scholarly journals Glycemic Control for Critically Ill Patients with Online Identification of Insulin Sensitivity

2020 ◽  
Vol 9 (0) ◽  
pp. 43-52 ◽  
Author(s):  
Sha Wu ◽  
Eiko Furutani ◽  
Tomonori Sugawara ◽  
Takehiko Asaga ◽  
Gotaro Shirakami
Keyword(s):  
Insulin Sensitivity ◽  
Glycemic Control ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Online Identification

scholarly journals Model-Based Insulin Sensitivity for Early Diagnosis of Sepsis in Critical Care

2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Wan Fadzlina Wan Muhd Shukeri ◽  
Azrina Md. Ralib ◽  
Ummu Khultum Jamaludin ◽  
Mohd Basri Mat-Nor
Keyword(s):  
Critical Care ◽  
Insulin Sensitivity ◽  
Glycemic Control ◽  
Real Time ◽  
Critically Ill ◽  
Sensitivity And Specificity ◽  
Critically Ill Patients ◽  
Predictive Value ◽  
Sepsis Group ◽  
Model Based

Introduction: Currently, it is almost impossible to diagnose a patient at the onset of sepsis due to the lack of real-time metrics with high sensitivity and specificity. The purpose of the present study is to determine the diagnostic value of model-based insulin sensitivity (SI) as a new sepsis biomarker in critically ill patients, and compare its performance to classical inflammatory parameters. Materials and method: We monitored hourly SI levels in septic (n=19) and non-septic (n=19) critically ill patients in a 24-hour follow-up study. Patients with type I or type II diabetes mellitus were excluded. SI levels were calculated by a validated glycemic control software, STAR TGC (Stochastic TARgeted Tight Glycemic Controller) (Christchurch, NZ). STAR TGC uses a physiological glucose-insulin system model coupled with stochastic models that capture SI variability in real time. Results: The median SI levels were lower in the sepsis group than in the non-sepsis group (1.9 x 10-4 L/mU/min vs 3.7 x 10-4 L/mU/min, P <0.0001). The areas under the receiver operating characteristic curve (AUROC) of the model-based SI for distinguishing non-sepsis from sepsis was 0.911, superior to white cells count (AUROC 0.611) and temperature (AUROC 0.618). The optimal cut-off value of the test was 2.9 x 10-4 L/mU/min. At this cut-off value, the sensitivity and specificity was 88.9% and 84.2%, respectively. The positive predictive value was 84.2%, while the negative predictive value was 88.9%. Conclusion: The early and relevant decrease of SI in sepsis suggests that it might be a promising novel biomarker of sepsis in critical care. Low SI is diagnostic of sepsis, while high SI rules out sepsis, and these may be determined non-invasively in real-time from glycemic control protocol data.

scholarly journals Second pilot trials of the STAR-Liege protocol for tight glycemic control in critically ill patients

2012 ◽  
Vol 11 (1) ◽  
pp. 58 ◽  
Author(s):  
Sophie Penning ◽  
Aaron J Le Compte ◽  
Paul Massion ◽  
Katherine T Moorhead ◽  
Christopher G Pretty ◽  
...  
Keyword(s):  
Glycemic Control ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Tight Glycemic Control ◽  
Pilot Trials

scholarly journals Insulin sensitivity and sepsis score: A correlation between model-based metric and sepsis scoring system in critically ill patients

2017 ◽  
Vol 32 ◽  
pp. 112-123
Author(s):  
Fatanah M. Suhaimi ◽  
J. Geoffrey Chase ◽  
Christopher G. Pretty ◽  
Geoffrey M. Shaw ◽  
Normy N. Razak ◽  
...  
Keyword(s):  
Insulin Sensitivity ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Scoring System ◽  
Model Based ◽  
Sepsis Score

Association Effects Of Comorbid Versus Uncomorbid Diabetes And Blood Glucose Concentrations On Short-term Outcomes In Patients With Critical Illness: A Real-World Study Based On Propensity Score Analyses

2020 ◽  
Author(s):  
Shan Lin ◽  
Shanhui Ge ◽  
Wanmei He ◽  
Mian Zeng
Keyword(s):  
Critical Illness ◽  
Glycemic Control ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Cox Model ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Optimal Range ◽  
Short Term ◽  
Inverse Probability

Abstract Background: The effects of combined diabetes and glycemic control strategies on the short-term prognosis in patients with a critical illness are currently ambiguous. The objectives of our study were to determine whether comorbid diabetes affects short-term prognosis and the optimal range of glycemic control in critically ill patients.Methods: We performed this study with the critical care database. The primary outcomes were 28-day mortality in critically ill patients with comorbid diabetes and the optimal range of glycemic control. Association of comorbid diabetes with 28-day mortality was assessed by multivariable Cox regression model with inverse probability weighting. Smooth curves were applied to fit the association for glucose and 28-day mortality.Results: Of the 33,680 patients enrolled in the study, 8,701 (25.83%) had diabetic comorbidity. Cox model with inverse probability weighting showed that the 28-day mortality rate was reduced by 29% (HR=0.71, 95% CI 0.67-0.76) in the group with diabetes in comparison to the group without diabetes. The E value of 2.17 indicated robustness to unmeasured confounders. The effect of the association between comorbid diabetes and 28-day mortality was generally in line for all subgroup variables, significant interactions were observed for glucose on first day, admission type, and use of insulin or not (Interaction P <0.05). A V-shaped relationship was observed between glucose concentrations and 28-day mortality in patients without diabetes, with the lowest 28-day mortality corresponding to the glucose level was 101.75 mg/dl (95% CI 94.64-105.80 mg/dl); whereas in patients with comorbid diabetes, the effect of glucose concentration on 28-day mortality was structurally softer than in those with uncomorbid diabetes. Lastly, of all patients, hyperglycemia had the greatest deleterious effect on patients admitted to CSRU.Conclusions: Our study further confirmed the protective effect of comorbid diabetes on the short-term prognosis of critically ill patients, resulting in an approximately 29% reduction in 28-day mortality. Besides, we also demonstrated the personalized glycemic control strategy for critically ill patients. Lastly, clinicians should be aware of the occurrence and the prompt management of hyperglycemia in critically ill patients admitted to the CSRU.

The Long and Winding Road to Personalized Glycemic Control in the Intensive Care Unit

2019 ◽  
Vol 40 (05) ◽  
pp. 571-579
Author(s):  
Mayanka Tickoo
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Glycemic Control ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Physiological Stress ◽  
Close Monitoring ◽  
Physiological Stress Response ◽  
Downstream Effects ◽  
Personalized Approach

AbstractIn the critically ill adult, dysglycemia is a marker of disease severity and is associated with worse clinical outcomes. Close monitoring of glucose and use of insulin in critically ill patients have been done for more than 2 decades, but the appropriate target glycemic range in critically ill patients remains controversial. Physiological stress response, levels of inflammatory cytokines, nutritional intake, and level of mobility affect glycemic control, and a more personalized approach to patients with dysglycemia is warranted in critically ill intensive care unit (ICU) patients. We discuss the pathophysiology and downstream effects of altered glycemic response in critical illness, management of glycemic control in the ICU, and future strategies toward personalization of critical care glycemic management.

Sign in / Sign up

Export Citation Format

Share Document