Left Atrial Appendage Thrombus in Patients with Nonvalvular Atrial Fibrillation before Catheter Ablation and Cardioversion: Risk Factors beyond the CHA2DS2-VASc Score.
Yang-Wei CaiQingsong XiongShaojie ChenXi JiangJia LiaoWeijie ChenLili ZouLei SuYefeng ZhuYuehui YinZhiyu LingPublished in: Journal of cardiovascular development and disease (2022)
Left atrial appendage thrombus (LAAT) is a surrogate of thromboembolic events in patients with nonvalvular atrial fibrillation (NVAF). We aimed to investigate the risk factors for LAAT formation before catheter ablation and cardioversion beside the CHA2DS2-VASc score. In this case-control study, patients with NVAF who underwent transesophageal echocardiography (TEE) were included. Demographic data, laboratory results, and echocardiographic measurements were retrospectively collected. Logistic regression analysis was performed to determine risk factors predicting LAAT. Of the 543 included patients, LAAT was identified in 50 patients (9.2%). Multivariable logistic regression analysis for the entire cohort showed that NT-proBNP (per 500 ng/L increase, OR (95% CI): 1.09 (1.00-1.19), p = 0.038) and LDL-C (per 1 mmol/L increase, OR (95% CI): 1.70 (1.05-2.77), p = 0.032) were independently correlated with the presence of LAAT after the adjustment for CHA2DS2-VASc score and anticoagulant therapy. The subgroup analysis of patients without anticoagulant therapy also yielded similar results. Regarding patients with CHA2DS2-VASc scores ≤ 1, a higher level of LDL-C (per 1 mmol/L increase, OR (95% CI): 6.31 (2.38-16.74), p < 0.001) independently correlated with the presence of LAAT. The present study suggests that beyond CHA2DS2-VASc score, raised NT-proBNP and LDL-C are additional predictors for LAAT in NVAF patients.
Keyphrases
- atrial fibrillation
- left atrial appendage
- catheter ablation
- left atrial
- oral anticoagulants
- direct oral anticoagulants
- ejection fraction
- risk factors
- heart failure
- newly diagnosed
- percutaneous coronary intervention
- pulmonary hypertension
- stem cells
- computed tomography
- bone marrow
- machine learning
- venous thromboembolism