Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation
Abstract 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 ba...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-06-01
|
| Series: | BMC Cardiovascular Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12872-025-04847-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849725450600841216 |
|---|---|
| author | Wanli Xiong Qiqi Cao Lu Jia Min Chen Tao Liu Qingyan Zhao Yanhong Tang Bo Yang Li Li Shaobo Shi He Huang Congxin Huang China Atrial Fibrillation Center Project Team |
| author_facet | Wanli Xiong Qiqi Cao Lu Jia Min Chen Tao Liu Qingyan Zhao Yanhong Tang Bo Yang Li Li Shaobo Shi He Huang Congxin Huang China Atrial Fibrillation Center Project Team |
| author_sort | Wanli Xiong |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-0275576e50f34c03b2ea2280dd0e3a9a |
| institution | DOAJ |
| issn | 1471-2261 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cardiovascular Disorders |
| spelling | doaj-art-0275576e50f34c03b2ea2280dd0e3a9a2025-08-20T03:10:28ZengBMCBMC Cardiovascular Disorders1471-22612025-06-0125111110.1186/s12872-025-04847-wMachine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillationWanli Xiong0Qiqi Cao1Lu Jia2Min Chen3Tao Liu4Qingyan Zhao5Yanhong Tang6Bo Yang7Li Li8Shaobo Shi9He Huang10Congxin Huang11China Atrial Fibrillation Center Project TeamDepartment of Cardiology, Renmin Hospital of Wuhan University, Hubei ProvinceDepartment of Cardiology, Renmin Hospital of Wuhan University, Hubei ProvinceDepartment of Cardiology, Renmin Hospital of Wuhan University, Hubei ProvinceWuhan Shinall Technology CoDepartment of Cardiology, Renmin Hospital of Wuhan University, Hubei ProvinceDepartment of Cardiology, Renmin Hospital of Wuhan University, Hubei ProvinceDepartment of Cardiology, Renmin Hospital of Wuhan University, Hubei ProvinceDepartment of Cardiology, Renmin Hospital of Wuhan University, Hubei ProvinceSchool of Electronic Information, Wuhan UniversityDepartment of Cardiology, Renmin Hospital of Wuhan University, Hubei ProvinceDepartment of Cardiology, Renmin Hospital of Wuhan University, Hubei ProvinceDepartment of Cardiology, Renmin Hospital of Wuhan University, Hubei ProvinceAbstract 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.https://doi.org/10.1186/s12872-025-04847-wParoxysmal atrial fibrillationLeft atrial thrombusTransesophageal echocardiographyMachine-learning model |
| spellingShingle | Wanli Xiong Qiqi Cao Lu Jia Min Chen Tao Liu Qingyan Zhao Yanhong Tang Bo Yang Li Li Shaobo Shi He Huang Congxin Huang China Atrial Fibrillation Center Project Team Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation BMC Cardiovascular Disorders Paroxysmal atrial fibrillation Left atrial thrombus Transesophageal echocardiography Machine-learning model |
| title | Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation |
| title_full | Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation |
| title_fullStr | Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation |
| title_full_unstemmed | Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation |
| title_short | Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation |
| title_sort | machine learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation |
| topic | Paroxysmal atrial fibrillation Left atrial thrombus Transesophageal echocardiography Machine-learning model |
| url | https://doi.org/10.1186/s12872-025-04847-w |
| work_keys_str_mv | AT wanlixiong machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT qiqicao machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT lujia machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT minchen machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT taoliu machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT qingyanzhao machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT yanhongtang machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT boyang machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT lili machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT shaoboshi machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT hehuang machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT congxinhuang machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation AT chinaatrialfibrillationcenterprojectteam machinelearningmodelforpredictingleftatrialthrombusinpatientswithparoxysmalatrialfibrillation |