Vandendriessche Benjamin, Peperstraete Harlinde, Rogge Elke, Cauwels Peter, Hoste Eric, Stiedl Oliver, Brouckaert Peter, Cauwels Anje
1Inflammation Research Center, VIB, Ghent, Belgium. 2Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium. 3Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium. 4Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland. 5Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, The Netherlands.
Crit Care Med. 2014 Aug;42(8):e560-9. doi: 10.1097/CCM.0000000000000299.
Early detection and start of appropriate treatment are highly correlated with survival of sepsis and septic shock, but the currently available predictive tools are not sensitive enough to identify patients at risk.
Linear (time and frequency domain) and nonlinear (unifractal and multiscale complexity dynamics) measures of beat-to-beat interval variability were analyzed in two mouse models of inflammatory shock to determine if they are sensitive enough to predict outcome.
University research laboratory.
Blood pressure transmitter-implanted female C57BL/6J mice.
IV administration of tumor necrosis factor (n = 11) or lipopolysaccharide (n = 14).
Contrary to linear indices of variability, unifractal dynamics, and absolute heart rate or blood pressure, quantification of complex beat-to-beat dynamics using multiscale entropy was able to predict survival outcome starting as early as 40 minutes after induction of inflammatory shock. Based on these results, a new and clinically relevant index of multiscale entropy was developed that scores the key features of a multiscale entropy profile. Contrary to multiscale entropy, multiscale entropy scoring can be followed as a function of time to monitor disease progression with limited loss of information.
Analysis of multiscale complexity of beat-to-beat dynamics at high temporal resolution has potential as a sensitive prognostic tool with translational power that can predict survival outcome in systemic inflammatory conditions such as sepsis and septic shock.
脓毒症和脓毒性休克的早期检测及开始适当治疗与生存率高度相关，但目前可用的预测工具在识别有风险的患者方面不够敏感。
在两种炎症性休克小鼠模型中分析逐搏间期变异性的线性（时域和频域）及非线性（单分形和多尺度复杂性动力学）指标，以确定它们是否足够敏感来预测结局。
大学研究实验室。
植入血压传感器的雌性C57BL/6J小鼠。
静脉注射肿瘤坏死因子（n = 11）或脂多糖（n = 14）。
与变异性、单分形动力学、绝对心率或血压的线性指标相反，使用多尺度熵对复杂的逐搏动力学进行量化能够早在炎症性休克诱导后40分钟就预测生存结局。基于这些结果，开发了一种新的、具有临床相关性的多尺度熵指标，该指标对多尺度熵图谱的关键特征进行评分。与多尺度熵不同，多尺度熵评分可作为时间的函数进行跟踪，以监测疾病进展，且信息损失有限。
在高时间分辨率下分析逐搏动力学的多尺度复杂性有潜力成为一种敏感的预后工具，具有转化应用价值，能够预测脓毒症和脓毒性休克等全身性炎症状态下的生存结局。
