†These authors contributed equally.
Academic Editor: Jerome L. Fleg
Background: A machine learning model was developed to estimate the
in-hospital mortality risk after congenital heart disease (CHD) surgery in
pediatric patient. Methods: Patients with CHD who underwent surgery were
included in the study. A Extreme Gradient Boosting (XGBoost) model was
constructed based onsurgical risk stratification and preoperative variables to
predict the risk of in-hospital mortality. We compared the predictive value of
the XGBoost model with Risk Adjustment in Congenital Heart Surgery-1 (RACHS-1)
and Society of Thoracic Surgery-European Association for
Cardiothoracic Surgery (STS-EACTS) categories. Results: A total of 24,685 patients underwent
CHD surgery and 595 (2.4%) died in hospital.
The area under curve (AUC) of the STS-EACTS and RACHS-1 risk stratification
scores were 0.748 [95% Confidence Interval (CI): 0.707–0.789, p