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A “Virtual Patient” Cohort and Mathematical Model of Glucose Dynamics in Critical Care

机译：重症监护中葡萄糖动力学的“虚拟患者”队列和数学模型

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Abstract: Stress hyperglycemia is common in critically ill patients and is strongly correlated with increased patient morbidity and mortality. Tight glucose control has been studied as a route to improving patient outcomes by attempting to maintain euglycemia in critical care patients. Unfortunately, insulin sensitivity variability associated with trauma or stress coupled with tight glucose control may also lead to significant hypoglycemia, which has been shown to be strongly correlated with patient mortality. To combat stress hyperglycemia, while taking care to avoid hypoglycemia, a number of systems including closed-loop control with continuous glucose monitoring have been proposed. We synthesize a mathematical model describing a virtual patient cohort, using clinical data, as a means to test these types of algorithms in silico . Virtual patients are primarily characterized by time-varying insulin sensitivity and pancreatic insulin secretion profiles and exhibit trajectories consistent with physiological and clinical expectations. Overall, two patient groups result: (i) good sensitivity to insulin and stable insulin sensitivity trajectories (presumably returned to a healthy state); and (ii) depressed, variable sensitivity.
机译：摘要：应激性高血糖症在重症患者中很常见，并且与患者发病率和死亡率增加密切相关。严格控制血糖已被研究为通过尝试在重症监护患者中维持正常血糖来改善患者预后的途径。不幸的是，与创伤或压力相关的胰岛素敏感性变异性加上严格的血糖控制也可能导致明显的低血糖症，这已被证明与患者死亡率密切相关。为了克服应激性高血糖症，同时注意避免低血糖症，已经提出了包括闭环控制和连续葡萄糖监测在内的许多系统。我们使用临床数据合成了描述虚拟患者队列的数学模型，以此作为在计算机上测试这些类型的算法的手段。虚拟患者的主要特征是随时间变化的胰岛素敏感性和胰腺胰岛素分泌状况，并表现出与生理和临床期望相一致的轨迹。总的来说，有两个患者组：（i）对胰岛素具有良好的敏感性和稳定的胰岛素敏感性轨迹（大概恢复了健康状态）; （ii）情绪低落，敏感度可变。

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