Cheungpasitporn Wisit, Thongprayoon Charat, Kashani Kianoush B
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Kidney Res Clin Pract. 2024 Jul;43(4):417-432. doi: 10.23876/j.krcp.23.298. Epub 2024 Jun 20.
Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.
脓毒症相关急性肾损伤（SA-AKI）是危重症患者的一种严重并发症，会导致更高的死亡率、发病率和医疗成本。SA-AKI复杂的病理生理学需要进行密切的临床监测以及恰当、及时的干预。虽然传统统计分析已确定了SA-AKI的严重风险因素，但各研究结果并不一致。这使得人们越来越有兴趣利用人工智能（AI）和机器学习（ML）来更好地预测SA-AKI。ML可以通过分析海量数据集揭示人类难以察觉的复杂模式。像XGBoost和RNN-LSTM这样的监督学习模型在预测SA-AKI发病及后续死亡率方面已被证明具有极高的准确性，常常超过传统风险评分。与此同时，无监督学习揭示了不同SA-AKI患者中与临床相关的亚表型，从而实现更具针对性的治疗。此外，它还可能通过基于患者预后的持续优化来优化脓毒症治疗，以预防SA-AKI。然而，利用AI/ML在数据隐私、算法偏差和监管合规方面存在伦理和实际挑战。AI/ML有助于SA-AKI管理中的早期风险检测、个性化管理、优化治疗策略以及协作学习。未来的方向包括实时患者监测、模拟数据生成以及用于及时干预的预测算法。然而，要顺利过渡到临床实践，需要持续改进模型并进行严格的监管监督。在本文中，我们概述了用于应对SA-AKI的传统方法，并探讨了AI和ML如何应用于SA-AKI的诊断和管理，强调了它们有可能彻底改变SA-AKI的治疗。
