PURPOSE: To perform a meta-analysis to compare the diagnostic performance of 16- versus 64-section computed tomography (CT) for the diagnosis of coronary artery disease (CAD). MATERIALS AND METHODS: The MEDLINE database was searched for relevant original articles. Criteria for inclusion of articles were (a) use of multisection spiral CT as a diagnostic test for obstructive CAD, (b) use of the newer generation of multisection spiral CT (16 or 64 section) scanners, and (c) use of coronary angiography as the reference standard for diagnosing obstructive CAD (>50% diameter stenosis was selected as the cutoff criterion for diagnosis of CAD). After data extraction, the analysis was performed according to a random-effects model. Between-study statistical heterogeneity also was assessed by using Cochran Q chi(2) tests. RESULTS: Of 328 identified relevant articles, 37 fulfilled all inclusion criteria, with data available for a patient-based analysis in 28. The patient-based analysis included pooled data from 16 studies, corresponding to 1292 patients who underwent 16-section spiral CT, and from 12 studies, corresponding to 695 patients who underwent 64-section spiral CT. Respectively, the results for 16-section CT versus 64-section CT were 95% (95% confidence interval [CI]: 93%, 96%) versus 97% (95% CI: 95%, 98%) for sensitivity (P = .03), 69% (95% CI: 66%, 73%) versus 90% (95% CI: 86%, 93%) for specificity (P < .001), 79% (95% CI: 76%, 82%) versus 93% (95% CI: 91%, 96%) for positive predictive value (PPV) (P < .001), 92% (95% CI: 88%, 94%) versus 96% (95% CI: 92%, 98%) for negative predictive value (P < .001), and 72.05 (95% CI: 31.35, 165.56) versus 181.82 (95% CI: 88.70, 372.71) for diagnostic odds ratio (P = .1). CONCLUSION: Sixty-four-section spiral CT has significantly higher specificity and PPV on a per-patient basis compared with 16-section CT for the detection of greater than 50% stenosis of coronary arteries. SUPPLEMENTAL MATERIAL: http://radiology.rsnajnls.org/cgi/content/full/2453061899/DC1. (c) RSNA, 2007.
PURPOSE: To perform a meta-analysis to compare the diagnostic performance of 16- versus 64-section computed tomography (CT) for the diagnosis of coronary artery disease (CAD). MATERIALS AND METHODS: The MEDLINE database was searched for relevant original articles. Criteria for inclusion of articles were (a) use of multisection spiral CT as a diagnostic test for obstructive CAD, (b) use of the newer generation of multisection spiral CT (16 or 64 section) scanners, and (c) use of coronary angiography as the reference standard for diagnosing obstructive CAD (>50% diameter stenosis was selected as the cutoff criterion for diagnosis of CAD). After data extraction, the analysis was performed according to a random-effects model. Between-study statistical heterogeneity also was assessed by using Cochran Q chi(2) tests. RESULTS: Of 328 identified relevant articles, 37 fulfilled all inclusion criteria, with data available for a patient-based analysis in 28. The patient-based analysis included pooled data from 16 studies, corresponding to 1292 patients who underwent 16-section spiral CT, and from 12 studies, corresponding to 695 patients who underwent 64-section spiral CT. Respectively, the results for 16-section CT versus 64-section CT were 95% (95% confidence interval [CI]: 93%, 96%) versus 97% (95% CI: 95%, 98%) for sensitivity (P = .03), 69% (95% CI: 66%, 73%) versus 90% (95% CI: 86%, 93%) for specificity (P < .001), 79% (95% CI: 76%, 82%) versus 93% (95% CI: 91%, 96%) for positive predictive value (PPV) (P < .001), 92% (95% CI: 88%, 94%) versus 96% (95% CI: 92%, 98%) for negative predictive value (P < .001), and 72.05 (95% CI: 31.35, 165.56) versus 181.82 (95% CI: 88.70, 372.71) for diagnostic odds ratio (P = .1). CONCLUSION: Sixty-four-section spiral CT has significantly higher specificity and PPV on a per-patient basis compared with 16-section CT for the detection of greater than 50% stenosis of coronary arteries. SUPPLEMENTAL MATERIAL: http://radiology.rsnajnls.org/cgi/content/full/2453061899/DC1. (c) RSNA, 2007.
Authors: Andrea L Vavere; Gregory G Simon; Richard T George; Carlos E Rochitte; Andrew E Arai; Julie M Miller; Marcello Di Carli; Armin Arbab-Zadeh; Armin A Zadeh; Marc Dewey; Hiroyuki Niinuma; Roger Laham; Frank J Rybicki; Joanne D Schuijf; Narinder Paul; John Hoe; Sachio Kuribyashi; Hajime Sakuma; Cesar Nomura; Tan Swee Yaw; Klaus F Kofoed; Kunihiro Yoshioka; Melvin E Clouse; Jeffrey Brinker; Christopher Cox; Joao A C Lima Journal: J Cardiovasc Comput Tomogr Date: 2011-11-12
Authors: Sang Il Choi; Richard T George; Karl H Schuleri; Eun Ju Chun; Joao A C Lima; Albert C Lardo Journal: Int J Cardiovasc Imaging Date: 2009-03-03 Impact factor: 2.357
Authors: Hye-Jeong Lee; Young Jin Kim; Jin Hur; Ji Won Lee; Yoo Jin Hong; Hee Yeong Kim; Hyuk-Jae Chang; Tae Hoon Kim; Byoung Wook Choi Journal: Int J Cardiovasc Imaging Date: 2014-01-23 Impact factor: 2.357
Authors: Julie M Miller; Marc Dewey; Andrea L Vavere; Carlos E Rochitte; Hiroyuki Niinuma; Armin Arbab-Zadeh; Narinder Paul; John Hoe; Albert de Roos; Kunihiro Yoshioka; Pedro A Lemos; David E Bush; Albert C Lardo; John Texter; Jeffery Brinker; Christopher Cox; Melvin E Clouse; João A C Lima Journal: Eur Radiol Date: 2008-11-08 Impact factor: 5.315