Mohammadi Ida, Rajai Firouzabadi Shahryar, Hosseinpour Melika, Akhlaghpasand Mohammadhosein, Hajikarimloo Bardia, Zeraatian-Nejad Sam, Sardari Nia Peyman
Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), PO box 14665-354, Tehran, Iran.
Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Teheran, Iran.
BMC Cardiovasc Disord. 2024 Dec 20;24(1):718. doi: 10.1186/s12872-024-04336-6.
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.
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.
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.
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).
先天性心脏病（CHD）是最常见的先天性畸形类型，是导致非传染性疾病负担的重要因素，这凸显了改进风险评估工具的迫切需求。人工智能（AI）有望改善先天性心脏手术的预后预测。本研究旨在系统回顾人工智能在预测该人群术后结局中的应用。
按照PRISMA指南，对PubMed、Scopus和Web of Science数据库进行了全面检索。两名独立评审员根据预先定义的标准筛选文章。纳入的研究聚焦于预测先天性心脏手术各种术后结局的人工智能模型。
该综述纳入了35篇文章，主要发表于过去四年内，表明对人工智能应用的兴趣与日俱增。模型主要针对死亡率和生存率（n = 16）、住院或重症监护病房（ICU）住院时间延长（n = 7）、术后并发症（n = 6）、机械通气支持时间延长（n = 4），此外还关注特定结局，如脑室周围白质软化（n = 2）和营养不良（n = 1）。曲线下面积（AUC）等性能指标范围为0.52至0.997。值得注意的是，这些人工智能模型始终优于传统风险分层类别。例如，在评估发病和死亡风险时，人工智能模型表现优于传统方法。
人工智能驱动的预测模型在改善先天性心脏手术结局预测方面显示出巨大潜力。它们不仅在术后即刻风险方面超越传统风险预测工具，在1年生存率和营养不良等长期结局方面也表现出色。需要进行更有力的外部验证的进一步研究，以评估这些模型在临床环境中的实际适用性。本综述方案已在PROSPERO（CRD42024550942）上进行前瞻性注册。
