PDF(Pubmed)
Abstract：
UNASSIGNED: Sepsis-associated encephalopathy (SAE) is prevalent in intensive care unit (ICU) environments but lacks established treatment protocols, necessitating prompt diagnostic methods for early intervention. Traditional symptom-based diagnostics are non-specific and confounded by sedatives, while emerging biomarkers like neuron-specific enolase (NSE) and S100 calcium-binding protein B (S100B) have limited specificity. Transcranial Doppler (TCD) indicators, although is particularly relevant for SAE, requires high operator expertise, limiting its clinical utility.
UNASSIGNED: This pilot study aims to utilize cerebral circulation time (CCT) assessed via contrast-enhanced ultrasound (CEUS) as an innovative approach to investigate the accuracy of SAE prediction. Further, these CCT measurements are integrated into a nomogram to optimize the predictive performance.
UNASSIGNED: This study employed a prospective, observational design, enrolling 67 ICU patients diagnosed with sepsis within the initial 24 h. Receiver operating characteristic (ROC) curve analyses were conducted to assess the predictive accuracy of potential markers including NSE, S100B, TCD parameters, and CCT for SAE. A nomogram was constructed via multivariate Logistic Regression to further explore the combined predictive potential of these variables. The model\'s predictive performance was evaluated through discrimination, calibration, and decision curve analysis (DCA).
UNASSIGNED: SAE manifested at a median of 2 days post-admission in 32 of 67 patients (47.8%), with the remaining 35 sepsis patients constituting the non-SAE group. ROC curves revealed substantial predictive utility for CCT, pulsatility index (PI), and S100B, with CCT emerging as the most efficacious predictor, evidenced by an area under the curve (AUC) of 0.846. Multivariate Logistic Regression identified these markers as independent predictors for SAE, leading to the construction of a nomogram with excellent discrimination, substantiated by an AUC of 0.924 through bootstrap resampling. The model exhibited satisfactory concordance between observed and predicted probabilities, and DCA confirmed its clinical utility for the prompt identification of SAE.
UNASSIGNED: This study highlighted the enhanced predictive value of CCT in SAE detection within ICU settings. A novel nomogram incorporating CCT, PI, and S100B demonstrated robust discrimination, calibration, and clinical utility, solidifying it as a valuable tool for early SAE intervention.
UNASSIGNED: This pilot study aims to utilize cerebral circulation time (CCT) assessed via contrast-enhanced ultrasound (CEUS) as an innovative approach to investigate the accuracy of SAE prediction. Further, these CCT measurements are integrated into a nomogram to optimize the predictive performance.
UNASSIGNED: This study employed a prospective, observational design, enrolling 67 ICU patients diagnosed with sepsis within the initial 24 h. Receiver operating characteristic (ROC) curve analyses were conducted to assess the predictive accuracy of potential markers including NSE, S100B, TCD parameters, and CCT for SAE. A nomogram was constructed via multivariate Logistic Regression to further explore the combined predictive potential of these variables. The model\'s predictive performance was evaluated through discrimination, calibration, and decision curve analysis (DCA).
UNASSIGNED: SAE manifested at a median of 2 days post-admission in 32 of 67 patients (47.8%), with the remaining 35 sepsis patients constituting the non-SAE group. ROC curves revealed substantial predictive utility for CCT, pulsatility index (PI), and S100B, with CCT emerging as the most efficacious predictor, evidenced by an area under the curve (AUC) of 0.846. Multivariate Logistic Regression identified these markers as independent predictors for SAE, leading to the construction of a nomogram with excellent discrimination, substantiated by an AUC of 0.924 through bootstrap resampling. The model exhibited satisfactory concordance between observed and predicted probabilities, and DCA confirmed its clinical utility for the prompt identification of SAE.
UNASSIGNED: This study highlighted the enhanced predictive value of CCT in SAE detection within ICU settings. A novel nomogram incorporating CCT, PI, and S100B demonstrated robust discrimination, calibration, and clinical utility, solidifying it as a valuable tool for early SAE intervention.
摘要：
■脓毒症相关性脑病（SAE）在重症监护病房（ICU）环境中普遍存在，但缺乏既定的治疗方案，需要及时的诊断方法进行早期干预。传统的基于症状的诊断是非特异性的，并且被镇静剂混淆，而新出现的生物标志物如神经元特异性烯醇化酶（NSE）和S100钙结合蛋白B（S100B）具有有限的特异性。经颅多普勒(TCD)指标,虽然与SAE特别相关，需要很高的操作员专业知识，限制其临床效用。
■这项初步研究旨在利用通过对比增强超声（CEUS）评估的脑循环时间（CCT）作为一种创新方法来研究SAE预测的准确性。Further,这些CCT测量集成到列线图中，以优化预测性能。
■这项研究采用了前瞻性，观测设计，纳入67名ICU患者在最初24小时内被诊断为脓毒症。进行受试者工作特征（ROC）曲线分析，以评估包括NSE在内的潜在标志物的预测准确性。S100B,TCD参数,和SAE的CCT。通过多变量Logistic回归构建列线图以进一步探索这些变量的组合预测潜力。通过区分度评估模型的预测性能，校准,和决策曲线分析(DCA)。
■在67例患者中有32例（47.8％）在入院后2天出现SAE，其余35例脓毒症患者构成非SAE组。ROC曲线揭示了CCT的实质性预测效用，搏动指数(PI),和S100B,随着CCT成为最有效的预测因子，曲线下面积（AUC）为0.846。多变量Logistic回归将这些标志物确定为SAE的独立预测因子，导致具有出色辨别能力的列线图的构造，通过自举重新采样证明AUC为0.924。该模型在观测概率和预测概率之间表现出令人满意的一致性，DCA证实了其临床实用性，可及时鉴定SAE。
■这项研究强调了CCT在ICU环境中SAE检测中的增强预测价值。一种包含CCT的新颖列线图，PI,S100B表现出强大的辨别力，校准,和临床效用，巩固它作为早期SAE干预的有价值的工具。
■这项初步研究旨在利用通过对比增强超声（CEUS）评估的脑循环时间（CCT）作为一种创新方法来研究SAE预测的准确性。Further,这些CCT测量集成到列线图中，以优化预测性能。
■这项研究采用了前瞻性，观测设计，纳入67名ICU患者在最初24小时内被诊断为脓毒症。进行受试者工作特征（ROC）曲线分析，以评估包括NSE在内的潜在标志物的预测准确性。S100B,TCD参数,和SAE的CCT。通过多变量Logistic回归构建列线图以进一步探索这些变量的组合预测潜力。通过区分度评估模型的预测性能，校准,和决策曲线分析(DCA)。
■在67例患者中有32例（47.8％）在入院后2天出现SAE，其余35例脓毒症患者构成非SAE组。ROC曲线揭示了CCT的实质性预测效用，搏动指数(PI),和S100B,随着CCT成为最有效的预测因子，曲线下面积（AUC）为0.846。多变量Logistic回归将这些标志物确定为SAE的独立预测因子，导致具有出色辨别能力的列线图的构造，通过自举重新采样证明AUC为0.924。该模型在观测概率和预测概率之间表现出令人满意的一致性，DCA证实了其临床实用性，可及时鉴定SAE。
■这项研究强调了CCT在ICU环境中SAE检测中的增强预测价值。一种包含CCT的新颖列线图，PI,S100B表现出强大的辨别力，校准,和临床效用，巩固它作为早期SAE干预的有价值的工具。
