Du Rui, Yu Yue, Zhu Xiwen, Wang Ranchao, Yang Yu, Li Yang, Zhang Subo, Su Hui
Department of Radiology, Zhenjiang First People's Hospital, Zhenjiang, Jiangsu, People's Republic of China.
Department of Surgery, Lishui People's Hospital, Nanjing, Jiangsu, People's Republic of China.
J Inflamm Res. 2025 Jun 11;18:7585-7597. doi: 10.2147/JIR.S512092. eCollection 2025.
To predict the occurrence of sleeping disorders (SD) in patients with mild traumatic brain injury (mTBI) 3 months after injury.
This study recruited a total of 232 patients with mTBI and underwent a three-month follow-up period. Demographic information, MRI images, and inflammatory factor levels were collected one month after injury and PSQI (Pittsburgh Sleep Quality Index) scores were collected three times respectively on admission, 1 month and 3 months after injury. These mTBI patients were divided into those with SD group (mTBI-SD, n=130) and without SD group (mTBI-ND, n=85) based on PSQI score three months after injury. Differential indicators were used to construct univariate and multivariate logistic regression models, and receiver operating characteristic (ROC) curves were plotted. Pearson correlation analysis was conducted to explore the relationship between the differential indicators and PSQI scores.
Compared to the mTBI-ND group, patients in the mTBI-SD group exhibited lower levels of OLF.L nodal efficiency, ACG.L nodal efficiency, rich-club connection strength, and feeder connection strength, as well as higher levels of IL-8, IL-10, and TNF-α. In the univariate logistic regression model, OLF.L, ACG.L, rich-club connection strength, IL-8, and TNF-αwere identified as risk factors for the occurrence of SD three months after injury. Their Area Under the Curve (AUC) values were 0.669, 0.589, 0.672, 0.649, and 0.709, respectively. Among them, OLF.L nodal efficiency (78.80%) and rich-club connection strength (76.50%) exhibited higher specificity, while TNF-α (73.82%) demonstrated higher sensitivity. According to the multivariate regression results, the combined model constructed had an ROC-AUC of 0.809, with an accuracy of 75.35%, a sensitivity of 74.62%, and a specificity of 76.47%. The correlation results indicate that OLF.L nodal efficiency, rich-club connection strength and TNF-α are significantly correlated with PSQI scores three months after injury (r=-0.461, r =-0.563, r=0.538).
The logistic regression model and ROC curve based on OLF.L nodal efficiency, rich-club connection strength and TNF-α can effectively predict the occurrence of SD in mTBI patients 3 months after injury.
预测轻度创伤性脑损伤(mTBI)患者伤后3个月睡眠障碍(SD)的发生情况。
本研究共招募了232例mTBI患者,并进行了为期3个月的随访。伤后1个月收集人口统计学信息、MRI图像和炎症因子水平,分别在入院时、伤后1个月和3个月收集匹兹堡睡眠质量指数(PSQI)评分。根据伤后3个月的PSQI评分,将这些mTBI患者分为有SD组(mTBI-SD,n = 130)和无SD组(mTBI-ND,n = 85)。使用差异指标构建单因素和多因素逻辑回归模型,并绘制受试者工作特征(ROC)曲线。进行Pearson相关分析以探讨差异指标与PSQI评分之间的关系。
与mTBI-ND组相比,mTBI-SD组患者的嗅叶外侧(OLF.L)节点效率、前扣带回(ACG.L)节点效率、富俱乐部连接强度和馈线连接强度较低,而白细胞介素-8(IL-8)、白细胞介素-10(IL-10)和肿瘤坏死因子-α(TNF-α)水平较高。在单因素逻辑回归模型中,OLF.L、ACG.L、富俱乐部连接强度、IL-8和TNF-α被确定为伤后3个月SD发生的危险因素。它们的曲线下面积(AUC)值分别为0.669、0.589、0.672、0.649和0.709。其中,OLF.L节点效率(78.80%)和富俱乐部连接强度(76.50%)表现出较高的特异性,而TNF-α(73.82%)表现出较高的敏感性。根据多因素回归结果,构建的联合模型的ROC-AUC为0.809,准确率为75.35%,敏感性为74.62%,特异性为76.47%。相关结果表明,伤后3个月OLF.L节点效率、富俱乐部连接强度和TNF-α与PSQI评分显著相关(r = -0.461,r = -0.563,r = 0.538)。
基于OLF.L节点效率、富俱乐部连接强度和TNF-α的逻辑回归模型和ROC曲线可以有效预测mTBI患者伤后3个月SD的发生情况。