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Model-Based Decision Support Algorithm to Guide Fluid Resuscitation *

机译：引导流体复苏的基于模型的决策支持算法 *

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Fluid resuscitation is the first choice therapy for sepctic shock. However, fluid infusion only increases cardiac output in approximately 50 % of cases, while an excess of fluid can have harmful effects. Therefore, clinicians are looking for indices to predict the effect of fluid infusion on cardiac output, before giving fluid.In this work, a minimal mathematical model of the cardiovascular system is used, representing the heart, an artery and a vein. The nine model parameters, including total stressed blood volume, are identified from experimental data. The experimental data was recorded during three 500 ml fluid infusions on two pigs infected with endotoxin, to simulate septic shock.The total stressed blood volume parameter is negatively associated with the change in cardiac output after fluid infusion, as observed in previous studies. Subsequently, an algorithm is proposed to guide fluid resuscitation, based on the value of this parameter. The use of the algorithm results in 60 % less fluid being given with virtually no effect on cardiac output.The decision algorithm has the potential to be used in human clinical trials since the data required for parameter identification can be obtained in an intensive care unit.
机译：液体复苏是败血症休克的首选疗法。但是，在大约50％的情况下，输液只会增加心输出量，而过多的液体会产生有害影响。因此，临床医生正在寻找指标来预测输液对输液对心输出量的影响，然后再给予输液。在这项工作中，使用了心血管系统的最小数学模型，代表了心脏，动脉和静脉。从实验数据中识别出九种模型参数，包括总应激血容量。实验数据记录在两只被内毒素感染的猪进行3次500 ml输液过程中，以模拟败血性休克，正如先前研究中所观察到的那样，总应激血容量参数与输液后心输出量的变化呈负相关。随后，基于该参数的值，提出了一种指导液体复苏的算法。该算法的使用可减少60％的液体排出量，而对心输出量几乎没有影响。由于可以在重症监护病房中获得参数识别所需的数据，因此该决策算法可用于人类临床试验。

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