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Updated: Jul 15, 2026
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
A multivariable prediction model for left atrial appendage spontaneous echo contrast in patients with atrial
En Zhou1, Jing Zhou2, Yinghua Zou3
1Department of Cardiovascular Surgery, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, No. 280 Mohe Road, Baoshan District, Shanghai, 201900, China.
Objectives:
This study aimed to evaluate the association between LAA metabolic parameters-particularly lactic acid, glucose, and calcium-and spontaneous echo contrast, and to develop and externally validate a multivariable prediction model incorporating these indicators.
Methods:
Consecutive patients with AF undergoing radiofrequency catheter ablation and/or left atrial appendage occlusion were retrospectively enrolled. All patients underwent preprocedural transesophageal echocardiography with direct LAA blood sampling for metabolic analysis. An internal cohort was used for feature selection by LASSO regression and multivariable logistic regression. Model performance was assessed using ROC analysis, calibration, and decision curve analysis, with external validation in an independent cohort.
Results:
A total of 272 patients were included in the internal cohort, among whom 96 (35.3%) had SEC. Patients with SEC showed higher LAA lactic acid levels and lower LAA glucose and calcium levels. Age, persistent AF, LAA blood flow velocity, LAA lactic acid, LAA glucose, and LAA calcium were independently associated with SEC. The resulting nomogram demonstrated excellent discrimination in the internal cohort (AUC 0.895) and maintained robust performance in the external cohort (AUC 0.947). Decision curve analysis indicated a positive net clinical benefit across a wide range of threshold probabilities.
Conclusions:
LAA metabolic characteristics, particularly elevated lactic acid levels, are independently associated with SEC in AF. A prediction model integrating metabolic, clinical, and echocardiographic parameters provides robust and externally validated risk stratification for SEC.
