## 3 Current Risk Stratification Methods  
Risk stratification for patients with AF involves assessing the likelihood of complications such as stroke or bleeding. Anticoagulant medications, like warfarin, can prevent or treat approximately two-thirds of strokes and thrombotic events related to AF. However, these medications also increase the risk of bleeding, which can have a high mortality rate and lifelong implications [22; 23]. Thus, identifying individuals at low risk is crucial  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stroke Risk Stratification** & & **HAS-BLED** & **ATRIA** &  
to avoid unnecessary anticoagulant therapy. Healthcare providers must balance bleeding and stroke risks by selecting the most appropriate anticoagulants and dosages, and continually adjusting treatment as patient risk factors evolve. The CHA\({}_{2}\)DS\({}_{2}\)-VASc [42] score is the most widely used tool for assessing stroke risk, followed by the CHADS\({}_{2}\) score [43], ABC score [44], and Framingham score [45]. For evaluating bleeding risk, the HAS-BLED score [46] is most commonly used, along with the ATRIA score [47], ABC bleeding score [48], and ORBIT score [49]. Four stroke risk stratifications are listed in Table 1 and the four bleeding risk stratifications are listed in Table 2. The c-statistic was the most commonly used evaluation index in these meta-analyses, though some studies also used risk ratios or odds ratios. For consistency, the c-statistic was used as the evaluation metric for the different risk stratification methods in this section. Tables 1 and 2 summarize each risk stratification's risk factors, c-statistics from the meta-analyses, and corresponding references. Among them, the c-statistic value is the maximum, minimum, and average of all c-critic values that appear in Table 1. A c-statistic closer to 1 indicates better predictive performance. From Tables 1 and 2, it is clear that current risk stratification tools for stroke and bleeding take into account factors such as patient characteristics, comorbidities, and biomarkers. Among the various stroke scoring methods, the CHA\({}_{2}\)DS\({}_{2}\)-VASc score encompasses a wider range of complications and medical history. Notably, the ABC score is the only tool that incorporates biomarkers. In contrast to stroke risk scores, bleeding risk scores also consider renal and liver function, as well as anemia. The c-statistics of various risk stratification tools show minimal variation, indicating poor to moderate predictive performance (c-statistic \(<\) 0.7). While the CHADS\({}_{2}\) score seems to offer the best evaluative performance among them, several studies suggest that its ability to identify low-risk populations is less effective compared to the CHA\({}_{2}\)DS\({}_{2}\)-VASc score [50; 51; 52]. The CHA\({}_{2}\)DS\({}_{2}\)-VASc score remains the most commonly used for stroke risk stratification due to its simplicity. However, many researchers describe its predictive power as \"modest\" [28] and \"inferior\" [25]. On the one hand, numerous meta-analyses highlight the high heterogeneity of the CHA\({}_{2}\)DS\({}_{2}\)-VASc score across different studies [24; 26; 53]. On the other hand, a significant percentage of patients with low CHA\({}_{2}\)DS\({}_{2}\)-VASc scores are found to have LAA thrombus (5.2% in [52]). Some cohort studies and multivariable analyses even suggest no positive correlation between the CHA\({}_{2}\)DS\({}_{2}\)-VASc score and LAA thrombus presence, or no significant score differences between thrombus and control groups [54]. Additionally, the risk factors like sex, hypertension, heart failure, prior stroke, and diabetes as predictors of LAA thrombus have been questioned [55]. The relatively low accuracy of the CHA\({}_{2}\)DS\({}_{2}\)-VASc score could be attributed to its exclusive reliance on demographic and clinical data, lacking insights from modern imaging technology and hemodynamic analysis.