Prediction and risk factors of recurrence of atrial fibrillation in patients with valvular diseases after radiofrequency ablation based on machine learning_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

SHI Huanxu ¹ , HE Peiyu ¹ , TONG Qi ² , WANG Zhengjie ² , LI Tao ² ,  QIAN Yongjun ² , ZHAO Qijun ³ ,  PAN Fan ¹

1. School of Electronic Information, 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 Computer Science (School of Software), Sichuan University, Chengdu, 610065, P. R. China;

Corresponding author：

QIAN Yongjun, Email: qianyongjun@scu.edu.cn; PAN Fan, Email: panfan@scu.edu.cn

Keywords：

Atrial fibrillation; machine learning; recurrence prediction; risk factors

DOI：

10.7507/1007-4848.202204056

Video：

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Objective To use machine learning technology to predict the recurrence of atrial fibrillation (AF) after radiofrequency ablation, and try to find the risk factors affecting postoperative recurrence. Methods A total of 300 patients with valvular AF who underwent radiofrequency ablation in West China Hospital and its branch (Shangjin Hospital) from January 2017 to January 2021 were enrolled, including 129 males and 171 females with a mean age of 52.56 years. We built 5 machine learning models to predict AF recurrence, combined the 3 best performing models into a voting classifier, and made prediction again. Finally, risk factor analysis was performed using the SHApley Additive exPlanations method. Results The voting classifier yielded a prediction accuracy rate of 75.0%, a recall rate of 61.0%, and an area under the receiver operating characteristic curve of 0.79. In addition, factors such as left atrial diameter, ejection fraction, and right atrial diameter were found to have an influence on postoperative recurrence. Conclusion Machine learning-based prediction of recurrence of valvular AF after radiofrequency ablation can provide a certain reference for the clinical diagnosis of AF, and reduce the risk to patients due to ineffective ablation. According to the risk factors found in the study, it can provide patients with more personalized treatment.

Citation： SHI Huanxu, HE Peiyu, TONG Qi, WANG Zhengjie, LI Tao, QIAN Yongjun, ZHAO Qijun, PAN Fan. Prediction and risk factors of recurrence of atrial fibrillation in patients with valvular diseases after radiofrequency ablation based on machine learning. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(7): 840-847. doi: 10.7507/1007-4848.202204056 Copy

