Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance
| Autor: | J. Geoffrey Chase, Geoff Shaw, Jennifer L. Knopp, Normy Norfiza Abdul Razak, Anane Yahia, Ákos Szlávecz, Balázs Benyó, Asma Abu Samah |
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| Rok vydání: | 2021 |
| Předmět: |
Blood Glucose
medicine.medical_specialty Icu patients Critical Care Endocrinology Diabetes and Metabolism Critical Illness Biomedical Engineering 030209 endocrinology & metabolism Bioengineering Endogeny law.invention Glucose production 03 medical and health sciences 0302 clinical medicine Insulin resistance law Internal medicine Internal Medicine medicine Hyperinsulinemia Humans Insulin Critically ill business.industry Insulin sensitivity 030208 emergency & critical care medicine Original Articles medicine.disease Intensive care unit Intensive Care Units Endocrinology Glucose Hyperglycemia Insulin Resistance business |
| Zdroj: | J Diabetes Sci Technol |
| ISSN: | 1932-2968 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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