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

Authors
  • Yahia, Anane1
  • Szlávecz, Ákos1
  • Knopp, Jennifer L2
  • Norfiza Abdul Razak, Normy3
  • Abu Samah, Asma4
  • Shaw, Geoff2
  • Chase, J Geoffrey2
  • Benyo, Balazs1
  • 1 Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary. , (Hungary)
  • 2 Mechanical Engineering, Centre of Bio-Engineering, University of Canterbury, Christchurch, NZ.
  • 3 Department of Electronics and Communication Engineering, UNITEN, Malaysia. , (Malaysia)
  • 4 Institute of Energy Infrastructure, Universiti Tenaga Nasional, Jalan Ikram-UNITEN, Kajang, Selangor, Malaysia. , (Malaysia)
Type
Published Article
Journal
Journal of Diabetes Science and Technology
Publisher
SAGE Publications
Publication Date
Sep 01, 2022
Volume
16
Issue
5
Pages
1208–1219
Identifiers
DOI: 10.1177/19322968211018260
PMID: 34078114
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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. 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. 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 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. 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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