Prediction and characteristic analysis of cardiac thrombosis in patients with atrial fibrillation undergoing valve disease surgery based on machine learning_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHANG Yiwen ¹ , WANG Zhengjie ² , LEI Nuoyangfan ¹ , TONG Qi ² , LI Tao ² , PAN Fan ³ ,  QIAN Yongjun ² ,  ZHAO Qijun ¹

1. School of Computer Science (School of Software), Sichuan University, Chengdu, 610065, P. R. China;
2. Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
3. School of Electronic Information, Sichuan University, Chengdu, 610065, P. R. China;

Corresponding author：

QIAN Yongjun, Email: qianyongjun@scu.edu.cn; ZHAO Qijun, Email: qjzhao@scu.edu.cn

Keywords：

Atrial fibrillation; thromboembolism; valvular heart disease; machine learning; SHAP value; artificial intelligence

DOI：

10.7507/1007-4848.202204047

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To evaluate the use of machine learning algorithms for the prediction and characterization of cardiac thrombosis in patients with valvular heart disease and atrial fibrillation. Methods This article collected data of patients with valvular disease and atrial fibrillation from West China Hospital of Sichuan University and its branches from 2016 to 2021. From a total of 2 515 patients who underwent valve surgery, 886 patients with valvular disease and atrial fibrillation were included in the study, including 545 (61.5%) males and 341 (38.5%) females, with a mean age of 55.62±9.26 years, and 192 patients had intraoperatively confirmed cardiac thrombosis. We used five supervised machine learning algorithms to predict thrombosis in patients. Based on the clinical data of the patients (33 features after feature screening), the 10-fold nested cross-validation method was used to evaluate the predictive effect of the model through evaluation indicators such as area under the curve, F1 score and Matthews correlation coefficient. Finally, the SHAP interpretation method was used to interpret the model, and the characteristics of the model were analyzed using a patient as an example. Results The final experiment showed that the random forest classifier had the best comprehensive evaluation indicators, the area under the receiver operating characteristic curve was 0.748±0.043, and the accuracy rate reached 79.2%. Interpretation and analysis of the model showed that factors such as stroke volume, peak mitral E-wave velocity and tricuspid pressure gradient were important factors influencing the prediction. Conclusion The random forest model achieves the best predictive performance and is expected to be used by clinicians as an aided decision-making tool for screening high-embolic risk patients with valvular atrial fibrillation.

Citation： ZHANG Yiwen, WANG Zhengjie, LEI Nuoyangfan, TONG Qi, LI Tao, PAN Fan, QIAN Yongjun, ZHAO Qijun. Prediction and characteristic analysis of cardiac thrombosis in patients with atrial fibrillation undergoing valve disease surgery based on machine learning. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(9): 1105-1112. doi: 10.7507/1007-4848.202204047 Copy

