Medical record data-enabled machine learning can enhance prediction of left atrial appendage thrombosis in nonvalvular atrial fibrillation

Zhao, Yue; Cao, Liya; Zhao, Yong; Wang, Fei; Huang, Xing; Wang, Qian

doi:10.1016/j.thromres.2023.01.001

Cited by 4 publications

(4 citation statements)

References 46 publications

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“…Previous studies have shown that ML models can accurately predict left atrial appendage thrombosis and outperform conventional stroke risk scores, which may help predict the risk of stroke in NVAF patients. 11 The internal logic of ML models can identify critical risk factors such as left atrial and left atrial appendage structures and certain biomarkers, which are crucial for predicting thrombosis and optimizing treatment strategies for NVAF patients, with significant implications for preventing ischemic stroke. Studies have used data from the Korean AF Registry of Ischemic Stroke Patients and the Korean University Stroke Registry to internally and externally validate ML models.…”

Section: Discussionmentioning

confidence: 99%

“…10 Prediction models of thrombosis risk in atrial fibrillation (AF) are used to guide treatment. 11 Although regression models have traditionally been the preferred analytical approach for prediction modeling, ML has emerged as a potentially more effective methodology. In this study, we aim to develop a predictive model that employs machine learning (ML) techniques in conjunction with detailed echocardiographic parameters and essential clinical factors.…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

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Machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients based on multimodal echocardiographic parameters

Zeng,

Chang,

Zhang

et al. 2024

Preprint

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BACKGROUND: This study sought to develop a robust machine learning (ML)-based predictive model that synthesizes multimodal echocardiographic data and clinical risk factors to assess thrombosis risk in patients with non-valvular atrial fibrillation (NVAF). METHODS AND RESULTS: A total of 402 NVAF patients scheduled for AF radiofrequency ablation and/or left atrial appendage closure at the First Affiliated Hospital of Guangxi Medical University from January 2020 to December 2023 were prospectively collected. Among them, there were 289 males (71.9%) and 113 females (28.1%), with a mean age of 59.7 years. There were 142 patients (35.3%) with left atrial thrombus/spontaneous echocardiographic contrast (LAT/SEC) and 260 patients (64.7%) without LAT/SEC. We constructed seven ML models from the collected clinical data, biochemical markers, and multimodal echocardiographic parameters. Models were evaluated via ROC curves; SHAP values were employed for feature importance analysis.The integration of multimodal echocardiographic parameters with clinical risk factors based on sophisticated ML algorithms significantly improved the precision of thrombus risk prediction in patients with NVAF. Specifically, the XGBoost model (AUC 0.959, 95% CI 0.925?0.993) slightly outperformed the traditional Logistic regression model (AUC 0.949, 95% CI: 0.911-0.987) in predicting thrombus formation risk in NVAF patients, and showed superior predictive ability compared to other ML algorithms. Additionally, XGBoost offered greater clinical net benefit within a threshold probability range of 0.1 to 1.0. SHAP analysis revealed that left atrial structure (left atrial volume index, three-dimensional sphericity index), hemodynamic parameters (left atrial acceleration factor and S/D ratio), and functional parameters (peak atrial longitudinal strain) were important features in predicting the risk of thrombus formation in NVAF patients, with reduced peak atrial longitudinal strain being the most important risk factor for predicting thrombus. Conclusion: Developing a predictive model utilizing ML techniques that incorporate multimodal echocardiographic parameters in conjunction with clinical risk factors has the potential to enhance the predictive accuracy of the thrombosis risk in individuals with NVAF. The XGBoost model shows that decreased PALS, hemodynamic abnormalities and left atrium spherical remodeling are significant factors correlated with increased risk of thrombus in NVAF.

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Section: Discussionmentioning

confidence: 99%

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients based on multimodal echocardiographic parameters

Zeng,

Chang,

Zhang

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…This model optimized the treatment strategy through the best accuracy of 0.865, significantly outperforming the CHADS2 score and CHA2DS2-VASc score. 54 Electrocardiograms, cardiac monitor reports, and clinical notes from electronic health records also provided information for the identification of AF recurrence. Zheng et al developed and validated an automated natural language processing algorithm to capture recurrent AF using electronic health records.…”

Section: Ai In Treatment Of Af: Ai and Electronic Health Recordsmentioning

confidence: 99%

“…adopted medical record data‐enabled ML algorithms to construct classifiers for the prediction of left atrial appendage thrombosis risk. This model optimized the treatment strategy through the best accuracy of 0.865, significantly outperforming the CHADS2 score and CHA2DS2‐VASc score 54 . Electrocardiograms, cardiac monitor reports, and clinical notes from electronic health records also provided information for the identification of AF recurrence.…”

Section: Atrial Fibrillationmentioning

confidence: 99%

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia

Guo,

Tian,

Bazoukis

et al. 2024

Pacing Clinical Electrophis

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The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

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Harnessing Risk Assessment for Thrombosis and Bleeding to Optimize Anticoagulation Strategy in Nonvalvular Atrial Fibrillation

Zhao,

Cao,

Zhao

et al. 2024

Thromb Haemost

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Background: Oral anticoagulation (OAC) following catheter ablation (CA) of nonvalvular atrial fibrillation (NVAF) is essential for the prevention of thrombosis events. Inappropriate application of OACs does not benefit thrombosis prevention but may be associated with a higher risk of bleeding. Therefore, this study aims to develop clinical data-driven machine learning (ML) methods to predict the risk of thrombosis and bleeding to establish more precise anticoagulation strategies for patients with NVAF. Methods: Patients with NVAF underwent CA therapy were enrolled from Southwest Hospital from 2015 to 2023. This study compared eight ML algorithms to evaluate the predictive power for both thrombosis and bleeding. Model interpretations were recognized by feature importance and SHapley Addictive exPlanations methods. With potential essential risk factors, simplified ML models were proposed to improve the feasibility of the tool. Results: A total of 1055 participants were recruited, including 105 patients with thrombosis and 252 patients with bleeding. The models based on XGBoost achieved the best performance with accuracies of 0.704 and 0.781 for thrombosis and bleeding. Age, BNP and the duration of heparin and are closely related to the high risk of thrombosis, whereas anticoagulation strategy, BNP and lipids play a crucial role in the occurrence of bleeding. The optimized models enrolling crucial risk factors, RF-T for thrombosis and Xw-B for bleeding, achieved the best recalls of 0.774 and 0.780, respectively. Conclusions: The optimized models will have a great clinical application in predicting thrombosis and bleeding among NVAF patients and will form the basis for future score scales.

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Medical record data-enabled machine learning can enhance prediction of left atrial appendage thrombosis in nonvalvular atrial fibrillation

Cited by 4 publications

References 46 publications

Machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients based on multimodal echocardiographic parameters

Machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients based on multimodal echocardiographic parameters

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia

Harnessing Risk Assessment for Thrombosis and Bleeding to Optimize Anticoagulation Strategy in Nonvalvular Atrial Fibrillation

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