevalence was higher for the initial D+F (P<0.001). The discriminative ability was higher for the updated D+F 2011 (AUC of 0.73 95% confidence interval [CI] 0.70-0.76 versus AUC of 0.70 CI 0.67-0.73 for the initial D+F; P<0.001; odds ratio (or) 1.55 CI 1.29-1.86, net reclassification index 0.11 CI 0.05-0.16, P<0.001 ). Clinical pre-test probability calculation using the initial and updated D+F prediction models relevantly overestimates the actual prevalence of obstructive CAD in patients with stable chest pain clinically referred for ICA and CT suggesting that further refinements to improve clinical decision making are needed.
Sarah Feger is a radiologist at the Department of Radiology at the “Krankenhaus der Barmherzigen Brüder Trier” (academic training hospital of the Johannes- Gutenberg University Mainz). She has performed her medical studies and MD thesis at the Charité University Hospital in Berlin. Since 2011, She have been working in a non-invasive cardiovascular imaging group at the Charité University Hospital Berlin.Her research interest is on coronary CT angiography and myocardial CT perfusion. In particular, she focus on patient acceptance of non-invasive cardiovascular imaging tests, iterative reconstructions in cardiac CT, pretest probability prediction for coronary artery disease, long- term effects of coronary CT angiography and gender- specific aspects of coronary CT. She have given several presentations at international radiological conferences (ECR and RSNA) and published her research findings in international high- ranking journals (e.g. European Radiology).