Braithwaite Susan S, Godara Hemant, Song Julie, Cairns Bruce A, Jones Samuel W, Umpierrez Guillermo E
University of Illinois Chicago, Chicago, Illinois 60202, USA.
J Diabetes Sci Technol. 2009 Jul 1;3(4):835-56. doi: 10.1177/193229680900300432.
Algorithms for intravenous insulin infusion may assign the infusion rate (IR) by a two-step process. First, the previous insulin infusion rate (IR(previous)) and the rate of change of blood glucose (BG) from the previous iteration of the algorithm are used to estimate the maintenance rate (MR) of insulin infusion. Second, the insulin IR for the next iteration (IR(next)) is assigned to be commensurate with the MR and the distance of the current blood glucose (BG(current)) from target. With use of a specific set of algorithm parameter values, a family of iso-MR curves is created, each giving IR as a function of MR and BG.
To test the feasibility of estimating MR from the IR(previous) and the previous rate of change of BG, historical hyperglycemic data points were used to compute the "maintenance rate cross step next estimate" (MR(csne)). Historical cases had been treated with intravenous insulin infusion using a tabular protocol that estimated MR according to column-change rules. The mean IR on historical stable intervals (MR(true)), an estimate of the biologic value of MR, was compared to MR(csne) during the hyperglycemic iteration immediately preceding the stable interval. Hypothetically calculated MR(csne)-dependent IR(next) was compared to IR(next) assigned historically. An expanded theory of an algorithm is developed mathematically. Practical recommendations for computerization are proposed.
The MR(true) determined on each of 30 stable intervals and the MR(csne) during the immediately preceding hyperglycemic iteration differed, having medians with interquartile ranges 2.7 (1.2-3.7) and 3.2 (1.5-4.6) units/h, respectively. However, these estimates of MR were strongly correlated (R(2) = 0.88). During hyperglycemia at 941 time points the IR(next) assigned historically and the hypothetically calculated MR(csne)-dependent IR(next) differed, having medians with interquartile ranges 4.0 (3.0-6.0) and 4.6 (3.0-6.8) units/h, respectively, but these paired values again were correlated (R(2) = 0.87). This article describes a programmable algorithm for intravenous insulin infusion. The fundamental equation of the algorithm gives the relationship among IR; the biologic parameter MR; and two variables expressing an instantaneous rate of change of BG, one of which must be zero at any given point in time and the other positive, negative, or zero, namely the rate of change of BG from below target (rate of ascent) and the rate of change of BG from above target (rate of descent). In addition to user-definable parameters, three special algorithm parameters discoverable in nature are described: the maximum rate of the spontaneous ascent of blood glucose during nonhypoglycemia, the glucose per daily dose of insulin exogenously mediated, and the MR at given patient time points. User-assignable parameters will facilitate adaptation to different patient populations.
An algorithm is described that estimates MR prior to the attainment of euglycemia and computes MR-dependent values for IR(next). Design features address glycemic variability, promote safety with respect to hypoglycemia, and define a method for specifying glycemic targets that are allowed to differ according to patient condition.
静脉输注胰岛素的算法可能通过两步过程来确定输注速率（IR）。首先，利用先前的胰岛素输注速率（IR(previous)）和算法上一次迭代时血糖（BG）的变化速率来估算胰岛素输注的维持速率（MR）。其次，将下一次迭代的胰岛素IR（IR(next)）设定为与MR以及当前血糖（BG(current)）与目标值的差距相匹配。通过使用一组特定的算法参数值，创建了一族等MR曲线，每条曲线都将IR表示为MR和BG的函数。
为了测试从IR(previous)和先前的BG变化速率估算MR的可行性，利用历史高血糖数据点来计算“维持速率跨步下一次估计值”（MR(csne)）。历史病例采用表格方案进行静脉胰岛素输注治疗，该方案根据列变化规则估算MR。将历史稳定期的平均IR（MR(true)）（对MR生物学值的一种估计）与稳定期之前高血糖迭代期间的MR(csne)进行比较。将假设计算的依赖于MR(csne)的IR(next)与历史上分配的IR(next)进行比较。从数学上推导出一种扩展的算法理论。提出了计算机化的实用建议。
在30个稳定期各自确定的MR(true)与紧接在前的高血糖迭代期间的MR(csne)有所不同，中位数及其四分位间距分别为2.7（1.2 - 3.7）和3.2（1.5 - 4.6）单位/小时。然而，这些MR估计值具有很强的相关性（R² = 0.88）。在941个高血糖时间点，历史上分配的IR(next)与假设计算的依赖于MR(csne)的IR(next)有所不同，中位数及其四分位间距分别为4.0（3.0 - 6.0）和4.6（3.0 - 6.8）单位/小时，但这些配对值再次具有相关性（R² = 0.87）。本文描述了一种用于静脉胰岛素输注的可编程算法。该算法的基本方程给出了IR、生物学参数MR以及表示BG瞬时变化速率的两个变量之间的关系，其中一个变量在任何给定时间点必须为零，另一个为正、负或零，即低于目标值时的BG变化速率（上升速率）和高于目标值时的BG变化速率（下降速率）。除了用户可定义的参数外，还描述了三个在自然界中可发现的特殊算法参数：非低血糖期间血糖的最大自发上升速率、外源性介导的每单位胰岛素剂量对应的葡萄糖量以及给定患者时间点的MR。用户可分配的参数将有助于适应不同的患者群体。
描述了一种在达到正常血糖之前估算MR并计算依赖于MR的IR(next)值的算法。设计特点考虑了血糖变异性，提高了低血糖方面的安全性，并定义了一种根据患者情况允许设定不同血糖目标的方法。
