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全膝关节或髋关节置换术患者术前脑脊液sTREM2浓度与术后谵妄及三年死亡率的关联:一项前瞻性队列研究

Association of preoperative cerebrospinal fluid sTREM2 concentration with postoperative delirium and three-year mortality in patients total knee or hip arthroplasty: a prospective cohort study.

作者信息

Wang Bin, Wang Xiao, Liang Yizhi, Wang Jiahan, Hua Shuhui, Kong Jian, Xu Shanling, Yang Yunchao, Lin Yanan, Li Chuan, Gong Hongyan, Lin Xu, Bi Yanlin

机构信息

Department of Anesthesiology, Qingdao Municipal Hospital, Qingdao, Shandong Province, China.

Department of Anesthesiology, Binzhou Medical University, Binzhou, Shandong Province, China.

出版信息

Int J Surg. 2025 Sep 10. doi: 10.1097/JS9.0000000000003465.

DOI:10.1097/JS9.0000000000003465
PMID:40928387
Abstract

BACKGROUND

As a common postoperative neurological complication, postoperative delirium (POD) can lead to poor postoperative recovery in patients, prolonged hospitalization, and even increased mortality. However, POD's mechanism remains undefined and there are no reliable molecular markers of POD to date. The present work examined the associations of cerebrospinal fluid (CSF) sTREM2 with CSF POD biomarkers, and investigated whether the effects of CSF sTREM2 on POD were modulated by the core pathological indexes of POD (Aβ42, tau, and ptau). The association of presurgical CSF sTREM2 with 3-year mortality in POD cases administered total knee or hip arthroplasty was assessed.

METHODS

We enrolled 545 Chinese Han patients undergoing total knee or hip arthroplasty (aged 50-95, weighing 50-80 kg, and using ASA II-III) combined with epidural anesthesia between October 2020 and March 2022. In these participants, POD was identified using the Confusion Assessment Method (CAM) and the severity of POD was evaluated using the Memorial Delirium Assessment Scale (MDAS) at 1-7 days postoperatively (or before discharge) by an anesthesiologist. The levels of cerebrospinal fluid (CSF) POD biomarkers were measured by ELISA. Next, logistic regression models were used to analyze the association between sTREM2and POD, as well as between cerebrospinal fluid (CSF) biomarkers and POD. We used Stata MP16.0. to examine whether the association between sTREM2 and POD was mediated by CSF POD biomarkers. We also used potential predictive factors to built 5 models, including Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbours (KNN), AdaBoost and CatBoost to assess the predictive abilities of sTREM2. After that, we verified the performance of the 5 models in the set, plotting receiver operating characteristic (ROC) curve analysis and precision recall curve (PRC) were used to further evaluate whether the machine learning (ML) models were effective in supporting clinical decision-making. All POD patients were followed up for three years, and Kaplan-Meier (K-M) survival analysis was used to compare the 3-year mortality rates of high sTREM2 group and low sTREM2 group in patients with POD.

RESULTS

Finally, a total of 545 patients (122patients in POD group and 423in NPOD group) were included in our study. sTREM2 and CSF levels of tau and ptau in the POD group were higher than those in the NPOD group. CSF Aβ42, Aβ42/ tau, and Aβ42/ ptau in the POD group were lower than those in the NPOD group. CSF sTREM2 was negatively associated with Aβ42 (r = - 0.445, P < 0.001), Aβ42/ tau (r = - 0.350, P < 0.001) and Aβ42/ ptau (r = - 0.429, P < 0.001), CSF sTREM2 was positively associated with tau (r = 0.179, P = 0.048) and ptau (r = 0.311, P < 0.001). The relationship between sTREM2 and POD was partially mediated by tau and ptau, with the mediation proportion of 17.91% and 22.09%, respectively. The following 5 variables (sTREM2, age, tau, ptau and Aβ42/ptau) were significant predictive factors via Lasso regression. Meanwhile, univariable analysis demonstrated CSF Aβ42/ptau levels was the protective factor of POD and sTREM2, age, tau, ptau were the risk factors of POD. Upon adjusting for possible confounders, including education level, sex, MMSE score, as well as history of diabetes, smoking, drinking, and hypertension, multivariable analysis showed consistent results. Following two rounds of sensitivity analysis, our results remained robust.The ROC(AUC = 0.999,95%CI:0.999-1.000) and PRC(AUC = 0.998,95%CI:0.995-1.000) for CatBoost were significantly better than the other models. The dynamic online calculator can accurately predict the occurrence of POD by selecting POD patients for the internal validation study. The Kaplan-Meier curve showed no significant difference in survival probability between the low sTREM2 group and high sTREM2 group (log-rank P = 0.53), but age subgroup analysis revealed significantly between age≥80 plus sTREM2 ≥ 20 000 pg/ml subgroup and the other subgroups on mortality in patients with POD (log-rank P = 0.017).

