Jain Hritvik, Marsool Mohammed Dheyaa Marsool, Odat Ramez M, Noori Hamid, Jain Jyoti, Shakhatreh Zaid, Patel Nandan, Goyal Aman, Gole Shrey, Passey Siddhant
From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India.
Department of Internal Medicine, Al-Kindy College of Medicine, University of Baghdad, Baghdad.
Cardiol Rev. 2024 Jun 5. doi: 10.1097/CRD.0000000000000708.
Sudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexisting cardiac conditions. The ambiguous premortem warnings and the restricted interventional window related to SCD account for the complexity of the condition. Current reports suggest SCD to be accountable for 20% of all deaths hence accurately predicting SCD risk is an imminent concern. Traditional approaches for predicting SCA, particularly "track-and-trigger" warning systems have demonstrated considerable inadequacies, including low sensitivity, false alarms, decreased diagnostic liability, reliance on clinician involvement, and human errors. Artificial intelligence (AI) and machine learning (ML) models have demonstrated near-perfect accuracy in predicting SCA risk, allowing clinicians to intervene timely. Given the constraints of current diagnostics, exploring the benefits of AI and ML models in enhancing outcomes for SCA/SCD is imperative. This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.
心脏性猝死/心脏骤停（SCD/SCA）是全球范围内日益普遍的死亡原因，尤其是在已有心脏疾病的个体中。SCD生前预警不明确以及与之相关的干预窗口期受限，导致了该病症的复杂性。目前的报告显示，SCD占所有死亡人数的20%，因此准确预测SCD风险是当务之急。传统的预测SCA的方法，特别是“跟踪与触发”预警系统，已显示出相当大的不足，包括灵敏度低、误报、诊断可靠性降低、依赖临床医生参与以及人为错误。人工智能（AI）和机器学习（ML）模型在预测SCA风险方面已显示出近乎完美的准确性，使临床医生能够及时进行干预。鉴于当前诊断方法的局限性，探索AI和ML模型在改善SCA/SCD治疗效果方面的益处势在必行。这篇综述文章旨在研究AI和ML模型在预测和管理SCD方面的功效，尤其关注预测的准确性。
