Chityala Raja Savanth Reddy, Bishwakarma Sandhya, Shah Kaival Malav, Pandey Ashmita, Saad Muhammad
Kamineni Institute of Medical Sciences (KNR University Health Sciences) Narketpally, India.
Ivanovo State Medical Academy, Ivanovo, Russia.
J Electrocardiol. 2025 Mar-Apr;89:153882. doi: 10.1016/j.jelectrocard.2025.153882. Epub 2025 Jan 22.
WHO defines SCD as sudden unexpected death either within 1 h of symptom onset (witnessed) or within 24 h of having been observed alive and symptom-free (unwitnessed). Sudden cardiac arrest is a major cause of mortality worldwide, with survival to hospital discharge for hospital cardiac arrest and in-hospital cardiac arrest being only 9.3 % and 21.2 %, respectively, despite treatment highlighting the importance of effectively predicting and preventing cardiac arrest. This literature review aims to explore the role and application of AI (Artificial Intelligence) in predicting and preventing sudden cardiac arrest.
Eligible studies were searched from PubMed and Web of Science. The inclusion criteria were fulfilled if sudden cardiac death prediction and prevention, artificial intelligence, machine learning, and deep learning were included.
Artificial intelligence, machine learning, and deep learning have shown remarkable prospects in SCA risk stratification, which can improve the survival rate from SCA. Nonetheless, they have not been adequately trained and tested, necessitating further studies with explainable techniques, larger sample sizes, external validation, more diverse patient samples, multimodal tools, ethics, and bias mitigation to unlock their full potential.
世界卫生组织将心源性猝死定义为症状发作后1小时内（有目击者）或在被观察到还活着且无症状24小时内（无目击者）发生的意外猝死。心脏骤停是全球主要的死亡原因，尽管进行了治疗，但院外心脏骤停和院内心脏骤停患者出院生存率分别仅为9.3%和21.2%，这凸显了有效预测和预防心脏骤停的重要性。这篇文献综述旨在探讨人工智能在预测和预防心脏骤停中的作用及应用。
从PubMed和Web of Science检索符合条件的研究。如果研究涉及心脏性猝死的预测与预防、人工智能、机器学习和深度学习，则符合纳入标准。
人工智能、机器学习和深度学习在心源性心脏骤停风险分层方面已展现出显著前景，可提高心源性心脏骤停的生存率。尽管如此，它们尚未得到充分训练和测试，需要进一步开展研究，采用可解释技术、更大样本量、外部验证、更多样化的患者样本、多模态工具、伦理学以及减少偏差等方法，以充分发挥其潜力。
