The Efficacy of Artificial Intelligence in Predicting the Postoperative Mortality Rate in Patients with Congenital Heart Disease: A Systematic Review and Meta-Analysis.

Highlights: Artificial intelligence (AI) models, especially Extreme Gradient Boosting (XGBoost) and Gradient Boosting Machine (GBM), achieved the highest predictive performance for postoperative mortality in congenital heart disease (CHD) patients, with pooled AUC values of 0.93 and 0.91, respectively.The meta-analysis demonstrated that AI-assisted prediction tools provide high specificity (0.96) and robust accuracy compared to conventional risk stratification systems.Integrating AI into clinical workflows can enhance perioperative decision-making and improve patient outcomes in CHD, though further multicenter validation is needed for broader implementation. Background: Congenital heart disease (CHD) is a leading cause of morbidity and mortality in children requiring surgical intervention. Accurate prediction of postoperative mortality remains challenging because of the limitations of traditional risk stratification systems. Artificial intelligence (AI) has emerged as a promising tool for enhancing predictive accuracy in this field. Objective: This systematic review and meta-analysis aimed to evaluate the efficacy of AI in predicting postoperative mortality in patients with CHD. Methods: Following the PRISMA guidelines, we systematically searched four databases for relevant studies published up to July 16, 2024. Studies with retrospective, prospective, or cross-sectional designs that evaluated AI-based models for predicting mortality after CHD surgery were eligible for inclusion. Data were extracted, and study quality was assessed using the PROBAST tool. Pooled estimates for sensitivity, specificity, and the area under the curve (AUC) were calculated. Results: Six studies involving 42,536 patients and evaluating 11 distinct AI models were included. The meta-analysis yielded a pooled AUC of 0.90 (95% CI, 0.88 to 0.93), with a pooled sensitivity of 0.43 (95% CI, 0.23 to 0.65) and a pooled specificity of 0.96 (95% CI, 0.92 to 0.98). Subgroup analysis revealed that the Extreme Gradient Boosting (AUC, 0.93) and Gradient Boosting Machine (AUC, 0.91) models had the highest predictive performance. All included studies were judged to have a low risk of bias. Conclusion: The Extreme Gradient Boosting and Gradient Boosting Machine models demonstrate high specificity and promising accuracy for predicting postoperative mortality in patients with CHD, outperforming traditional scoring systems. Further multicenter, prospective studies are needed to enhance generalizability and support clinical implementation.