ArticleBMC medical imaging2025
An exploratory analysis of the utility of maximum degree of stenosis on computed tomography coronary angiography for predicting major adverse cardiac events.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionThe strength of CT coronary angiography (CTCA) is ruling out significant coronary artery disease (CAD) in symptomatic intermediate risk patients. CTCA is gaining attention as a tool for stratifying patients' risk of major adverse cardiac events (MACE). This study evaluated the ability of stenosis reporting on CTCA to predict MACE in patients undergoing investigation at Townsville University Hospital. METHODS AND
resultsOne-thousand and three patients (1003) who underwent a CTCA between January 2015 and November 2023 were followed up until February 2024. For each patient, maximum degree of stenosis on CTCA, coronary artery calcium score (CACS) and cardiac risk factors were collected. Four-hundred and seventy-one (471) patients had no stenosis on CTCA, 181 had 1-49% stenosis, 237 had 50-69% stenosis and 114 had [Formula: see text]70% stenosis. One hundred and sixteen (116) patients had invasive coronary angiography (ICA) performed of which 29 had a subsequent percutaneous coronary intervention (PCI) and 9 had a coronary artery bypass graft (CABG). In patients with 70% or more stenosis on CTCA, the hazard ratio for suffering a three-point definition of MACE (all-cause mortality, myocardial infarction and stroke or TIA) was 3.74 compared to the 0% stenosis group. ROC curve analysis revealed similar performance of CTCA between subsets of the population. There was no statistically significant difference in the ability of CTCA to predict MACE between women and men, and between Aboriginal and/or Torres Strait Islander patients and other Australians.
conclusionsMaximum degree of stenosis on CTCA can predict MACE. The apparent predictive value of CTCA for MACE largely depends on the features extracted from CTCA and the definition of MACE used.
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