Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance
glucose; glucose; insulin; adult; Article; blood glucose monitoring; cohort analysis; critically ill patient; disease simulation; female; finite element analysis; gluconeogenesis; glucose blood level; glycemic control; human; Hungary; hyperglycemia; hyperinsulinemia; insulin resistance; insulin sens...
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my.uniten.dspace-267822023-05-29T17:36:40Z Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance Yahia A. Szl�vecz �. Knopp J.L. Norfiza Abdul Razak N. Abu Samah A. Shaw G. Chase J.G. Benyo B. 57224210004 6505809017 57202741591 37059587300 56719596600 7401773560 35570524900 15757116300 glucose; glucose; insulin; adult; Article; blood glucose monitoring; cohort analysis; critically ill patient; disease simulation; female; finite element analysis; gluconeogenesis; glucose blood level; glycemic control; human; Hungary; hyperglycemia; hyperinsulinemia; insulin resistance; insulin sensitivity; intensive care unit; major clinical study; Malaysia; male; New Zealand; physiological stress; simulation; critical illness; hyperglycemia; intensive care; intensive care unit; procedures; Blood Glucose; Critical Care; Critical Illness; Glucose; Humans; Hyperglycemia; Insulin; Insulin Resistance; Intensive Care Units 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. � 2021 Diabetes Technology Society. Final 2023-05-29T09:36:40Z 2023-05-29T09:36:40Z 2022 Article 10.1177/19322968211018260 2-s2.0-85107261519 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107261519&doi=10.1177%2f19322968211018260&partnerID=40&md5=209255ccc8008684ce5b6e431d53b017 https://irepository.uniten.edu.my/handle/123456789/26782 16 5 1208 1219 All Open Access, Bronze, Green SAGE Publications Inc. Scopus |
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glucose; glucose; insulin; adult; Article; blood glucose monitoring; cohort analysis; critically ill patient; disease simulation; female; finite element analysis; gluconeogenesis; glucose blood level; glycemic control; human; Hungary; hyperglycemia; hyperinsulinemia; insulin resistance; insulin sensitivity; intensive care unit; major clinical study; Malaysia; male; New Zealand; physiological stress; simulation; critical illness; hyperglycemia; intensive care; intensive care unit; procedures; Blood Glucose; Critical Care; Critical Illness; Glucose; Humans; Hyperglycemia; Insulin; Insulin Resistance; Intensive Care Units |
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57224210004 Yahia A. Szl�vecz �. Knopp J.L. Norfiza Abdul Razak N. Abu Samah A. Shaw G. Chase J.G. Benyo B. |
| format |
Article |
| author |
Yahia A. Szl�vecz �. Knopp J.L. Norfiza Abdul Razak N. Abu Samah A. Shaw G. Chase J.G. Benyo B. |
| spellingShingle |
Yahia A. Szl�vecz �. Knopp J.L. Norfiza Abdul Razak N. Abu Samah A. Shaw G. Chase J.G. Benyo B. Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance |
| author_sort |
Yahia A. |
| title |
Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance |
| title_short |
Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance |
| title_full |
Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance |
| title_fullStr |
Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance |
| title_full_unstemmed |
Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance |
| title_sort |
estimating enhanced endogenous glucose production in intensive care unit patients with severe insulin resistance |
| publisher |
SAGE Publications Inc. |
| publishDate |
2023 |
| _version_ |
1806427788453871616 |