Dickson J L, Pretty C G, Alsweiler J, Lynn A, Chase J G
Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
Department of Paediatrics: Child and Youth Health, Auckland, New Zealand; Liggins Institute, University of Auckland, Auckland, New Zealand.
Math Biosci. 2017 Feb;284:61-70. doi: 10.1016/j.mbs.2016.08.006. Epub 2016 Aug 30.
Models of human glucose-insulin physiology have been developed for a range of uses, with similarly different levels of complexity and accuracy. STAR (Stochastic Targeted) is a model-based approach to glycaemic control. Elevated blood glucose concentrations (hyperglycaemia) are a common complication of stress and prematurity in very premature infants, and have been associated with worsened outcomes and higher mortality. This research identifies and validates the model parameters for model-based glycaemic control in neonatal intensive care.
C-peptide, plasma insulin, and BG from a cohort of 41 extremely pre-term (median age 27.2 [26.2-28.7] weeks) and very low birth weight infants (median birth weight 839 [735-1000] g) are used alongside C-peptide kinetic models to identify model parameters associated with insulin kinetics in the NICING (Neonatal Intensive Care Insulin-Nutrition-Glucose) model. A literature analysis is used to determine models of kidney clearance and body fluid compartment volumes. The full, final NICING model is validated by fitting the model to a cohort of 160 glucose, insulin, and nutrition data records from extremely premature infants from two different NICUs (neonatal intensive care units).
Six model parameters related to insulin kinetics were identified. The resulting NICING model is more physiologically descriptive than prior model iterations, including clearance pathways of insulin via the liver and kidney, rather than a lumped parameter. In addition, insulin diffusion between plasma and interstitial spaces is evaluated, with differences in distribution volume taken into consideration for each of these spaces. The NICING model was shown to fit clinical data well, with a low model fit error similar to that of previous model iterations.
Insulin kinetic parameters have been identified, and the NICING model is presented for glycaemic control neonatal intensive care. The resulting NICING model is more complex and physiologically relevant, with no loss in bedside-identifiability or ability to capture and predict metabolic dynamics.
人类葡萄糖 - 胰岛素生理模型已被开发用于一系列用途，其复杂程度和准确性也各有不同。STAR（随机靶向）是一种基于模型的血糖控制方法。血糖浓度升高（高血糖症）是极早产儿应激和早产的常见并发症，与不良预后和更高死亡率相关。本研究确定并验证了新生儿重症监护中基于模型的血糖控制的模型参数。
来自41例极早产儿（中位年龄27.2[26.2 - 28.7]周）和极低出生体重儿（中位出生体重839[735 - 1000]g）队列的C肽、血浆胰岛素和血糖数据，与C肽动力学模型一起用于确定NICING（新生儿重症监护胰岛素 - 营养 - 葡萄糖）模型中与胰岛素动力学相关的模型参数。通过文献分析确定肾脏清除模型和体液腔室容积模型。通过将模型拟合来自两个不同新生儿重症监护病房的160例极早产儿的葡萄糖、胰岛素和营养数据记录队列，对完整的最终NICING模型进行验证。
确定了六个与胰岛素动力学相关的模型参数。所得的NICING模型比先前的模型迭代在生理描述上更详细，包括胰岛素通过肝脏和肾脏的清除途径，而不是一个集总参数。此外，评估了血浆和间质空间之间的胰岛素扩散，并考虑了每个空间的分布容积差异。结果表明NICING模型能很好地拟合临床数据，模型拟合误差低，与先前模型迭代相似。
已确定胰岛素动力学参数，并提出了用于新生儿重症监护血糖控制的NICING模型。所得的NICING模型更复杂且与生理相关，在床边可识别性或捕获和预测代谢动力学的能力方面没有损失。
