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Original article
Published online: 2024-11-25
Application of machine learning in predicting postoperative arrhythmia following transcatheter closure of perimembranous ventricular septal defects
Abstract
Not available
References
- Penny DJ, Vick GW. Ventricular septal defect. Lancet. 2011; 377(9771): 1103–1112.
- Jiang D, Zhang S, Zhang Y, et al. Predictors and long-term outcomes of heart block after transcatheter device closure of perimembranous ventricular septal defect. Front Cardiovasc Med. 2022; 9: 1041852.
- Weryński P, Skorek P, Wójcik A, et al. Recent achievements in transcatheter closure of ventricular septal defects: a systematic review of literature and a meta-analysis. Kardiol Pol. 2021; 79(2): 161–169.
- Saurav A, Kaushik M, Mahesh Alla V, et al. Comparison of percutaneous device closure versus surgical closure of peri-membranous ventricular septal defects: A systematic review and meta-analysis. Catheter Cardiovasc Interv. 2015; 86(6): 1048–1056.
- Merkin A, Krishnamurthi R, Medvedev ON. Machine learning, artificial intelligence and the prediction of dementia. Curr Opin Psychiatry. 2022; 35(2): 123–129.
- de Jong G, Aquarius R, Sanaan B, et al. Prediction models in aneurysmal subarachnoid hemorrhage: Forecasting clinical outcome with artificial intelligence. Neurosurgery. 2021; 88(5): E427–E434.
- Chen Y, Zheng Y, Lip GYH. Leveraging machine learning for enhanced mortality risk prediction in atrial fibrillation: A step towards precision medicine? Pol Heart J. 2024; 82(10): 921–923.
- Kim Y, Margonis GA, Prescott JD, et al. Nomograms to predict recurrence-free and overall survival after curative resection of adrenocortical carcinoma. JAMA Surg. 2016; 151(4): 365–373.
- Holzer R, de Giovanni Jo, Walsh KP, et al. Transcatheter closure of perimembranous ventricular septal defects using the Amplatzer membranous VSD occluder: Immediate and midterm results of an international registry. Catheter Cardiovasc Interv. 2006; 68(4): 620–628.
- Bergmann M, Germann CP, Nordmeyer J, et al. Short- and long-term outcome after interventional VSD closure: A single-center experience in pediatric and adult patients. Pediatr Cardiol. 2021; 42(1): 78–88.
- Xian SJ, Wei G, Ai ZQ, et al. Risk factors for arrhythmia early after transcatheter closure of perimembranous ventricular septal defects. Chin J Pediatr. 2005; 43(10): 767–771.
- Wang L, Cao S, Li J, et al. Transcatheter closure of congenital perimembranous ventricular septal defect in children using symmetric occluders: An 8-year multiinstitutional experience. Ann Thorac Surg. 2012; 94(2): 592–598.
- Zuo J, Xie J, Yi W, et al. Results of transcatheter closure of perimembranous ventricular septal defect. Am J Cardiol. 2010; 106(7): 1034–1037.
- Zhao LJ, Han Bo, Zhang JJ, et al. Postprocedural outcomes and risk factors for arrhythmias following transcatheter closure of congenital perimembranous ventricular septal defect: A single-center retrospective study. Chin Med J (Engl). 2017; 130(5): 516–521.
- Wu Z, Yang P, Xiang P, et al. Left anterior fascicular block after transcatheter closure of ventricular septal defect in children. Front Cardiovasc Med. 2021; 8: 609531.
- Lei YQ, Lin WH, Lin SH, et al. Influence of percutaneous catheter intervention for congenital perimembranous ventricular septal defects in children on the cardiac conduction system and associated risk factors: a meta-analysis. J Cardiothorac Surg. 2022; 17(1): 19.
- Tang C, Shao S, Zhou K, et al. Complete left bundle-branch block after transcatheter closure of perimembranous ventricular septal defect using Amplatzer duct occluder II. J Am Heart Assoc. 2022; 11(7): e022651.
- Li Y, Hua Y, Fang J, et al. Identification of risk factors for arrhythmia post transcatheter closure of perimembranous ventricular septal defect. J Invasive Cardiol. 2015; 27(8): E158–E166.
- Chen T, Guestrin C. XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 785–794.
- Cherkassky V, Ma Y. Another look at statistical learning theory and regularization. Neural Netw. 2009; 22(7): 958–969.
- Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995; 20(3): 273–297.
- Breiman L. Random forests. Machine Learning. 2001; 45(1): 5–32.
- Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: Users' guides to the medical literature. JAMA. 2017; 318(14): 1377–1384.
- Yang R, Kong XQ, Sheng YH, et al. Risk factors and outcomes of post-procedure heart blocks after transcatheter device closure of perimembranous ventricular septal defect. JACC Cardiovasc Interv. 2012; 5(4): 422–427.
- Tanghöj G, Liuba P, Sjöberg G, et al. Risk factors for adverse events within one year after atrial septal closure in children: A retrospective follow-up study. Cardiol Young. 2020; 30(3): 303–312.
- Kramer H, Körfer R. Early postoperative arrhythmias after cardiac operation in patients with congenital heart disease. Pediatr Cardiol. 2005; 26(1): 10–15.
- Kovach JR, Brown ML. Postoperative arrhythmias after congenital heart surgery. J Thorac Dis. 2013; 5(S6): S526–S533.
- El-Said HG. Transcatheter closure of congenital ventricular septal defects. Cath Cardiovasc Interv. 2003; 60(1): 107–113.
- Terada R, Warren S, Lu JT, et al. Ablation of Nkx2-5 at mid-embryonic stage results in premature lethality and cardiac malformation. Cardiovasc Res. 2011; 91(2): 289–299.
- Gatzoulis MA, Balaji S, Webber SA, et al. Risk factors for arrhythmia and sudden cardiac death late after repair of tetralogy of Fallot: A multicentre study. Lancet. 2000; 356(9234): 975–981.
