scholarly journals Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

2011 ◽  
Vol 102 (2) ◽  
pp. 156-171 ◽  
Author(s):  
J. Geoffrey Chase ◽  
Aaron J. Le Compte ◽  
Fatanah Suhaimi ◽  
Geoffrey M. Shaw ◽  
Adrienne Lynn ◽  
...  
Keyword(s):  
Critical Care ◽  
Insulin Sensitivity ◽  
Glycemic Control ◽  
Tight Glycemic Control ◽  
Model Based ◽  
Leading Role ◽  
Model Based Analysis

scholarly journals Insulin Sensitivity, Its Variability and Glycemic Outcome: A model-based analysis of the difficulty in achieving tight glycemic control in critical care

2011 ◽  
Vol 44 (1) ◽  
pp. 1745-1750
Author(s):  
J. Geoffrey Chase ◽  
Aaron J. Le Compte ◽  
Jean-Charles Preiser ◽  
Christopher G. Pretty ◽  
Katherine T. Moorhead ◽  
...  
Keyword(s):  
Critical Care ◽  
Insulin Sensitivity ◽  
Glycemic Control ◽  
Tight Glycemic Control ◽  
Model Based ◽  
Model Based Analysis

scholarly journals Tight Glycemic Control - The leading role of insulin sensitivity in determining efficacy and thus outcome

2009 ◽  
Vol 42 (12) ◽  
pp. 1-6
Author(s):  
J. Geoffrey Chase ◽  
Aaron J. Le Compte ◽  
Geoffrey M. Shaw ◽  
Jessica Lin ◽  
Christopher G. Pretty ◽  
...  
Keyword(s):  
Insulin Sensitivity ◽  
Glycemic Control ◽  
Tight Glycemic Control ◽  
Leading Role

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 Interface Design and Human Factors Considerations for Model-Based Tight Glycemic Control in Critical Care

2012 ◽  
Vol 6 (1) ◽  
pp. 125-134 ◽  
Author(s):  
Logan Ward ◽  
James Steel ◽  
Aaron Le Compte ◽  
Alicia Evans ◽  
Chia-Siong Tan ◽  
...  
Keyword(s):  
Critical Care ◽  
Glycemic Control ◽  
Human Factors ◽  
Interface Design ◽  
Tight Glycemic Control ◽  
Model Based

scholarly journals Data Entry Errors and Design for Model-Based Tight Glycemic Control in Critical Care

2012 ◽  
Vol 6 (1) ◽  
pp. 135-143 ◽  
Author(s):  
Logan Ward ◽  
James Steel ◽  
Aaron Le Compte ◽  
Alicia Evans ◽  
Chia-Siong Tan ◽  
...  
Keyword(s):  
Critical Care ◽  
Glycemic Control ◽  
Data Entry ◽  
Tight Glycemic Control ◽  
Model Based

Model-Based Analysis of Friction-Induced Sub-Synchronous Whirl for a Rotor Contacting With Clearance Bearings Under Axial Load

2015 ◽  
Author(s):  
Matthew O. T. Cole ◽  
Lawrence Hawkins
Keyword(s):  
Axial Load ◽  
Coulomb Friction ◽  
Friction Forces ◽  
Root Finding ◽  
Model Based ◽  
Simple Physical Interpretation ◽  
Level Of Agreement ◽  
Model Based Analysis

For rotors supported by active magnetic bearings (AMBs), clearance bearings are commonly used to provide backup support under loss of AMB functionality. Test data from real machines shows that rotor vibration during touchdown on backup bearings may involve steady forward whirling at a sub-synchronous frequency. This excitation is believed to be due to friction forces transmitted between the rotor and a bearing end-face under axial load. This paper proposes a new analytical approach to model and predict such friction-driven forward whirl behaviors. A set of constraint equations are derived that relate a circular whirl motion of arbitrary orbital speed to the frequency response functions of the rotor-housing structure. This model is coupled with an evaluation of Coulomb friction associated with slip between the rotor and the supporting end-face of a thrust bearing. The resulting equations can be used to compute a set of possible whirl motions via a root-finding procedure. A case study is undertaken for a 140 kW energy storage flywheel. Model-based predictions are compared with measured data from spin-down tests and show a good level of agreement. The study confirms the role of friction-related forces in driving forward-whirl response behaviors. It also highlights the key role of housing and machine support characteristics in response behavior. This influence is shown to be complex and not open to simple physical interpretation. Therefore, the proposed analytical method is seen as a useful tool to investigate this influence while avoiding the need for time consuming numerical simulations.

scholarly journals Role of Sodium-Glucose Cotransporter 2 (SGLT 2) Inhibitors in the Treatment of Type 2 Diabetes

2011 ◽  
Vol 32 (4) ◽  
pp. 515-531 ◽  
Author(s):  
Muhammad A. Abdul-Ghani ◽  
Luke Norton ◽  
Ralph A. DeFronzo
Keyword(s):  
Type 2 Diabetes ◽  
Glycemic Control ◽  
Microvascular Complications ◽  
Tight Glycemic Control ◽  
Β Cell ◽  
Oral Antidiabetic Agents ◽  
Oral Antidiabetic ◽  
Sodium Glucose Cotransporter

Hyperglycemia plays an important role in the pathogenesis of type 2 diabetes mellitus, i.e., glucotoxicity, and it also is the major risk factor for microvascular complications. Thus, effective glycemic control will not only reduce the incidence of microvascular complications but also correct some of the metabolic abnormalities that contribute to the progression of the disease. Achieving durable tight glycemic control is challenging because of progressive β-cell failure and is hampered by increased frequency of side effects, e.g., hypoglycemia and weight gain. Most recently, inhibitors of the renal sodium-glucose cotransporter have been developed to produce glucosuria and reduce the plasma glucose concentration. These oral antidiabetic agents have the potential to improve glycemic control while avoiding hypoglycemia, to correct the glucotoxicity, and to promote weight loss. In this review, we will summarize the available data concerning the mechanism of action, efficacy, and safety of this novel antidiabetic therapeutic approach.

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