Key result
Artificial intelligence prediction models demonstrated strong performance in predicting post-operative outcomes in congenital heart surgery, with AUCs ranging from 0.52 to 0.997, consistently outperforming traditional risk stratification methods.
Why the study?
Congenital heart disease is a major contributor to non-communicable disease burden, highlighting the need for improved risk assessment tools to predict congenital cardiac surgery outcomes.
Do artificial intelligence models improve the prediction of post-operative outcomes in patients undergoing congenital heart surgery?
Systematic Review
Do artificial intelligence models improve the prediction of post-operative outcomes in patients undergoing congenital heart surgery?
AI-driven prediction models demonstrate superior performance compared to traditional risk stratification tools for predicting postoperative outcomes in congenital heart surgery.
May enhance risk stratification and counseling in congenital heart surgery; extends support for AI over traditional tools in systematic evidence.
INTRODUCTION: Congenital heart disease (CHD) represents the most common group of congenital anomalies, constitutes a significant contributor to the burden of non-communicable diseases, highlighting the critical need for improved risk assessment tools. Artificial intelligence (AI) holds promise in enhancing outcome predictions for congenital cardiac surgery. This study aims to systematically review the utilization of AI in predicting post-operative outcomes in this population. METHODS: Following PRISMA guidelines, a comprehensive search of Pubmed, Scopus, and Web of Science databases was conducted. Two independent reviewers screened articles based on predefined criteria. Included studies focused on AI models predicting various post-operative outcomes in congenital heart surgery. RESULTS: The review included 35 articles, primarily published within the last four years, indicating growing interest in AI applications. Models predominantly targeted mortality and survival (n = 16), prolonged length of hospital or ICU stay (n = 7), postoperative complications (n = 6), prolonged mechanical ventilatory support time (n = 4), with additional focus on specific outcomes such as peri-ventricular leucomalacia (n = 2) and malnutrition (n = 1). Performance metrics, such as area under the curve (AUC), ranged from 0.52 to 0.997. Notably, these AI models consistently outperformed traditional risk stratification categories. For instance, in assessing the risk of morbidity and mortality, the AI models demonstrated superior performance compared to conventional methods. CONCLUSION: AI-driven prediction models show significant promise in improving outcome predictions for congenital heart surgery. They surpass traditional risk prediction tools not only in immediate postoperative risks but also in long-term outcomes such as 1-year survival and malnutrition. Further studies with robust external validation are necessary to assess the practical applicability of these models in clinical settings. The protocol of this review was prospectively registered on PROSPERO (CRD42024550942).
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Mohammadi et al. (2024) conducted a systematic review in Congenital heart disease. Artificial intelligence prediction models vs. Traditional risk stratification categories was evaluated on Post-operative outcomes (including mortality, survival, prolonged length of stay, and complications). Artificial intelligence prediction models demonstrated strong performance in predicting post-operative outcomes in congenital heart surgery, with AUCs ranging from 0.52 to 0.997, consistently outperforming traditional risk stratification methods.
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