PasqualiSKThibaultDO'BrienSM, et al.National variation in congenital heart surgery outcomes. Circulation. 2020;142(14):1351–1360.
2.
JacobsJPO'BrienSMHillKD, et al.Refining The society of thoracic surgeons congenital heart surgery database mortality risk model With enhanced risk adjustment for chromosomal abnormalities, syndromes, and noncardiac congenital anatomic abnormalities. Ann Thorac Surg. 2019;108(2):558–566.
3.
PagelCBrownKLCroweSUtleyMCunninghamDTsangV. A mortality risk model to adjust for case mix in UK paediatric cardiac surgery. Health Serv Deliv Res. 2013;1(1). doi:10.3310/hsdr01010.
4.
BertsimasDZhuoDDunnJ, et al.Adverse outcomes prediction for congenital heart surgery: a machine learning approach. World J Ped Cong Heart Surg. 2021;12(4):453–460.
5.
BertsimasDZhuoDLevineJ,et al.Benchmarking in Congenital Heart Surgery Using Machine Learning-Derived Optimal Classification Trees. World J Ped Cong Heart Surg. 2022;13(1):23–35.
6.
ShahianDMLippmannRP. Machine learning and cardiac surgery risk prediction. J Thorac Cardiovasc Surg. 2020;S0022-5223(20):32444-2.
7.
SprayTLGaynorJW. A word of caution in public reporting. Semin Thorac Cardiovasc Surg Pediatr Card Surg Ann. 2017;20:59–55.
8.
https://health.usnews.com/health-news/blogs/second-opinion/articles/2017-10-20/correspondence-on-the-pediatric-cardiology-heart-surgery-rankings. Accessed November 3, 2021.