Validation of machine learning angiography-derived physiological pattern of coronary artery disease.

ABSTRACT:

Aims

The classification of physiological patterns of coronary artery disease (CAD) is crucial for clinical decision-making, significantly affecting the planning and success of percutaneous coronary interventions (PCIs). This study aimed to develop a novel index to reliably interpret and classify physiological CAD patterns based on virtual pullbacks from single-view Murray's law-based quantitative flow ratio (μFR) analysis.

Methods and results

The pullback pressure gradient index (PPGi) was used to classify CAD patterns, with a cut-off value of PPGi = 0.78 distinguishing focal from diffuse and non-focal disease. The machine learning methods using penalized logistic regression and random forest were proposed to assess CAD patterns. Scores derived from multivariate functional p

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC12282386 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature