Clinical radiology · 2024
Impact of risk factors on atrial fibrillation types via epicardial adipose tissue computed tomography-based radiomics analysis.
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Hu Y, Zhou G, Jiang S, Gao L, Yu Y, Wang Q, Chen J, Zhang J
Аннотация
AIM: To analyze epicardial adipose tissue (EAT) radiomics and assess the impact of risk factors on different types of atrial fibrillation (AF).
MATERIALS AND METHODS: We included AF patients undergoing radiofrequency ablation from June 2018 to December 2020. Propensity score matching (PSM) was employed to limit the differences in risk factors between patients with different types of AF. The nnU-Net model was utilized to segment EAT. After extracting radiomic features, a generalized linear model was used to examine the epicardial adipose tissue (EAT) radiomic features associated with risk factors, and logistic regression analysis was conducted to identify the features differentiating the two AF groups. Hierarchical clustering was utilized to identify clusters among significant features.
RESULTS: After PSM, 794 patients (median age 67, 547 males) were selected, with 397 having paroxysmal and 397 persistent AF. In univariate analysis, 15.11% of the features were significantly associated with the type of AF (P<.000005). 39.29% of all features were significantly correlated with at least one risk factor, with 141 features being significantly related to both risk factors and the type of AF. Three independent clusters were successfully identified among the 141 features. The biggest cluster is dominated by hypertension and hyperlipidemia factors, exhibiting a high overlap in their effects on EAT. Following closely in influence are gender, age, and diabetes clusters, collectively covering 59 out of the 141 features.
CONCLUSION: Our study identifies distinct differences in EAT characteristics between paroxysmal and persistent AF, with persistent cases showing higher EAT gray values and a more disordered texture.
MATERIALS AND METHODS: We included AF patients undergoing radiofrequency ablation from June 2018 to December 2020. Propensity score matching (PSM) was employed to limit the differences in risk factors between patients with different types of AF. The nnU-Net model was utilized to segment EAT. After extracting radiomic features, a generalized linear model was used to examine the epicardial adipose tissue (EAT) radiomic features associated with risk factors, and logistic regression analysis was conducted to identify the features differentiating the two AF groups. Hierarchical clustering was utilized to identify clusters among significant features.
RESULTS: After PSM, 794 patients (median age 67, 547 males) were selected, with 397 having paroxysmal and 397 persistent AF. In univariate analysis, 15.11% of the features were significantly associated with the type of AF (P<.000005). 39.29% of all features were significantly correlated with at least one risk factor, with 141 features being significantly related to both risk factors and the type of AF. Three independent clusters were successfully identified among the 141 features. The biggest cluster is dominated by hypertension and hyperlipidemia factors, exhibiting a high overlap in their effects on EAT. Following closely in influence are gender, age, and diabetes clusters, collectively covering 59 out of the 141 features.
CONCLUSION: Our study identifies distinct differences in EAT characteristics between paroxysmal and persistent AF, with persistent cases showing higher EAT gray values and a more disordered texture.