Zhao Jian, Dai Rushun, Zhao Yi, Tan Jiaping, Hao Di, Ren Jie, Wang Xianwen, Chen Yanqing, Peng Hu, Zhuang Yugang, Zhou Shuqin, Chen Yuanzhuo
Department of Emergency, Shanghai 10th People's Hospital, Tongji University School of Medicine, Shanghai, 200072, People's Republic of China.
Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China.
J Inflamm Res. 2024 Dec 28;17:11719-11728. doi: 10.2147/JIR.S491137. eCollection 2024.
Sepsis is a heterogeneous clinical syndrome. Identifying distinct clinical phenotypes may enable more targeted therapeutic interventions and improve patient care.
This study aims to use clustering analysis techniques to identify different immune subtypes in sepsis patients and explore their clinical relevance and prognosis.
The study included 236 patients from the EICU at Shanghai Tenth People's Hospital, who met the Sepsis 3.0 diagnostic criteria. Blood samples were collected to measure lymphocyte subsets and cytokine levels, along with demographic and clinical data. K-means clustering analysis was used to categorize patients into three groups based on immune and inflammatory markers.
Three immune subtypes were identified: the high immune activation subtype (Cluster 1), characterized by high levels of CRP and WBC, high levels of T cells, NK cells, and B cells, and low levels of IL-6, IL-8, and IL-10; the moderate immune activation subtype (Cluster 2), characterized by moderate levels of CRP, WBC, T cells, NK cells, B cells, IL-6, IL-8, and IL-10; and the high inflammation and immune suppression subtype (Cluster 3), characterized by very high levels of IL-6, IL-8, and IL-10, low levels of T cells, NK cells, and B cells, and relatively lower CRP levels. Patients in Cluster 3 had a significantly increased 28-day mortality risk compared to those in Cluster 1 (HR = 21.65, 95% CI: 7.46-62.87, p < 0.001). Kaplan-Meier survival curves showed the lowest survival rates for Cluster 3 and the highest for Cluster 1, with the differences among the three groups being highly statistically significant (p < 0.0001).
This study identified three immune subtypes of sepsis that are significantly associated with clinical outcomes. These findings provide evidence for personalized treatment strategies to improve patient outcomes.
脓毒症是一种异质性临床综合征。识别不同的临床表型可能有助于采取更具针对性的治疗干预措施并改善患者护理。
本研究旨在使用聚类分析技术识别脓毒症患者的不同免疫亚型，并探讨其临床相关性和预后。
该研究纳入了上海第十人民医院急诊重症监护病房（EICU）符合脓毒症3.0诊断标准的236例患者。采集血样以检测淋巴细胞亚群和细胞因子水平，同时收集人口统计学和临床数据。采用K均值聚类分析根据免疫和炎症标志物将患者分为三组。
识别出三种免疫亚型：高免疫激活亚型（聚类1），其特征为CRP和白细胞水平高，T细胞、自然杀伤（NK）细胞和B细胞水平高，而白细胞介素（IL）-6、IL-8和IL-10水平低；中度免疫激活亚型（聚类2），其特征为CRP、白细胞、T细胞、NK细胞、B细胞、IL-6、IL-8和IL-10水平中等；高炎症和免疫抑制亚型（聚类3），其特征为IL-6、IL-8和IL-10水平非常高，T细胞、NK细胞和B细胞水平低，且CRP水平相对较低。与聚类1中的患者相比，聚类3中的患者28天死亡风险显著增加（风险比[HR]=21.65，95%置信区间[CI]：7.46 - 62.87，p<0.001）。Kaplan-Meier生存曲线显示聚类3的生存率最低，聚类1的生存率最高，三组之间的差异具有高度统计学意义（p<0.0001）。
本研究识别出与临床结局显著相关的三种脓毒症免疫亚型。这些发现为改善患者结局的个性化治疗策略提供了证据。
