Combining computed tomography features of left atrial epicardial and pericoronary adipose tissue with the triglyceride-glucose index to predict the recurrence of atrial fibrillation after radiofrequency catheter ablation: a machine learning study

Xiaole Li; Zishuo Wang; Siyi Wang; Wensu Chen; Chengzong Li; Yinyang Zhang; Aiyun Sun; Lixiang Xie; Chunfeng Hu

doi:10.21037/qims-24-1393

Combining computed tomography features of left atrial epicardial and pericoronary adipose tissue with the triglyceride-glucose index to predict the recurrence of atrial fibrillation after radiofrequency catheter ablation: a machine learning study

Quant Imaging Med Surg. 2024 Dec 5;14(12):9306-9322. doi: 10.21037/qims-24-1393. Epub 2024 Nov 29.

Authors

Xiaole Li¹, Zishuo Wang¹, Siyi Wang¹, Wensu Chen², Chengzong Li², Yinyang Zhang³, Aiyun Sun⁴, Lixiang Xie¹, Chunfeng Hu¹

Affiliations

¹ Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
² Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
³ College of Medical Imaging, Xuzhou Medical University, Xuzhou, China.
⁴ CT Imaging Research Center, GE HealthCare China, Shanghai, China.

Abstract

Background: Radiofrequency catheter ablation (RFCA) represents an important treatment option for atrial fibrillation (AF); however, the recurrence rate following surgery is relatively high. This study aimed to predict the recurrence of AF after RFCA using interpretable machine learning models that combined the triglyceride-glucose (TyG) index and the quantification of left atrial epicardial and pericoronary adipose tissue.

Methods: This retrospective study included 325 patients with AF who underwent their first successful RFCA, among whom 79 had confirmed recurrence. The preoperative clinical data of patients were collected, the TyG index was calculated, and computed tomography (CT) image features were quantitatively measured. Multivariate Cox regression analysis was used to identify the independent risk factors for RFCA recurrence, and adjustments being made for various confounding factors. Post-hoc subgroup analysis was conducted to evaluate the predictive value of the TyG index for recurrence in different patient subgroups. Prediction models based on six machine learning algorithms were constructed. The optimal model's features were evaluated using Shapley additive explanations (SHAP).

Results: After adjustment were made for various confounding factors such as comorbidities of AF, Cox regression showed that the volume of left atrial epicardial adipose tissue (LA-EAT), LA-EAT attenuation, left circumflex coronary artery fat attenuation index (LCX-FAI), and the TyG index were independent risk factors for recurrence after RFCA (P<0.001). The support vector machine (SVM) model based on these combined indicators had the best predictive performance, with an area under the curve of 0.793 [95% confidence interval (CI): 0.782-0.805] in the validation set, while its accuracy and positive predictive value were 0.804 and 0.710, respectively. The predictive efficiency of the TyG index appeared to be independent of type 2 diabetes mellitus (T2DM) status (P_interaction=0.660).

Conclusions: The SVM model that integrated the TyG index and quantitative CT imaging variables demonstrated good predictive ability for post-RFCA recurrence in patients with AF. Furthermore, the TyG index appeared capable of predicting recurrence independently of T2DM status.

Keywords: Atrial fibrillation (AF); fat attenuation index (FAI); pericoronary adipose tissue (PCAT); radiofrequency catheter ablation (RFCA); triglyceride-glucose index (TyG index).