Comprehensive risk prediction of acute coronary syndrome by integrating coronary plaque morphology and hemodynamic characteristics

Background: Acute coronary syndrome (ACS) represents a severe manifestation of coronary artery disease (CAD). Although coronary computed tomography angiography (CCTA) enables the effective assessment of the anatomical characteristics of plaque and high-risk morphological features, its predictive accuracy for ACS remains limited. This study aimed to integrate computational fluid dynamics (CFD) techniques with CCTA image data to investigate the combined value of CCTA-derived plaque characteristics and CFD-derived local wall shear stress (WSS) for predicting ACS.

Methods: This retrospective study included 85 patients who underwent CCTA between January 1, 2022, and January 31, 2024, and subsequently developed ACS, including acute myocardial infarction or unstable angina with plaque rupture. A total of 216 lesions were analyzed and classified as culprit or non-culprit lesions based on treatment status. CCTA-derived morphological features and CFD-calculated WSS parameters were compared between the 83 culprit and 133 non-culprit lesions. Multivariate logistic regression was used to identify independent predictors of ACS. Different predictive models were constructed, and their discriminative and reclassification performances were evaluated using the Harrell’s concordance index (C-index), net reclassification index (NRI), and integrated discrimination improvement (IDI).

Results: Morphologically, culprit lesions exhibited more severe stenosis (70.6%±8.5% vs. 66.4%±7.8%, P<0.001), longer lesion length (15.8±8.4 vs. 12.1±7.4 mm, P=0.002), and a higher prevalence of high-risk plaque (HRP) (51.4% vs. 48.6%, P=0.02). Hemodynamically, the culprit lesions exhibited significantly higher total WSS {17.3 [interquartile range (IQR), 11.7–24.1] vs. 14.9 (IQR, 9.6–20.2) Pa, P=0.045} and proximal WSS (WSS_prox) [10.3 (4.4–17.1) vs. 6.4 (3.4–11.4) Pa, P<0.001]. Stenosis, lesion length, HRP, and hemodynamic parameters were associated with the subsequent occurrence of ACS. The addition of hemodynamic parameters improved the ability of the model to predict and reclassify ACS. Among the individual indicators added to the model, WSS_prox showed the highest C-index (0.767 vs. 0.733; P=0.048), as well as the greatest incremental and net reclassification improvement (NRI: 0.369, P<0.007; relative IDI: 0.048, P<0.001).

Conclusions: CFD-derived non-invasive hemodynamic assessment, particularly WSS, significantly enhanced the ability of the model to predict ACS. The integration of non-invasive hemodynamic parameters may improve the identification of culprit lesions associated with future ACS, with relevant diagnostic and therapeutic implications.