1.	Miyasaka Y, Barnes ME, Gersh BJ, et al. Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation, 2006, 114(2): 119-125.
2.	lip GYH, Tse HF, Lane DA. Atrial fibrillation. Lancet, 2012, 379(9816): 648-661.
3.	Hakalahti A, Biancari F, Nielsen JC, et al. Radiofrequency ablation vs. antiarrhythmic drug therapy as first line treatment of symptomatic atrial fibrillation: Systematic review and meta-analysis. Europace, 2015, 17(3): 370-378.
4.	Winkle RA, Jarman JW, Mead RH, et al. Predicting atrial fibrillation ablation outcome: The CAAP-AF score. Heart Rhythm, 2016, 13(11): 2119-2125.
5.	Mesquita J, Ferreira AM, Cavaco D, et al. Development and validation of a risk score for predicting atrial fibrillation recurrence after a first catheter ablation procedure—ATLAS score. Europace, 2018, 20(FI_3): f428-f435.
6.	Winkle RA, Mead RH, Engel G, et al. Long-term results of atrial fibrillation ablation: The importance of all initial ablation failures undergoing a repeat ablation. Am Heart J, 2011, 162(1): 193-200.
7.	Smiti A. When machine learning meets medical world: Current status and future challenges. Comput Sci Rev, 2020, 37: 100280.
8.	Calkins H, Hindricks G, Cappato R, et al. 2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation. Europace, 2018, 20(1): e1-e160.
9.	Chawla NV, Bowyer KW, Hall LO, et al. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res, 2002, 16: 321-57.
10.	Chen T, Guestrin C. XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA, 2016.
11.	Belgiu M, Drăguţ L. Random forest in remote sensing: A review of applications and future directions. ISPRS-J Photogramm Remote Sens, 2016, 114: 24-31.
12.	Schuldt C, Laptev I, Caputo B. Recognizing human actions: A local SVM approach. Proceedings of the 17th International Conference on Pattern Recognition. Cambridge, UK, 2004.
13.	Hastie T, Tibshirani R. Discriminant adaptive nearest neighbor classification and regression. Adv Neural Inf Process Syst, Denver, USA, 1995.
14.	Lavalley MP. Logistic regression. Circulation, 2008, 117(18): 2395-2399.
15.	Lundberg S, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst, Long Beach, CA, USA, 2017.
16.	Kornej J, Hindricks G, Shoemaker MB, et al. The APPLE score: A novel and simple score for the prediction of rhythm outcomes after catheter ablation of atrial fibrillation. Clin Res Cardiol, 2015, 104(10): 871-876.
17.	Soffer S, Klang E, Barash Y, et al. Predicting in-hospital mortality at admission to the medical ward: A big-data machine learning model. Am J Med, 2021, 134(2): 227-234.
18.	卓陈贵. 人工智能预测射频消融术后房颤复发的初步研究. 浙江大学, 2020.
19.	Shamout F, Zhu T, Clifton DA. Machine learning for clinical outcome prediction. IEEE Rev Biomed Eng, 2021, 14: 116-126.
20.	Ikemura K, Bellin E, Yagi Y, et al. Using automated machine learning to predict the mortality of patients with COVID-19: Prediction model development study. J Med Internet Res, 2021, 23(2): e23458.
21.	Roney CH, Sim I, Yu J, et al. Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models. Circ Arrhythm Electrophysiol, 2022, 15(2): e010253.
22.	Firouznia M, Feeny AK, LaBarbera MA, et al. Machine learning-derived fractal features of shape and texture of the left atrium and pulmonary veins from cardiac computed tomography scans are associated with risk of recurrence of atrial fibrillation postablation. Circ Arrhythm Electrophysiol, 2021, 14(3): e9265.
23.	Liu CM, Chang SL, Chen HH, et al. The clinical application of the deep learning technique for predicting trigger origins in patients with paroxysmal atrial fibrillation with catheter ablation. Circ Arrhythm Electrophysiol, 2020, 13(11): e8518.
24.	Budzianowski J, Hiczkiewicz J, Burchardt P, et al. Predictors of atrial fibrillation early recurrence following cryoballoon ablation of pulmonary veins using statistical assessment and machine learning algorithms. Heart Vessels, 2019, 34(2): 352-359.
25.	Alhusseini MI, Abuzaid F, Rogers AJ, et al. Machine learning to classify intracardiac electrical patterns during atrial fibrillation: Machine learning of atrial fibrillation. Circ Arrhythm Electrophysiol, 2020, 13(8): e8160.
26.	Atta-Fosu T, LaBarbera M, Ghose S, et al. A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC Med Imaging, 2021, 21(1): 45.
27.	Toufan M, Kazemi B, Molazadeh N. The significance of the left atrial volume index in prediction of atrial fibrillation recurrence after electrical cardioversion. J Cardiovasc Thorac Res, 2017, 9(1): 54-59.
28.	Luong C, Thompson DJ, Bennett M, et al. Right atrial volume is superior to left atrial volume for prediction of atrial fibrillation recurrence after direct current cardioversion. Can J Cardiol, 2015, 31(1): 29-35.
29.	Takagi T, Nakamura K, Asami M, et al. Impact of right atrial structural remodeling on recurrence after ablation for atrial fibrillation. J Arrhythm, 2021, 37(3): 597-606.
30.	Sasaki T, Nakamura K, Naito S, et al. The right to left atrial volume ratio predicts outcomes after circumferential pulmonary vein isolation of longstanding persistent atrial fibrillation. Pacing Clin Electrophysiol, 2016, 39(11): 1181-1190.
31.	Tse G, Wong CW, Gong M, et al. Predictive value of inter-atrial block for new onset or recurrent atrial fibrillation: A systematic review and meta-analysis. Int J Cardiol, 2018, 250: 152-156.
32.	Gurses KM, Yalcin MU, Kocyigit D, et al. Red blood cell distribution width predicts outcome of cryoballoon-based atrial fibrillation ablation. J Interv Card Electrophysiol, 2015, 42(1): 51-58.

