Scholarlink

Objective <p>Left atrial thrombus (LAT) poses a significant risk for stroke and other thromboembolic complication in patients with atrial fibrillation (AF). This study aimed to evaluate the incidence and predictors of LAT in patients with paroxysmal AF, utilizing machine learning techniques based on data from the Chinese Atrial Fibrillation study.</p> Methods <p>A large-scale multi-center retrospective study was conducted involving patients diagnosed with non-valvular paroxysmal AF. LAT incidence was assessed, and potential risk factors were analyzed. Machine learning algorithms, including decision tree, random forest, AdaBoost, k-Nearest Neighbor, and logistic regression, were employed to develop a predictive model for LAT.</p> Results <p>Of the 49,515 patients with paroxysmal AF, 1,058 patients (2.1%, 95% CI 2.0%-2.3%) were identified with LAT. Sixty-one variables were initially included to train machine learning models, with the random forest algorithm demonstrating the best predictive performance (AUC 0.833, 95%CI 0.730–0.924). The final model, refined to include nine essential features, achieved an AUC of 0.787 (95%CI 0.670–0.883). Calibration analysis indicated no significant difference between predicted and observed values (<i>p</i> = 0.181). The median predicted probabilities of LAT across quintiles were 2.3%, 7.0%, 11.8%, 16.6%, and 21.5%.</p> Conclusion <p>This simplified prediction model effectively identifies the risk of LAT in patients with paroxysmal AF, providing a valuable tool for clinical decision-making. Further studies are needed to explore AF management and risk stratification in other AF subtypes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月2日

查看全文
  • Wanli Xiong,
  • Qiqi Cao,
  • Lu Jia,
  • Min Chen,
  • Tao Liu,
  • Qingyan Zhao,
  • Yanhong Tang,
  • Bo Yang,
  • Li Li,
  • Shaobo Shi,
  • He Huang,
  • Congxin Huang

摘要

Objective

Left atrial thrombus (LAT) poses a significant risk for stroke and other thromboembolic complication in patients with atrial fibrillation (AF). This study aimed to evaluate the incidence and predictors of LAT in patients with paroxysmal AF, utilizing machine learning techniques based on data from the Chinese Atrial Fibrillation study.

Methods

A large-scale multi-center retrospective study was conducted involving patients diagnosed with non-valvular paroxysmal AF. LAT incidence was assessed, and potential risk factors were analyzed. Machine learning algorithms, including decision tree, random forest, AdaBoost, k-Nearest Neighbor, and logistic regression, were employed to develop a predictive model for LAT.

Results

Of the 49,515 patients with paroxysmal AF, 1,058 patients (2.1%, 95% CI 2.0%-2.3%) were identified with LAT. Sixty-one variables were initially included to train machine learning models, with the random forest algorithm demonstrating the best predictive performance (AUC 0.833, 95%CI 0.730–0.924). The final model, refined to include nine essential features, achieved an AUC of 0.787 (95%CI 0.670–0.883). Calibration analysis indicated no significant difference between predicted and observed values (p = 0.181). The median predicted probabilities of LAT across quintiles were 2.3%, 7.0%, 11.8%, 16.6%, and 21.5%.

Conclusion

This simplified prediction model effectively identifies the risk of LAT in patients with paroxysmal AF, providing a valuable tool for clinical decision-making. Further studies are needed to explore AF management and risk stratification in other AF subtypes.