title: Computed tomography segmental calcium score (SCS) to predict stenosis severity of calcified coronary lesions.
abstract: To estimate the probability of ≥ 50% coronary stenoses based on computed tomography (CT) segmental calcium score (SCS) and clinical factors. The Institutional Review Board approved the study. A training sample of 201 patients underwent CT calcium scoring and conventional coronary angiography (CCA). All patients consented to undergo CT before CCA after being informed of the additional radiation dose. SCS and calcification morphology were assessed in individual coronary segments. We explored the predictive value of patient's symptoms, clinical history, SCS and calcification morphology. We developed a prediction model in the training sample based on these variables then tested it in an independent test sample. The odds ratio (OR) for ≥ 50% coronary stenosis was 1.8-fold greater (p = 0.006) in patients with typical chest pain, twofold (p = 0.014) greater in patients with acute coronary syndromes, twofold greater (p < 0.001) in patients with prior myocardial infarction. Spotty calcifications had an OR for ≥ 50% stenosis 2.3-fold (p < 0.001) greater than the absence of calcifications, wide calcifications 2.7-fold (p < 0.001) greater, diffuse calcifications 4.6-fold (p < 0.001) greater. In middle segments, each unit of SCS had an OR 1.2-fold (p < 0.001) greater than in distal segments; in proximal segments the OR was 1.1-fold greater (p = 0.021). The ROC curve area of the prediction model was 0.795 (0.95 confidence interval 0.602-0.843). Validation in a test sample of 201 independent patients showed consistent diagnostic performance. In conjunction with calcification morphology, anatomical location, patient's symptoms and clinical history, SCS can be helpful to estimate the probability of ≥ 50% coronary stenosis.
authors.affiliation: Centre for Advanced Cardiovascular Imaging, NIHR Cardiovascular Biomedical Research Unit at Barts, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK. f.pugliese@qmul.ac.uk.
authors.forename: Francesca
authors.initials: F
authors.lastname: Pugliese
authors.affiliation: Department of Radiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.|Harvard School of Public Health, Harvard University, Boston, MA, USA.
authors.forename: M G M
authors.initials: MG
authors.lastname: Hunink
authors.affiliation: Department of Cardiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.
authors.forename: Willem B
authors.initials: WB
authors.lastname: Meijboom
authors.affiliation: Medical University of Silesia, Katowice, Poland.
authors.forename: Katarzyna
authors.initials: K
authors.lastname: Gruszczynsnka
authors.affiliation: University of Rome La Sapienza, Polo Pontino, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Rengo
authors.affiliation: Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
authors.forename: Lu
authors.initials: L
authors.lastname: Zou
authors.affiliation: Medical University of Silesia, Katowice, Poland.
authors.forename: Ian
authors.initials: I
authors.lastname: Baron
authors.affiliation: Department of Radiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.
authors.forename: Marcel L
authors.initials: ML
authors.lastname: Dijkshoorn
authors.affiliation: Department of Radiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.
authors.forename: Gabriel P
authors.initials: GP
authors.lastname: Krestin
authors.affiliation: Department of Radiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.|Department of Cardiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.
authors.forename: Pim J
authors.initials: PJ
authors.lastname: de Feyter
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D016009:Chi-Square Distribution; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D023921:Coronary Stenosis / Q000000981:diagnostic imaging*; D003331:Coronary Vessels / Q000000981:diagnostic imaging*; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D061330:Multidetector Computed Tomography; D015999:Multivariate Analysis; D016017:Odds Ratio; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D061205:Vascular Calcification / Q000000981:diagnostic imaging*; D055815:Young Adult
keywords: Atherosclerosis; Computed tomography; Coronary arteries; Coronary calcification; Coronary computed tomography angiography