A controlled study of the effectiveness of an adaptive closed-loop algorithm to minimize corticosteroid-induced stress hyperglycemia in type 1 diabetes

Abstract

To be effective in type 1 diabetes, algorithms must be able to limit hyperglycemic excursions resulting from medical and emotional stress. We tested an algorithm that estimates insulin sensitivity at regular intervals and continually adjusts gain factors of a fading memory proportional-derivative (FMPD) algorithm. In order to assess whether the algorithm could appropriately adapt and limit the degree of hyperglycemia, we administered oral hydrocortisone repeatedly to create insulin resistance. We compared this indirect adaptive proportional-derivative (APD) algorithm to the FMPD algorithm, which used fixed gain parameters. Each subject with type 1 diabetes (n = 14) was studied on two occasions, each for 33 h. The APD algorithm consistently identified a fall in insulin sensitivity after hydrocortisone. The gain factors and insulin infusion rates were appropriately increased, leading to satisfactory glycemic control after adaptation (premeal glucose on day 2, 148 ± 6 mg/dl). After sufficient time was allowed for adaptation, the late postprandial glucose increment was significantly lower than when measured shortly after the onset of the steroid effect. In addition, during the controlled comparison, glycemia was significantly lower with the APD algorithm than with the FMPD algorithm. No increase in hypoglycemic frequency was found in the APD-only arm. An afferent system of duplicate amperometric sensors demonstrated a high degree of accuracy; the mean absolute relative difference of the sensor used to control the algorithm was 9.6 ± 0.5%. We conclude that an adaptive algorithm that frequently estimates insulin sensitivity and adjusts gain factors is capable of minimizing corticosteroid-induced stress hyperglycemia.

© 2011 Diabetes Technology Society.

Figures

Figure 1

Control diagrams of the FMPD algorithm and the indirect APD algorithm showing major system elements. The FMPD algorithm is preserved in the body of the APD algorithm.

Figure 2

In creating the APD algorithm, estimates of insulin sensitivity obtained every 30 min lead to a TDIR, as shown. The change in TDIR leads to changes in the gain settings and in basal rate provided through the FMPD controller.

Figure 3

A simulation, using an early version of the APD algorithm. This simulation used the Kovatchev/Cobelli simulator of carbohydrate metabolism as described in the text. It can be seen that this version of the algorithm, although it prevented hypoglycemia, led to substantial hyperglycemia after meals.

Figure 4

A graph of blood glucose and IIR during the first 13 h in the controlled arm of the study, which compares the FMPD and the APD algorithms. Glycemic control is significantly tighter in APD-only arm, as described in the text. In addition, insulin sensitivity, which was measured in the latter algorithm, is shown here; there is a marked decline after administration of hydrocortisone. Meals and hydrocortisone (HC) are indicated by different arrows, as indicated.

Figure 5

This graph is a comparison (in the APD-only group) of the postprandial increments in blood glucose after lunch on day one vs day two. At hour 3 and hour 4, there was a lower glucose increment on day two, after the system had more time to adapt to insulin resistance. NS = not significant.

Figure 6

A comparison of blood glucose and IIR over the 33 h experiments in both arms. For the first 780 min, one arm used the FMPD algorithm and the other arm used the APD algorithm. For the final 20 h, both arms used the APD algorithm.

Figure 7

An example in one subject of blood glucose values, sensor glucose values, IIR, and glucagon delivery. In general, although two sensor switches were needed, the sensor values reflected the blood glucose values closely. Small doses of glucagon were occasionally required. Meals are indicated by arrows.

Figure A1

In this diagram, Q1 and Q2 represent the measurable and unmeasurable glucose compartments, Qi1a, Qi1b, and Qi2 represent subcutaneous insulin compartments where Qi1a and Qi2 model a slow pathway of insulin absorption and Qi1b models a fast pathway. Qi3 and compartment I are interchangeable plasma insulin compartments, while the insulin action compartments shown represent effects on glucose distribution to (x1) and utilization (x2) by insulin-sensitive tissues, as well as suppression of EGP (x3). NIMGU = noninsulin-mediated glucose uptake.