Feasibility of an automatic computer-assisted algorithm for the detection of significant coronary artery disease in patients presenting with acute chest pain

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• 作者：Ki-Woon Kang^a ; ¹ ; ^{kwkang@eulji.ac.kr} ; Hyuk-Jae Chang^b ; ^{hjchang@yuhs.ac} ; Hackjoon Shim^b ; ² ; ^{hjshim@yuhs.ac} ; Young-Jin Kim^c ; ³ ; ^{kimyj@yuhs.ac} ; Byoung-Wook Choi^c ; ³ ; ^{bchoi@yuhs.ac} ; Woo-In Yang^b ; ² ; ^{yrblues@yuhs.ac} ; Jee-Young Shim^b ; ² ; ^{cysprs@yuhs.ac} ; Jongwon Ha^b ; ² ; ^jwha@yuhs.ac ; Namsik Chung^b ; ² ; ^{namsikc@yuhs.ac}
• 关键词：Automatic computer-assisted detection ; Coronary artery disease ; Acute chest pain
• 刊名：European Journal of Radiology
• 出版年：2012
• 出版时间：April, 2012
• 年：2012
• 卷：81
• 期：4
• 页码：e640-e646
• 全文大小：1061 K

文摘

Automatic computer-assisted detection (auto-CAD) of significant coronary artery disease (CAD) in coronary computed tomography angiography (cCTA) has been shown to have relatively high accuracy. However, to date, scarce data are available regarding the performance of auto-CAD in the setting of acute chest pain. This study sought to demonstrate the feasibility of an auto-CAD algorithm for cCTA in patients presenting with acute chest pain. We retrospectively investigated 398 consecutive patients (229 male, mean age 50 ¡À 21 years) who had acute chest pain and underwent cCTA between Apr 2007 and Jan 2011 in the emergency department (ED). All cCTA data were analyzed using an auto-CAD algorithm for the detection of >50 % CAD on cCTA. The accuracy of auto-CAD was compared with the formal radiology report. In 380 of 398 patients (18 were excluded due to failure of data processing), per-patient analysis of auto-CAD revealed the following: sensitivity 94 % , specificity 63 % , positive predictive value (PPV) 76 % , and negative predictive value (NPV) 89 % . After the exclusion of 37 cases that were interpreted as invalid by the auto-CAD algorithm, the NPV was further increased up to 97 % , considering the false-negative cases in the formal radiology report, and was confirmed by subsequent invasive angiogram during the index visit. We successfully demonstrated the high accuracy of an auto-CAD algorithm, compared with the formal radiology report, for the detection of >50 % CAD on cCTA in the setting of acute chest pain. The auto-CAD algorithm can be used to facilitate the decision-making process in the ED.