1.	Calabrò P, Gragnano F, Cesaro A, et al. Non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation and atrial thrombosis: An appraisal of current evidence. Arch Cardiovasc Dis, 2020, 113(10): 642-651.
2.	Aimo A, Kollia E, Ntritsos G, et al. Echocardiography versus computed tomography and cardiac magnetic resonance for the detection of left heart thrombosis: A systematic review and meta-analysis. Clin Res Cardiol, 2021, 110(11): 1697-1703.
3.	吴越峰, 王琪, 吴明. 机器学习技术在食管癌研究领域中应用的现状与展望. 中国胸心血管外科临床杂志, 2022, 29(6): 770-776.
4.	Tseng AS, Noseworthy PA. Prediction of atrial fibrillation using machine learning: A review. Front Physiol, 2021, 12: 752317.
5.	Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell, 2019, 1(5): 206-215.
6.	Arrieta AB, Díaz-Rodríguez N, Del Ser J, et al. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inform Fus, 2020, 58: 82-115.
7.	Lundberg SM, Erion G, Chen H, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell, 2020, 2(1): 56-67.
8.	Sabovčik F, Cauwenberghs N, Kouznetsov D, et al. Applying machine learning to detect early stages of cardiac remodelling and dysfunction. Eur Heart J Cardiovasc Imaging, 2021, 22(10): 1208-1217.
9.	Writing Committee Members, Otto CM, Nishimura RA, et al. 2020 ACC/AHA guideline for the management of patients with valvular heart disease: A report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol, 2021, 77(4): e25-e197.
10.	Hindricks G, Potpara T, Dagres N, et al. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J, 2021, 42(5): 373-498.
11.	王新荣, 王平基, 孙伟. 中国人体体表面积计算图. 白求恩军医学院学报, 2011, 9(1): 39-40.
12.	Ke G, Meng Q, Finley T, et al. LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017.
13.	Breiman L. Random forests. Machin learn, 2001, 45(1): 5-32.
14.	Sánchez AVD. Advanced support vector machines and kernel methods. Neurocomputing, 2003, 55(1-2): 5-20.
15.	Rish I. An empirical study of the naive Bayes classifier. IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 2001.
16.	Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Stat, 2001: 1189-1232.
17.	Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 2006, 7: 91.
18.	Huang Q, Mao J, Liu Y. An improved grid search algorithm of SVR parameters optimization. 2012 IEEE 14th International Conference on Communication Technology. IEEE, 2012: 1022-1026.
19.	Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017.
20.	Ribeiro MT, Singh S, Guestrin C. "Why should I trust you"? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 1135-1144.
21.	Jin CN, Salgo IS, Schneider RJ, et al. Using anatomic intelligence to localize mitral valve prolapse on three-dimensional echocardiography. J Am Soc Echocardiogr, 2016, 29(10): 938-945.
22.	Boughorbel S, Jarray F, El-Anbari M. Optimal classifier for imbalanced data using Matthews correlation coefficient metric. PLoS One, 2017, 12(6): e0177678.
23.	Uziębło-Życzkowska B, Krzesiński P, Jurek A, et al. Left ventricular ejection fraction is associated with the risk of thrombus in the left atrial appendage in patients with atrial fibrillation. Cardiovasc Ther, 2020, 2020: 3501749.
24.	Kurokawa S, Okumura Y. Atrial fibrillation with valvular heart disease—New insight into clinical outcomes. Circ J, 2020, 84(5): 697-699.
25.	Kebed KY, Addetia K, Lang RM. Importance of the left atrium: More than a bystander? Heart Fail Clin, 2019, 15(2): 191-204.
26.	Lip GYH, Collet JP, Caterina R, et al. Antithrombotic therapy in atrial fibrillation associated with valvular heart disease: A joint consensus document from the European Heart Rhythm Association (EHRA) and European Society of Cardiology Working Group on Thrombosis, endorsed by the ESC Working Group on Valvular Heart Disease, Cardiac Arrhythmia Society of Southern Africa (CASSA), Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), South African Heart (SA Heart) Association and Sociedad Latinoamericana de Estimulación Cardíaca y Electrofisiología (SOLEACE). Europace, 2017, 19(11): 1757-1758.
27.	Qiu D, Peng L, Ghista DN, et al. Left atrial remodeling mechanisms associated with atrial fibrillation. Cardiovasc Eng Technol, 2021, 12(3): 361-372.
28.	Smiljic S. The clinical significance of endocardial endothelial dysfunction. Medicina (Kaunas), 2017, 53(5): 295-302.
29.	Li X, Weber NC, Cohn DM, et al. Effects of hyperglycemia and diabetes mellitus on coagulation and hemostasis. J Clin Med, 2021, 10(11): 2419.
30.	Sagar RC, Ajjan RA, Naseem KM. Non-traditional pathways for platelet pathophysiology in diabetes: Implications for future therapeutic targets. Int J Mol Sci, 2022, 23(9): 4973.

