Automated Computer-Assisted Diagnosis of Obstructive Coronary Artery Disease in Emergency Department Patients Undergoing 256-Slice Coronary Computed Tomography Angiography for Acute Chest Pain

• Published in: The American journal of cardiology 2015-10, Vol.116 (7), p.1017-1021
• Main Authors: Hashoul, Sharbell, MD, Gaspar, Tamar, MD, Halon, David A., MBChB, Lewis, Basil S., MD, Shenkar, Yuval, MD, Jaffe, Ronen, MD, Peled, Nathan, MD, Rubinshtein, Ronen, MD
• Format: Article
• Language: English
• Subjects: Acute Disease; Adolescent; Adult; Aged; Aged, 80 and over; Cardiology; Cardiovascular; Chest Pain - diagnostic imaging; Chest Pain - etiology; Coronary Angiography - methods; Coronary Occlusion - complications; Coronary Occlusion - diagnostic imaging; Emergency Service, Hospital; Feasibility Studies; Female; Follow-Up Studies; Heart attacks; Humans; Male; Medical imaging; Middle Aged; Multidetector Computed Tomography - methods; Pattern Recognition, Automated - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Retrospective Studies; Software; Young Adult
• ISSN: 0002-9149; 1879-1913
• DOI: 10.1016/j.amjcard.2015.07.014

A 256-slice coronary computed tomography angiography (CCTA) is an accurate method for detection and exclusion of obstructive coronary artery disease (OBS-CAD). However, accurate image interpretation requires expertise and may not be available at all hours. The purpose of this study was to evaluate the usefulness of a fully automated computer-assisted diagnosis (COMP-DIAG) tool for exclusion of OBS-CAD in patients in the emergency department (ED) presenting with chest pain. Three hundred sixty-nine patients in ED without known coronary disease underwent 256-slice CCTA as part of the assessment of chest pain of uncertain origin. COMP-DIAG (CorAnalyzer II) automatically reported presence or exclusion of OBS-CAD (>50% stenosis, ≥1 vessel). Performance characteristics of COMP-DIAG for exclusion and detection of OBS-CAD were determined using expert reading as the reference standard. Seventeen (5%) studies were unassessable by COMP-DIAG software, and 352 patients (1,056 vessels) were therefore available for analysis. COMP-DIAG identified 33% of assessable studies as having OBS-CAD, but the prevalence of OBS-CAD on CCTA was only 18% (66 of 352 patients) by standard expert reading. However, COMP-DIAG correctly identified 61 of the 66 patients (93%) with OBS-CAD with 21 vessels (2%) with OBS-CAD misclassified as negative. In conclusion, compared to expert reading, automated computer-assisted diagnosis using the CorAnalyzer showed high sensitivity but only moderate specificity for detection of obstructive coronary disease in patients in ED who underwent 256-slice CCTA. The high negative predictive value of this computer-assisted algorithm may be useful in the ED setting.