Estimating enhanced endogenous glucose production in intensive care unit patients with severe insulin resistance
A Yahia, Á Szlávecz, JL Knopp… - Journal of Diabetes …, 2022 - journals.sagepub.com
Journal of Diabetes Science and Technology, 2022•journals.sagepub.com
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 …
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 …
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.
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