High-Risk Morphological and Physiological Coronary Disease Attributes as Outcome Markers After Medical Treatment and

Seokhun Yang1, Bon-Kwon Koo2, Doyeon Hwang1

  • 1Department of Internal Medicine and Cardiovascular Center, Seoul National University Hospital, Seoul, Korea.

Insights

High-risk plaque morphology in coronary artery disease offers prognostic value beyond fractional flow reserve (FFR). Incorporating plaque characteristics can refine risk prediction and guide treatment decisions for better patient outcomes.

Area of Science:

  • Cardiovascular Imaging and Intervention
  • Interventional Cardiology
  • Coronary Artery Disease

Background:

  • Fractional flow reserve (FFR) is the standard for assessing coronary artery disease severity.
  • Coronary computed tomography angiography (CCTA) provides detailed plaque morphology information.
  • The combined prognostic impact of FFR and CCTA-derived plaque characteristics is not fully understood.

Purpose of the Study:

  • To evaluate the prognostic significance of coronary plaque morphology and physiology.
  • To assess outcomes after medical treatment versus percutaneous coronary intervention (PCI).
  • To determine if plaque morphology adds value to FFR in risk stratification.

Main Methods:

  • Invasive FFR and CCTA were performed on 872 vessels in 538 patients.
  • High-risk attributes (HRA) included physiological (FFR ≤0.8) and morphological factors (plaque burden, adverse characteristics, global plaque burden).
  • Primary outcome was a composite of revascularization, myocardial infarction, or cardiac death at 5 years.

Main Results:

  • High-risk morphological attributes were associated with increased adverse outcomes, independent of FFR.
  • Event risk increased proportionally with the number of HRA.
  • PCI reduced risk in vessels with 1-2 HRA compared to medical treatment, but not in vessels with 0 or ≥3 HRA.

Conclusions:

  • Coronary plaque morphology provides additive prognostic information to FFR.
  • Integrating morphological data may optimize treatment selection for coronary artery disease.
  • This approach can enhance FFR-based risk prediction models.
Abstract

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