Assessment of cardiac allograft vasculopathy in heart transplant patients using multidimensional dynamic CTA and principal components analysis.

Abstract

Background: Cardiac allograft vasculopathy (CAV) is a major cause of late graft failure post heart transplantation. While coronary angiography remains the gold standard, non-invasive techniques, such as CT angiography (CTA), are emerging alternatives. Electrocardiogram-gated multidimensional dynamic CTA (MD CTA) allows to track dynamic motions of coronary artery throughout the cardiac cycles, potentially revealing valuable insights into coronary abnormalities.

Methods: Principal component analysis (PCA) is employed to analyze the left anterior descending artery (LAD) motion, aiming to assess CAV in heart transplant patients. The motions were determined through registration of MD CTA images, and the incremental displacement of LAD between adjacent phases in a complete cardiac cycle was used as input in PCA. Two-sample t-test and logistic regression were used to compare and differentiate the control and CAV group based on PCA results, and a linear regression was used to correlate PCA results with the degree of stenosis.

Results: The resulted contribution rate of the first principal component (PC1) in control group (0.61 ± 0.05) is significantly higher than the value observed in CAV group (0.46 ± 0.06, p < 0.05). A univariate logistic model (AUC = 0.97) based on contribution rate can sharply discriminate the control and CAV group. Importantly, a negative correlation was found between the contribution rate of PC1 and the degree of stenosis in CAV group.

Conclusion: This study employs PCA and multidimensional CTA to analyze LAD dynamic motion for assessment of CAV. The contribution rate of the first principal component (PC1) was identified as a promising indicator for evaluating CAV and tracking stenosis progression. These findings offer a quantitative, non-invasive approach that may enhance clinical decision-making in post heart transplantation care.