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Diagnostic accuracy of static CT perfusion for the detection of myocardial ischemia. A systematic review and meta-analysis

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OBJECTIVES: The aim of this study is to provide a meta-analysis of all published studies assessing the diagnostic accuracy of stress CT myocardial perfusion imaging (CTP) in patients suspected of or with known coronary artery disease. This analysis is limited to static stress CTP.

METHODS: Systematic literature review and meta-analysis of studies examining the diagnostic accuracy of static CTP imaging alone or combined with coronary CT angiography (CTA) in comparison to single photon emission computed tomography (SPECT), magnetic resonance perfusion (MRP), and/or invasive coronary angiography with and without fractional flow reserve (FFR).

RESULTS: The search revealed 19 eligible studies including 1188 patients. Pooled results showed that CTP had a good agreement with SPECT and MRP. On a per-patient level, sensitivity, specificity and AUC were 0.85 (95% CI: 0.70-0.93), 0.81 (95% CI: 0.59-0.93), 0.90 (95% CI: 0.87-0.92). On a per-artery level, sensitivity, specificity and AUC were 0.80 (95% CI: 0.67-0.88), 0.81 (95% CI: 0.72-0.88) and 0.87 (95% CI: 0.84-0.90). When invasive coronary angiography was used as reference standard, combined coronary CTA and CTP compared to coronary CTA alone significantly improved the specificity from 0.62 (95% CI: 0.52-0.70) to 0.84 (95% CI: 0.74-0.91) on a per-patient level (p = 0.008) and from 0.72 (95% CI: 0.63-0.79) to 0.90 (95% CI: 0.85-0.93) on a per-artery level (p = 0.0001) without significant decrease in sensitivity (p = 0.59 and p = 0.23, respectively).

CONCLUSION: In selected patients, static CT myocardial perfusion has high diagnostic accuracy to detecting myocardial ischemia. Specificity increases significantly when CT myocardial perfusion is combined with coronary CTA.

OriginalsprogEngelsk
TidsskriftJournal of Cardiovascular Computed Tomography
Vol/bind10
Udgave nummer6
Sider (fra-til)450-457
Antal sider8
ISSN1934-5925
DOI
StatusUdgivet - nov. 2016

ID: 49233443