CONCLUSION

Elevated CSF sTREM2 is a preoperative risk factor for POD, which is partially mediated by tau and ptau. The CatBoost model can accurately predict the occurrence of POD. Age≥80 plus sTREM2 ≥ 20 000 pg/ml could increase 3-year mortality in POD cases.

摘要

背景

术后谵妄(POD)作为一种常见的术后神经并发症,可导致患者术后恢复不佳、住院时间延长,甚至死亡率增加。然而,POD的发病机制仍不明确,迄今为止尚无可靠的POD分子标志物。本研究探讨了脑脊液(CSF)可溶性触发受体表达分子2(sTREM2)与CSF中POD生物标志物的相关性,并研究了CSF sTREM2对POD的影响是否受POD核心病理指标(β淀粉样蛋白42(Aβ42)、tau蛋白和磷酸化tau蛋白(ptau))的调节。评估了接受全膝关节或髋关节置换术的POD患者术前CSF sTREM2与3年死亡率的相关性。

方法

我们纳入了2020年10月至2022年3月期间545例接受全膝关节或髋关节置换术(年龄50 - 95岁,体重50 - 80 kg,美国麻醉医师协会(ASA)分级为II - III级)并采用硬膜外麻醉的中国汉族患者。在这些参与者中,术后1 - 7天(或出院前)由麻醉医生使用意识模糊评估法(CAM)识别POD,并使用 Memorial谵妄评估量表(MDAS)评估POD的严重程度。采用酶联免疫吸附测定法(ELISA)检测脑脊液(CSF)中POD生物标志物的水平。接下来,使用逻辑回归模型分析sTREM2与POD之间以及CSF生物标志物与POD之间的关联。我们使用Stata MP16.0软件检验sTREM2与POD之间的关联是否由CSF POD生物标志物介导。我们还使用潜在预测因素构建了5种模型,包括逻辑回归(LR)、支持向量机(SVM)、K近邻算法(KNN)、自适应增强算法(AdaBoost)和梯度提升分类算法(CatBoost),以评估sTREM2的预测能力。之后,我们在该队列中验证这5种模型的性能,绘制受试者工作特征(ROC)曲线分析图,并使用精确召回率曲线(PRC)进一步评估机器学习(ML)模型在支持临床决策方面是否有效。对所有POD患者进行了3年随访,采用Kaplan - Meier(K - M)生存分析比较POD患者中高sTREM2组和低sTREM2组的3年死亡率。

结果

最终,本研究共纳入545例患者(POD组122例,非POD组423例)。POD组中sTREM2以及CSF中tau蛋白和ptau的水平高于非POD组。POD组中CSF Aβ蛋白42、Aβ42/tau和Aβ蛋白42/ptau低于非POD组。CSF sTREM2与Aβ42呈负相关(r = - 0.445,P < 0.001)、与Aβ42/tau呈负相关(r = - 0.350,P < 0.001)以及与Aβ42/ptau呈负相关(r = - 0.429,P < 0.001),CSF sTREM2与tau呈正相关(r = 0.179,P = 0.048)以及与ptau呈正相关(r = 0.311,P < 0.001)。sTREM2与POD之间的关系部分由tau和ptau介导,介导比例分别为17.91%和22.09%。通过套索回归分析,以下5个变量(sTREM2、年龄、tau、ptau和Aβ42/ptau)是显著的预测因素。同时,单因素分析表明CSF Aβ42/ptau水平是POD的保护因素,而sTREM2、年龄、tau、ptau是POD的危险因素。在调整了可能的混杂因素,包括教育水平、性别、简易精神状态检查表(MMSE)评分以及糖尿病、吸烟、饮酒和高血压病史后,多因素分析显示结果一致。经过两轮敏感性分析,我们的结果仍然稳健。CatBoost模型的ROC曲线(AUC = 0.999,95%CI:0.999 - 1.000)和PRC曲线(AUC = 0.998,95%CI:0.995 -

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