Key result
Deep learning-based RCA PCAT linked to a ~7-fold increase in MACE after PCI.
Why the study?
The prognostic value of deep learning based RCA PCAT and plaque volume beyond CT-FFR in patients with PCI was unclear.
Does deep learning based RCA PCAT and plaque volume assessment predict MACE better than CT-FFR in patients with prior PCI?
Cohort (n=183)
Does deep learning based RCA PCAT and plaque volume assessment predict MACE better than CT-FFR in patients with prior PCI?
Hazard Ratio: 7.05 (95% CI 1.44–34.63)
p-value: p=0.016
Deep learning-derived RCA PCAT and plaque volume from coronary CTA provide stronger prognostic value for MACE than CT-FFR in patients with prior stent implantation.
RCA PCAT and plaque volume may add prognostic value beyond CT-FFR post-PCI; leaves open whether they improve risk stratification in prospective cohorts.
AIM: The study aims to investigate the prognostic value of deep learning based pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume beyond coronary computed tomography angiography (CTA) -derived fractional flow reserve (CT-FFR) in patients with percutaneous coronary intervention (PCI). METHODS: A total of 183 patients with PCI who underwent coronary CTA were included in this retrospective study. Imaging assessment included PCAT, plaque volume, and CT-FFR, which were performed using an artificial intelligence (AI) assisted workstation. Kaplan-Meier survival curves analysis and multivariate Cox regression were used to estimate major adverse cardiovascular events (MACE), including non-fatal myocardial infraction (MI), stroke, and mortality. RESULTS: In total, 22 (12%) MACE occurred during a median follow-up period of 38.0 months (34.6-54.6 months). Kaplan-Meier analysis revealed that right coronary artery (RCA) PCAT (p = 0.007) and plaque volume (p = 0.008) were significantly associated with the increase in MACE. Multivariable Cox regression indicated that RCA PCAT (hazard ratios (HR): 7.05, 95%CI: 1.44-34.63, p = 0.016) and plaque volume (HR: 3.84, 95%CI: 1.44-10.27, p = 0.007) were independent predictors of MACE after adjustment by clinical risk factors. However, CT-FFR was not independently associated with MACE in multivariable Cox regression (p = 0.150). CONCLUSIONS: Deep learning based RCA PCAT and plaque volume derived from coronary CTA were found to be more strongly associated with MACE than CTFFR in patients with PCI.
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Huang et al. (2025) conducted a cohort in Patients with percutaneous coronary intervention (PCI) (n=183). Deep learning based RCA PCAT and plaque volume vs. CT-FFR was evaluated on Major adverse cardiovascular events (MACE), including non-fatal myocardial infraction (MI), stroke, and mortality (HR 7.05, 95% CI 1.44-34.63, p=0.016). Deep learning-based right coronary artery PCAT (HR 7.05; 95% CI 1.44-34.63; p=0.016) and plaque volume were independent predictors of MACE in patients with prior PCI, outperforming CT-FFR.
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