Center for Model-based Medical Decision Support, Aalborg University, Fredrik-Bajers-Vej 7, E4-215, 9220 Aalborg, Denmark.
Comput Methods Programs Biomed. 2010 Mar;97(3):211-22. doi: 10.1016/j.cmpb.2009.06.004. Epub 2009 Jul 25.
Consistent tight blood sugar control in critically ill patients has proven elusive. Properly accounting for the saturation of insulin action and reducing the need for frequent measurements are important aspects in intensive insulin therapy. This paper presents a composite metabolic model, 'Glucosafe', that integrates models and parameters from normal physiology and accounts for the reduced rate of glucose gut absorption and saturation of insulin action in patients with reduced insulin sensitivity. Particularly, two different sites of reduced insulin sensitivity, before and after the non-linearity of insulin action, are explored with this model. These approaches are assessed based on the model's accuracy in retrospectively predicting blood glucose measurements of 10 randomly chosen, hyperglycemic intensive care patients. For each patient, median absolute percent error is <25% for prediction times < or = 270min and modelling reduced insulin sensitivity after the non-linearity, compared to <29% for modelling reduced insulin sensitivity before the non-linearity. Scaling the insulin effect (after the non-linearity) is a suitable assumption in this model structure. These results are preliminary and subject to further and more extensive validation of the model's capability to predict the longer term (>2h) blood glucose excursion in critically ill patients.
在危重病患者中实现严格的血糖控制一直难以实现。正确考虑胰岛素作用的饱和度并减少频繁测量的需求是强化胰岛素治疗的重要方面。本文提出了一种综合代谢模型“Glucosafe”，它整合了正常生理学的模型和参数，并考虑了胰岛素敏感性降低患者的葡萄糖肠道吸收速率降低和胰岛素作用的饱和度。特别是，该模型探索了胰岛素作用非线性前后两种不同的胰岛素敏感性降低部位。这些方法是基于该模型在回顾性预测 10 名随机选择的高血糖重症监护患者的血糖测量值的准确性进行评估的。对于每个患者，预测时间<270min 时，中位数绝对百分比误差<25%，而建模胰岛素作用的非线性之后的胰岛素敏感性降低，与建模胰岛素作用的非线性之前的胰岛素敏感性降低相比，<29%。在这种模型结构中，缩放胰岛素作用（非线性之后）是一个合适的假设。这些结果是初步的，并且需要进一步和更广泛地验证该模型预测危重病患者长期（>2 小时）血糖波动的能力。