1. Calabrò P, Gragnano F, Cesaro A, et al. Non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation and atrial thrombosis: An appraisal of current evidence. Arch Cardiovasc Dis, 2020, 113(10): 642-651.
2. Aimo A, Kollia E, Ntritsos G, et al. Echocardiography versus computed tomography and cardiac magnetic resonance for the detection of left heart thrombosis: A systematic review and meta-analysis. Clin Res Cardiol, 2021, 110(11): 1697-1703.
3. 吴越峰, 王琪, 吴明. 机器学习技术在食管癌研究领域中应用的现状与展望. 中国胸心血管外科临床杂志, 2022, 29(6): 770-776.
4. Tseng AS, Noseworthy PA. Prediction of atrial fibrillation using machine learning: A review. Front Physiol, 2021, 12: 752317.
5. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell, 2019, 1(5): 206-215.
6. Arrieta AB, Díaz-Rodríguez N, Del Ser J, et al. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inform Fus, 2020, 58: 82-115.
7. Lundberg SM, Erion G, Chen H, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell, 2020, 2(1): 56-67.
8. Sabovčik F, Cauwenberghs N, Kouznetsov D, et al. Applying machine learning to detect early stages of cardiac remodelling and dysfunction. Eur Heart J Cardiovasc Imaging, 2021, 22(10): 1208-1217.
9. Writing Committee Members, Otto CM, Nishimura RA, et al. 2020 ACC/AHA guideline for the management of patients with valvular heart disease: A report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol, 2021, 77(4): e25-e197.
10. Hindricks G, Potpara T, Dagres N, et al. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J, 2021, 42(5): 373-498.
11. 王新荣, 王平基, 孙伟. 中国人体体表面积计算图. 白求恩军医学院学报, 2011, 9(1): 39-40.
12. Ke G, Meng Q, Finley T, et al. LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017.
13. Breiman L. Random forests. Machin learn, 2001, 45(1): 5-32.
14. Sánchez AVD. Advanced support vector machines and kernel methods. Neurocomputing, 2003, 55(1-2): 5-20.
15. Rish I. An empirical study of the naive Bayes classifier. IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 2001.
16. Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Stat, 2001: 1189-1232.
17. Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 2006, 7: 91.
18. Huang Q, Mao J, Liu Y. An improved grid search algorithm of SVR parameters optimization. 2012 IEEE 14th International Conference on Communication Technology. IEEE, 2012: 1022-1026.
19. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017.
20. Ribeiro MT, Singh S, Guestrin C. "Why should I trust you"? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 1135-1144.
21. Jin CN, Salgo IS, Schneider RJ, et al. Using anatomic intelligence to localize mitral valve prolapse on three-dimensional echocardiography. J Am Soc Echocardiogr, 2016, 29(10): 938-945.
22. Boughorbel S, Jarray F, El-Anbari M. Optimal classifier for imbalanced data using Matthews correlation coefficient metric. PLoS One, 2017, 12(6): e0177678.
23. Uziębło-Życzkowska B, Krzesiński P, Jurek A, et al. Left ventricular ejection fraction is associated with the risk of thrombus in the left atrial appendage in patients with atrial fibrillation. Cardiovasc Ther, 2020, 2020: 3501749.
24. Kurokawa S, Okumura Y. Atrial fibrillation with valvular heart disease—New insight into clinical outcomes. Circ J, 2020, 84(5): 697-699.
25. Kebed KY, Addetia K, Lang RM. Importance of the left atrium: More than a bystander? Heart Fail Clin, 2019, 15(2): 191-204.
26. Lip GYH, Collet JP, Caterina R, et al. Antithrombotic therapy in atrial fibrillation associated with valvular heart disease: A joint consensus document from the European Heart Rhythm Association (EHRA) and European Society of Cardiology Working Group on Thrombosis, endorsed by the ESC Working Group on Valvular Heart Disease, Cardiac Arrhythmia Society of Southern Africa (CASSA), Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), South African Heart (SA Heart) Association and Sociedad Latinoamericana de Estimulación Cardíaca y Electrofisiología (SOLEACE). Europace, 2017, 19(11): 1757-1758.
27. Qiu D, Peng L, Ghista DN, et al. Left atrial remodeling mechanisms associated with atrial fibrillation. Cardiovasc Eng Technol, 2021, 12(3): 361-372.
28. Smiljic S. The clinical significance of endocardial endothelial dysfunction. Medicina (Kaunas), 2017, 53(5): 295-302.
29. Li X, Weber NC, Cohn DM, et al. Effects of hyperglycemia and diabetes mellitus on coagulation and hemostasis. J Clin Med, 2021, 10(11): 2419.
30. Sagar RC, Ajjan RA, Naseem KM. Non-traditional pathways for platelet pathophysiology in diabetes: Implications for future therapeutic targets. Int J Mol Sci, 2022, 23(9): 4973.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Prediction and characteristic analysis of cardiac thrombosis in patients with atrial fibrillation undergoing valve disease surgery based on machine learning

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content