Clustering of artificial intelligence derived plaque features for risk stratification for future atrial fibrillation development
Authors: Epstein E , Luceri R , Burton T , Hermel M , Hu E , Bagsic S , Calicchio F , Chiou A , Van Rosendael A , Newlander S , Robinson A , Bhavnani S , Stojanovska J , Gonzalez J , Wesbey G 
Published: October 2024

Abstract

Introduction
We previously demonstrated that artificial intelligence (AI)-derived coronary artery plaque features are associated with future development of atrial fibrillation (AF) among patients undergoing CCTA(1). Our results suggest that phenotyping a large dataset of plaque features using data science techniques may reveal associations not visible from univariate analysis.

Purpose
Use data science analysis of AI-CT plaque characteristics to identify subpopulations at higher risk of AF.

Methods
We applied dimensionality reduction and visual cluster identification to phenotype our population of patients based on their AI-CT plaque features. We used a single center CCTA registry of patients with prior clinically-indicated CCTA (N=2700) from 2017-2020. Cases of new AF were defined by onset of AF on ECG or telemetry after CCTA via EMR search. Patients with AF prior to CCTA, history of mitral valve surgery, or postoperative AF were excluded. Controls were selected 1:1 by nearest neighbor propensity matching (R. v. 4.0.3.) based on age, sex, tobacco use, HTN, HLD, and diabetes. Atherosclerotic plaques were quantitatively analyzed using a commercially available FDA-approved AI coronary analysis software yielding stenosis area and diameter, remodeling index, volume of calcified/non-calcified/low-density plaques, and number of 2 or more high risk plaques. Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction with cosine distance metric was used to reduce the plaque feature space from 80 dimensions (8 AI measurements per each of 10 anatomical vessel segments in all patients) to 2 dimensions on a scatter plot. Two non-contiguous clusters (i.e. plaque phenotypes) were identified based on visual analysis of UMAP feature reduction (Figure 1).

Results
94 cases comprised the study cohort (47 cases and 47 controls). Mean age was 67±11 years. 38% were female. Mean CADRADS score was 1.8±1.52 and mean CAC was 325±622. Mean time from CT to AF was 281±300 days. The majority of AF patients (67%) fell into Cluster 1 (N=36), while Cluster 2 (N=58) contained most of the control subjects (75%). Treating Cluster 1 as a predictor of AF, the PPV and specificity of the UMAP reduction was 67% and 75% respectively, with NPV of 60% and sensitivity of 51% (Table 1). Left atrial volume index (LAVI) was significantly different between the 2 clusters, with mean 56.15±12.42 ml/m2 in Cluster 1 and 46.96±16.03 in Cluster 2 (p=0.0046). LAVI was also significantly different between control subjects in Cluster 1 vs 2, mean 54.84±11.6 vs 41.62±7.92 (p&lt;0.001).

Conclusions
We demonstrated that UMAP-derived clustering of AI-CT plaque characteristics identifies novel phenotypes for future risk of AF among patients undergoing CCTA. Further, control patients in Cluster 1 were found to have a significantly higher LAVI compared to control subjects in Cluster 2, which supports that membership in Cluster 1 may increase risk of future development of AF.