Observational studyThe international journal of cardiovascular imaging2025
Predictive value of plaque features quantified by coronary CT angiography for periprocedural myocardial infarction in Non-ST-Segment elevation acute coronary syndrome.
Observational study in The international journal of cardiovascular 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
Periprocedural myocardial infarction (PMI) is associated with adverse outcomes, but determinants of its occurrence in patients with non-ST-segment elevation acute coronary syndrome (NSTE-ACS) remain unclear. This study aimed to investigate the predictive value of plaque characteristics quantified by coronary CT angiography (CCTA) for PMI in this population. In this secondary analysis of a prospective multicenter NSTE-ACS cohort, participants who underwent CCTA before percutaneous coronary intervention were analyzed. Plaque components were defined by CT thresholds: lipid core (< 30 HU), fibrous (30-350 HU), and calcified (≥ 350 HU). Plaque characteristics were comprehensively quantified on CCTA. PMI was adjudicated according to the Fourth Universal Definition of Myocardial Infarction. A total of 201 participants (mean age 56.8 years ± 11.3 [SD]; 170 male) were included for the analysis. PMI occurred in 30 (14.9%) participants. Lipid core burden (adjusted OR: 1.05; 95% CI: 1.01-1.09; P = 0.03) and plaque length (adjusted OR: 1.04; 95% CI: 1.01-1.07; P = 0.02) were independent predictors of PMI, with optimal cutoffs of 30.6% and 45.0 mm. The addition of dichotomized lipid core burden and plaque length to a baseline model significantly improved predictive performance, as indicated by an integrated discrimination improvement (IDI) of 0.11 (P < 0.001) and a net reclassification improvement (NRI) of 0.60 (P = 0.002). In patients with NSTE-ACS, CCTA-quantified lipid core burden and plaque length were independently associated with the risk of PMI. Pre-procedural CCTA plaque assessment improves risk stratification for PMI and may aid in planning individualized PCI strategies.
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