1. Miyasaka Y, Barnes ME, Gersh BJ, et al. Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation, 2006, 114(2): 119-125.
2. lip GYH, Tse HF, Lane DA. Atrial fibrillation. Lancet, 2012, 379(9816): 648-661.
3. Hakalahti A, Biancari F, Nielsen JC, et al. Radiofrequency ablation vs. antiarrhythmic drug therapy as first line treatment of symptomatic atrial fibrillation: Systematic review and meta-analysis. Europace, 2015, 17(3): 370-378.
4. Winkle RA, Jarman JW, Mead RH, et al. Predicting atrial fibrillation ablation outcome: The CAAP-AF score. Heart Rhythm, 2016, 13(11): 2119-2125.
5. Mesquita J, Ferreira AM, Cavaco D, et al. Development and validation of a risk score for predicting atrial fibrillation recurrence after a first catheter ablation procedure—ATLAS score. Europace, 2018, 20(FI_3): f428-f435.
6. Winkle RA, Mead RH, Engel G, et al. Long-term results of atrial fibrillation ablation: The importance of all initial ablation failures undergoing a repeat ablation. Am Heart J, 2011, 162(1): 193-200.
7. Smiti A. When machine learning meets medical world: Current status and future challenges. Comput Sci Rev, 2020, 37: 100280.
8. Calkins H, Hindricks G, Cappato R, et al. 2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation. Europace, 2018, 20(1): e1-e160.
9. Chawla NV, Bowyer KW, Hall LO, et al. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res, 2002, 16: 321-57.
10. Chen T, Guestrin C. XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA, 2016.
11. Belgiu M, Drăguţ L. Random forest in remote sensing: A review of applications and future directions. ISPRS-J Photogramm Remote Sens, 2016, 114: 24-31.
12. Schuldt C, Laptev I, Caputo B. Recognizing human actions: A local SVM approach. Proceedings of the 17th International Conference on Pattern Recognition. Cambridge, UK, 2004.
13. Hastie T, Tibshirani R. Discriminant adaptive nearest neighbor classification and regression. Adv Neural Inf Process Syst, Denver, USA, 1995.
14. Lavalley MP. Logistic regression. Circulation, 2008, 117(18): 2395-2399.
15. Lundberg S, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst, Long Beach, CA, USA, 2017.
16. Kornej J, Hindricks G, Shoemaker MB, et al. The APPLE score: A novel and simple score for the prediction of rhythm outcomes after catheter ablation of atrial fibrillation. Clin Res Cardiol, 2015, 104(10): 871-876.
17. Soffer S, Klang E, Barash Y, et al. Predicting in-hospital mortality at admission to the medical ward: A big-data machine learning model. Am J Med, 2021, 134(2): 227-234.
18. 卓陈贵. 人工智能预测射频消融术后房颤复发的初步研究. 浙江大学, 2020.
19. Shamout F, Zhu T, Clifton DA. Machine learning for clinical outcome prediction. IEEE Rev Biomed Eng, 2021, 14: 116-126.
20. Ikemura K, Bellin E, Yagi Y, et al. Using automated machine learning to predict the mortality of patients with COVID-19: Prediction model development study. J Med Internet Res, 2021, 23(2): e23458.
21. Roney CH, Sim I, Yu J, et al. Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models. Circ Arrhythm Electrophysiol, 2022, 15(2): e010253.
22. Firouznia M, Feeny AK, LaBarbera MA, et al. Machine learning-derived fractal features of shape and texture of the left atrium and pulmonary veins from cardiac computed tomography scans are associated with risk of recurrence of atrial fibrillation postablation. Circ Arrhythm Electrophysiol, 2021, 14(3): e9265.
23. Liu CM, Chang SL, Chen HH, et al. The clinical application of the deep learning technique for predicting trigger origins in patients with paroxysmal atrial fibrillation with catheter ablation. Circ Arrhythm Electrophysiol, 2020, 13(11): e8518.
24. Budzianowski J, Hiczkiewicz J, Burchardt P, et al. Predictors of atrial fibrillation early recurrence following cryoballoon ablation of pulmonary veins using statistical assessment and machine learning algorithms. Heart Vessels, 2019, 34(2): 352-359.
25. Alhusseini MI, Abuzaid F, Rogers AJ, et al. Machine learning to classify intracardiac electrical patterns during atrial fibrillation: Machine learning of atrial fibrillation. Circ Arrhythm Electrophysiol, 2020, 13(8): e8160.
26. Atta-Fosu T, LaBarbera M, Ghose S, et al. A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC Med Imaging, 2021, 21(1): 45.
27. Toufan M, Kazemi B, Molazadeh N. The significance of the left atrial volume index in prediction of atrial fibrillation recurrence after electrical cardioversion. J Cardiovasc Thorac Res, 2017, 9(1): 54-59.
28. Luong C, Thompson DJ, Bennett M, et al. Right atrial volume is superior to left atrial volume for prediction of atrial fibrillation recurrence after direct current cardioversion. Can J Cardiol, 2015, 31(1): 29-35.
29. Takagi T, Nakamura K, Asami M, et al. Impact of right atrial structural remodeling on recurrence after ablation for atrial fibrillation. J Arrhythm, 2021, 37(3): 597-606.
30. Sasaki T, Nakamura K, Naito S, et al. The right to left atrial volume ratio predicts outcomes after circumferential pulmonary vein isolation of longstanding persistent atrial fibrillation. Pacing Clin Electrophysiol, 2016, 39(11): 1181-1190.
31. Tse G, Wong CW, Gong M, et al. Predictive value of inter-atrial block for new onset or recurrent atrial fibrillation: A systematic review and meta-analysis. Int J Cardiol, 2018, 250: 152-156.
32. Gurses KM, Yalcin MU, Kocyigit D, et al. Red blood cell distribution width predicts outcome of cryoballoon-based atrial fibrillation ablation. J Interv Card Electrophysiol, 2015, 42(1): 51-58.

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