Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance

Abstract

Background: Critically ill ICU patients frequently experience acute insulin resistance and increased endogenous glucose production, manifesting as stress-induced hyperglycemia and hyperinsulinemia. STAR (Stochastic TARgeted) is a glycemic control protocol, which directly manages inter- and intra- patient variability using model-based insulin sensitivity (SI). The model behind STAR assumes a population constant for endogenous glucose production (EGP), which is not otherwise identifiable.

Objective: This study analyses the effect of estimating EGP for ICU patients with very low SI (severe insulin resistance) and its impact on identified, model-based insulin sensitivity identification, modeling accuracy, and model-based glycemic clinical control.

Methods: Using clinical data from 717 STAR patients in 3 independent cohorts (Hungary, New Zealand, and Malaysia), insulin sensitivity, time of insulin resistance, and EGP values are analyzed. A method is presented to estimate EGP in the presence of non-physiologically low SI. Performance is assessed via model accuracy.

Results: Results show 22%-62% of patients experience 1+ episodes of severe insulin resistance, representing 0.87%-9.00% of hours. Episodes primarily occur in the first 24 h, matching clinical expectations. The Malaysian cohort is most affected. In this subset of hours, constant model-based EGP values can bias identified SI and increase blood glucose (BG) fitting error. Using the EGP estimation method presented in these constrained hours significantly reduced BG fitting errors.

Conclusions: Patients early in ICU stay may have significantly increased EGP. Increasing modeled EGP in model-based glycemic control can improve control accuracy in these hours. The results provide new insight into the frequency and level of significantly increased EGP in critical illness.

Keywords: EGP; STAR; blood glucose; endogenous glucose production; glycemic control; insulin sensitivity; model-based.

Figure 1.

Graphic representation of the insulin/glucose system modeled by ICING.

Figure 2.

Example of poor BG fitting (top figure) with SI time function (bottom figure). The red curve is the simulated BG using the fixed EGP; the BG measurements are shown by stars (*).

Figure 3.

Flowchart of the implementation of the new EGP estimation method embedded into the SI identification process. Treatment calculation includes the selection of the optimal insulin and nutrition intake to be given to the patient according to the original STAR protocol.

Figure 4.

Distribution of identified SI for each cohort using the standard, fixed value for EPG of 1.16 mmol/min. On the left, all patients (N = 717), and on the right, only patients with SI constrained to the minimum value for at least 1 hour (N = 330). The proportion of values at the lower limit of SI = 1e-7 L/mU/min in Table 3 match the starting points in this figure (left). Note that the x-axis is a log scale.

Figure 5.

Distribution function of SI values with fixed EGP vs. Estimated EGP for the: (left) Malaysian; (middle) New Zealand; and (right) Hungarian cohorts. Note that the x-axis is a log scale.

Figure 6.

Distribution of the estimated EGP for the 3 cohorts for the hours where it was constrained and the proposed method applied in Table 3. Malaysian (left); New Zealand (middle); and Hungarian (right) cohorts. X is the EGP estimated for each low minimum SI hours, Y is the number of cases when it was changed (low minimum SI hours).

Figure 7.

Occurrence in time of low minimum SI for Hungarian, Malaysian and New Zealand cohort (one histogram bin corresponds to 1 hour) Malaysian (left); New Zealand (middle); and Hungarian (right) cohorts.

Figure 8.

Example for a BG trajectory simulated by using the original EGP/SI values (shown as red) and the identified EGP/SI values (shown as blue) of the patient shown in Figure 2.