Wang Ruoran, Xu Jianguo, He Min
Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Front Immunol. 2025 Jan 9;15:1504668. doi: 10.3389/fimmu.2024.1504668. eCollection 2024.
Leukocytes play an important role in inflammatory response after a traumatic brain injury (TBI). We designed this study to identify TBI phenotypes by clustering blood levels of various leukocytes.
TBI patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were included. Blood levels of neutrophils, lymphocytes, monocytes, basophils, and eosinophils were collected by analyzing the first blood sample within 24 h since admission. Overall, TBI patients were divided into clusters following the K-means clustering method using blood levels of five types of leukocytes. The correlation between identified clusters and mortality was tested by univariate and multivariate logistic regression analyses. The Kaplan-Meier method was used to verify the survival difference between identified TBI clusters.
A total of 172 (cluster 1), 791 (cluster 2), and 636 (cluster 3) TBI patients were divided into three clusters with the following percentages, 10.8%, 49.5%, and 39.8%, respectively. Cluster 1 had the lowest Glasgow Coma Scale (GCS) and the highest Injury Severity Score (ISS) while cluster 2 had the highest GCS and the lowest ISS. The mortality rates of the three clusters were 25.6%, 13.3%, and 18.1%, respectively. The multivariate logistic regression indicated that cluster 1 had a higher mortality risk (OR = 2.211, p = 0.003) than cluster 2, while cluster 3 did not show a significantly higher mortality risk than cluster 2 (OR = 1.285, p = 0.163). Kapan-Meier analysis showed that cluster 1 had shorter survival than cluster 2 and cluster 3.
Three TBI phenotypes with different inflammatory statuses and mortality rates were identified based on blood levels of leukocytes. This classification is helpful for physicians to evaluate the prognosis of TBI patients.
白细胞在创伤性脑损伤（TBI）后的炎症反应中起重要作用。我们设计了本研究，通过对各种白细胞的血液水平进行聚类来识别TBI的表型。
纳入重症监护医学信息数据库-III（MIMIC-III）中的TBI患者。通过分析入院后24小时内的第一份血液样本，收集中性粒细胞、淋巴细胞、单核细胞、嗜碱性粒细胞和嗜酸性粒细胞的血液水平。总体而言，使用五种白细胞的血液水平，按照K均值聚类方法将TBI患者分为不同的聚类。通过单因素和多因素逻辑回归分析检验所识别聚类与死亡率之间的相关性。采用Kaplan-Meier方法验证所识别的TBI聚类之间的生存差异。
总共172例（聚类1）、791例（聚类2）和636例（聚类3）TBI患者被分为三个聚类，其百分比分别为10.8%、49.5%和39.8%。聚类1的格拉斯哥昏迷量表（GCS）最低，损伤严重程度评分（ISS）最高，而聚类2的GCS最高，ISS最低。三个聚类的死亡率分别为25.6%、13.3%和18.1%。多因素逻辑回归表明，聚类1的死亡风险高于聚类2（OR = 2.211，p = 0.003），而聚类3的死亡风险与聚类2相比没有显著升高（OR = 1.285，p = 0.163）。Kaplan-Meier分析表明，聚类1的生存期短于聚类2和聚类3。
基于白细胞的血液水平，识别出了三种具有不同炎症状态和死亡率的TBI表型。这种分类有助于医生评估TBI患者的预后。
