Development of a Disease Model for Predicting Postoperative Delirium Using Combined Blood Biomarkers.

IF 4.4 2区 医学 Q1 CLINICAL NEUROLOGY
Hengjun Wan, Huaju Tian, Cheng Wu, Yue Zhao, Daiying Zhang, Yujie Zheng, Yuan Li, Xiaoxia Duan
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引用次数: 0

Abstract

Objective: Postoperative delirium, a common neurocognitive complication after surgery and anesthesia, requires early detection for potential intervention. Herein, we constructed a multidimensional postoperative delirium risk-prediction model incorporating multiple demographic parameters and blood biomarkers to enhance prediction accuracy.

Methods: We included 555 patients undergoing radical surgery for colorectal cancer. Demographic characteristics and lipid profiles were collected preoperatively, and perioperative anesthesia and surgical conditions were recorded; blood biomarkers were measured before and after surgery. The 3D-CAM scale was used to assess postoperative delirium occurrence within 3 days after surgery. Patients were divided into the postoperative delirium (N = 100) and non-postoperative delirium (N = 455) groups. Based on machine learning, linear and nine non-linear models were developed and compared to select the optimal model. Shapley value-interpretation methods and mediation analysis were used to assess feature importance and interaction.

Results: The median age of the participants was 65 years (interquartile range: 56-71 years; 57.8% male). Among the 10 machine-learning models, the random forest model performed the best (validation cohort, area under the receiver operating characteristic curve of 0.795 [0.704-0.885]). Lipid profile (total cholesterol, triglycerides, and trimethylamine-N-oxide) levels were identified as key postoperative delirium predictors. Mediation analysis further confirmed mediating effects among total cholesterol, trimethylamine-N-oxide, and postoperative delirium; a nomogram model was developed as a web-based tool for external validation and use by other clinicians.

Interpretation: Blood biomarkers are crucial in predicting postoperative delirium and aid anesthesiologists in identifying its risks in a timely manner. This model facilitates personalized perioperative management and reduces the occurrence of postoperative delirium.

Trial registration: ChiCTR2300075723.

利用联合血液生物标志物预测术后谵妄的疾病模型的建立。
目的:术后谵妄是手术和麻醉后常见的神经认知并发症,需要早期发现并进行干预。在此,我们构建了一个多维度的术后谵妄风险预测模型,该模型包含多个人口统计学参数和血液生物标志物,以提高预测准确性。方法:我们纳入555例接受根治性手术治疗的结直肠癌患者。术前收集患者人口统计学特征及血脂,记录围术期麻醉及手术情况;在手术前后测量血液生物标志物。采用3D-CAM量表评估术后3天内谵妄的发生情况。将患者分为术后谵妄组(N = 100)和非术后谵妄组(N = 455)。在机器学习的基础上,建立了线性模型和九个非线性模型,并进行了比较,以选择最优模型。采用Shapley值解释法和中介分析评估特征重要性和交互作用。结果:参与者的中位年龄为65岁(四分位数范围:56-71岁;57.8%的男性)。在10个机器学习模型中,随机森林模型表现最好(验证队列,受试者工作特征曲线下面积为0.795[0.704-0.885])。脂质谱(总胆固醇、甘油三酯和三甲胺- n -氧化物)水平被确定为术后谵妄的关键预测因素。中介分析进一步证实了总胆固醇、三甲胺- n -氧化物与术后谵妄之间的中介作用;nomogram模型是一种基于网络的工具,用于外部验证和其他临床医生的使用。解释:血液生物标志物在预测术后谵妄和帮助麻醉师及时识别其风险方面至关重要。这种模式有利于个性化围手术期管理,减少术后谵妄的发生。试验注册:ChiCTR2300075723。
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来源期刊
Annals of Clinical and Translational Neurology
Annals of Clinical and Translational Neurology Medicine-Neurology (clinical)
CiteScore
9.10
自引率
1.90%
发文量
218
审稿时长
8 weeks
期刊介绍: Annals of Clinical and Translational Neurology is a peer-reviewed journal for rapid dissemination of high-quality research related to all areas of neurology. The journal publishes original research and scholarly reviews focused on the mechanisms and treatments of diseases of the nervous system; high-impact topics in neurologic education; and other topics of interest to the clinical neuroscience community.
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