Prediction of clinical outcome by myocardial CT perfusion in patients with low-risk unstable angina pectoris View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-02

AUTHORS

Jesper J. Linde, Mathias Sørgaard, Jørgen T. Kühl, Jens D. Hove, Henning Kelbæk, Walter B. Nielsen, Klaus F. Kofoed

ABSTRACT

The prognostic implications of myocardial computed tomography perfusion (CTP) analyses are unknown. In this sub-study to the CATCH-trial we evaluate the ability of adenosine stress CTP findings to predict mid-term major adverse cardiac events (MACE). In 240 patients with acute-onset chest pain, yet normal electrocardiograms and troponins, a clinically blinded adenosine stress CTP scan was performed in addition to conventional diagnostic evaluation. A reversible perfusion defect (PD) was found in 38 patients (16 %) and during a median follow-up of 19 months (range 12-22 months) 25 patients (10 %) suffered a MACE (cardiac death, non-fatal myocardial infarction and revascularizations). Accuracy for the prediction of MACE expressed as the area under curve (AUC) on receiver-operating characteristic curves was 0.88 (0.83-0.92) for visual assessment of a PD and 0.80 (0.73-0.85) for stress TPR (transmural perfusion ratio). After adjustment for the pretest probability of obstructive coronary artery disease, both detection of a PD and stress TPR were significantly associated with MACE with an adjusted hazard ratio of 39 (95 % confidence interval 11-134), p < 0.0001, for visual interpretation and 0.99 (0.98-0.99) for stress TPR, p < 0.0001. Patients with a PD volume covering >10 % of the LV myocardium had a worse prognosis compared to patients with a PD covering <10 % of the LV myocardium, p = 0.0002. The optimal cut-off value of the myocardial PD extent to predict MACE was 5.3 % of the left ventricle [sensitivity 84 % (64-96), specificity 95 % (91-97)]. Myocardial CT perfusion parameters predict mid-term clinical outcome in patients with recent acute-onset chest pain. More... »

PAGES

261-270

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10554-016-0994-x

DOI

http://dx.doi.org/10.1007/s10554-016-0994-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1037264097

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27718140


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