Peak and Range of Blood Glucose Are Also Associated With Icu Mortality

Shaw²,

Lotz³

et al. 2007

CDD

Self Cite

118

Section: Virtual Trial Protocol Comparisonmentioning

confidence: 99%

Model-Based Insulin and Nutrition Administration for Tight Glycaemic Control in Critical Care

Shaw²,

Lotz³

et al. 2007

CDD

Self Cite

118

“…Results show that fewer, less frequent measurements often result in greater glycaemic variation in critical care and poorer outcome [80,85,86].…”

Section: Sensors and Signal Processingmentioning

confidence: 99%

“…However, it provides no further information on glucose excursions or tightness of control. An important consideration is the use of a trapezoidal mean to obtain the proper mean value if the sampling period is irregular [85,86].…”

Section: Performance Metricsmentioning

confidence: 99%

Model-based glycaemic control in critical care—A review of the state of the possible

Biomedical Signal Processing and Control

Wong

Lotz

et al. 2006

109

Hyperglycaemia is prevalent in critical care, as patients experience stress-induced hyperglycaemia, even with no history of diabetes. Hyperglycaemia in critical care is not largely benign, as once thought, and it has a deleterious effect on outcome. Recent studies have shown that tight glucose regulation to average levels from 6.1-7.75 mmol/L can reduce mortality 25-43%, while also significantly reducing other negative clinical outcomes.However, clinical results are highly variable and there is little agreement on what levels of performance can be achieved and how to achieve them. A typical clinical solution is to use ad-hoc protocols based primarily on experience, where large amounts of insulin, up to 50U/hr, are titrated against glucose measurements variably taken every 1-4 hours. When combined with the unpredictable and sudden metabolic changes that characterise this aspect of critical illness and/or clinical changes in nutritional support, this approach results in highly variable blood glucose levels. The overall result is sustained periods of hyper-or hypo-glycaemia, characterised by oscillations between these states, which can adversely affect clinical outcomes and mortality. The situation is exacerbated by exogenous nutritional support regimes with high dextrose content.Hence, there is an emerging, strong need for the more rigorous analysis and methods that model-based control methods bring to this type of problem. This paper reviews the state of the clinical and model-based control systems approach to the problem of managing hyperglycaemia in critical care, emphasising emerging methods and results. The overall goal is to present the fundamental problem and associated science and technologies involved. Thus, it is less a discussion of specific advantageous approaches than a presentation of the different factors that impact the problem and the different approaches taken to address them in the limited clinical engineering research done to date. These discussions are presented in the context of current and emerging clinical studies, both model-based and empirical protocol driven. Analogies to the Type 1 diabetes mellitus control problem are also noted where relevant and significant. Hence, it is more overview than specific analysis, where the overall conclusion is that there are many opportunities and unanswered questions remaining on which model-based control research can have significant clinical impact. Declaration of originality and author(s) agreement

“…Several other potential measures exist already, such as MAGE (mean average glycemic excursion) 75 or the peak/range of blood glucose. 76 However, both of these metrics require all glucose values to be reported, rather than just morning averages as in the van den Berghe et al studies or the study by Thomas et al…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the MAGE metric is effectively equivalent to the standard deviation metric, and the peak and range of blood glucose are highly correlated to the mean glucose value, 76 adding no additional information to the metric used. In addition, reporting multiple time in band percentages can compactly illustrate both the average glucose levels and fundamental distribution of glycemic values.…”

Section: Discussionmentioning

confidence: 99%

Overview of Glycemic Control in Critical Care: Relating Performance and Clinical Results

Hann

Wong

et al. 2007

J Diabetes Sci Technol

Self Cite

Background:Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and produce highly variable results. Model-based methods can provide tight, patient specific control, while addressing practical clinical difficulties and dynamic patient evolution. However, tight control remains elusive as there is not enough understanding of the relationship between control performance and clinical outcome. Methods:The general problem and performance criteria are defined. The clinical studies performed to date using both ad-hoc titration and model-based methods are reviewed. Studies reporting mortality outcome are analysed in terms of standardized mortality ratio (SMR) and a 95 th percentile (±2s) standard error (SE 95% ) to enable better comparison across cohorts. Results:Model-based control trials lower blood glucose into a 72-110 mg/dL band within 10 hours, have target accuracy over 90%, produce fewer hypoglycemic episodes, and require no additional clinical intervention. Plotting SMR versus SE 95% shows potentially high correlation (r=0.84) between ICU mortality and tightness of control. Summary:Model-based methods provide tighter, more adaptable one method fits all solutions, using methods that enable patientspecific modeling and control. Correlation between tightness of control and clinical outcome suggests that performance metrics, such as time in a relevant glycemic band, may provide better guidelines. Overall, compared to the current one size fits all sliding scale and ad-hoc regimens, patient-specific pharmacodynamic and pharmacokinetic model-based, or one method fits all control, utilizing computational and emerging sensor technologies, offers improved treatment and better potential outcomes when treating hyperglycemia in the highly dynamic critically ill patient. J Diabetes Sci Technol 2007; 1:82-91 Journal of Diabetes Science and Technology