Antcliffe David B, Burrell Aidan, Boyle Andrew J, Gordon Anthony C, McAuley Daniel F, Silversides Jon
Division of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Imperial College London, London, UK.
Intensive Care Unit, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK.
Intensive Care Med. 2025 Apr;51(4):756-768. doi: 10.1007/s00134-025-07873-6. Epub 2025 Mar 31.
Heterogeneity between critically ill patients with sepsis is a major barrier to the discovery of effective therapies. The use of machine learning techniques, coupled with improved understanding of sepsis biology, has led to the identification of patient subphenotypes. This exciting development may help overcome the problem of patient heterogeneity and lead to the identification of patient subgroups with treatable traits. Re-analyses of completed clinical trials have demonstrated that patients with different subphenotypes may respond differently to treatments. This suggests that future clinical trials that take a precision medicine approach will have a higher likelihood of identifying effective therapeutics for patients based on their subphenotype. In this review, we describe the emerging subphenotypes identified in the critically ill and outline the promising immune modulation therapies which could have a beneficial treatment effect within some of these subphenotypes. Furthermore, we will also highlight how bringing subphenotype identification to the bedside could enable a new generation of precision-medicine clinical trials.
脓毒症重症患者之间的异质性是发现有效治疗方法的主要障碍。机器学习技术的应用，加上对脓毒症生物学认识的提高，已促使识别出患者亚表型。这一令人兴奋的进展可能有助于克服患者异质性问题，并促成识别出具有可治疗特征的患者亚组。对已完成临床试验的重新分析表明，具有不同亚表型的患者对治疗的反应可能不同。这表明，未来采用精准医学方法的临床试验更有可能根据患者的亚表型为其确定有效的治疗方法。在本综述中，我们描述了在重症患者中识别出的新兴亚表型，并概述了有望在其中一些亚表型中产生有益治疗效果的免疫调节疗法。此外，我们还将强调将亚表型识别应用于临床如何能够推动新一代精准医学临床试验的开展。
