Estimating Increased EGP During Stress Response in Critically Ill Patients.

EGP critical illness endogenous glucose production glycemic control hyperglycemia insulin resistance insulin sensitivity physiological stress

Journal

Journal of diabetes science and technology
ISSN: 1932-2968
Titre abrégé: J Diabetes Sci Technol
Pays: United States
ID NLM: 101306166

Informations de publication

Date de publication:
07 2021
Historique:
pubmed: 2 6 2020
medline: 29 10 2021
entrez: 2 6 2020
Statut: ppublish

Résumé

Stress-induced hyperglycemia is frequently experienced by critically ill patients and the use of glycemic control (GC) has been shown to improve patient outcomes. For model-based approaches to GC, it is important to understand and quantify model parameter assumptions. This study explores endogenous glucose production (EGP) and the use of a population-based parameter value in the intensive care unit context. Hourly insulin sensitivity (SI) was fit to clinical data from 145 patients on the Specialized Relative Insulin and Nutrition Titration GC protocol for at least 24 hours. Constraint of SI at a lower bound was used to explore likely EGP variability due to stress response. Minimum EGP was estimated during times when the model SI was constrained, and time and duration of events were examined. Constrained events occur for 1.6% of patient hours. About 70% of constrained events occur in the first 12 hours and most events (~80%) occur when there is no exogenous nutrition given. Enhanced EGP values ranged from 1.16 mmol/min (current population value) to 2.75 mmol/min, with most being below 1.5 mmol/min (21% increase). The frequency of constrained events is low and the current population value of 1.16 mmol/min is sufficient for more than 98% of patient hours, however, some patients experience significantly raised EGP probably due to an extreme stress response early in patient stay.

Sections du résumé

BACKGROUND
Stress-induced hyperglycemia is frequently experienced by critically ill patients and the use of glycemic control (GC) has been shown to improve patient outcomes. For model-based approaches to GC, it is important to understand and quantify model parameter assumptions. This study explores endogenous glucose production (EGP) and the use of a population-based parameter value in the intensive care unit context.
METHOD
Hourly insulin sensitivity (SI) was fit to clinical data from 145 patients on the Specialized Relative Insulin and Nutrition Titration GC protocol for at least 24 hours. Constraint of SI at a lower bound was used to explore likely EGP variability due to stress response. Minimum EGP was estimated during times when the model SI was constrained, and time and duration of events were examined.
RESULTS
Constrained events occur for 1.6% of patient hours. About 70% of constrained events occur in the first 12 hours and most events (~80%) occur when there is no exogenous nutrition given. Enhanced EGP values ranged from 1.16 mmol/min (current population value) to 2.75 mmol/min, with most being below 1.5 mmol/min (21% increase).
CONCLUSION
The frequency of constrained events is low and the current population value of 1.16 mmol/min is sufficient for more than 98% of patient hours, however, some patients experience significantly raised EGP probably due to an extreme stress response early in patient stay.

Identifiants

pubmed: 32476457
doi: 10.1177/1932296820922842
pmc: PMC8258514
doi:

Substances chimiques

Blood Glucose 0
Insulin 0
Glucose IY9XDZ35W2

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

856-864

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Auteurs

Jennifer J Ormsbee (JJ)

Department of Mechanical Engineering, Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand.

Jennifer L Knopp (JL)

Department of Mechanical Engineering, Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand.

J Geoffrey Chase (JG)

Department of Mechanical Engineering, Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand.

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Classifications MeSH