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

Abstract

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

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

“…This result also requires a level of adaptability from the TGC protocol that is not available in most published cases [66,[76][77][78].…”

Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care

“…Over 165 patients and 23,000 patient hours, it achieved an average blood glucose level of 5.8±1.2mmol/L with 61% of the time in the 4 -6.1mmol/L band [4]. This is the only approach adaptive to insulin sensitivity variation, as reflected in the hour to hour insulin-nutrition response to changing blood glucose levels.…”

“…Critically ill patients often experience stress-induced hyperglycemia and high levels of insulin resistance [1][2][3][4]. The increased counter-regulatory hormone and cytokine response stimulates endogenous glucose production and increases effective insulin resistance.…”

“…Therefore, understanding and modelling the variability in insulin sensitivity can assist clinical control intervention and minimise associated risk. 4 Currently, no intensive insulin therapy protocol offers the glycemic response likelihood to an intervention. Clinicians are thus partly blind in controlling this highly dynamic system.…”

A fast generalizable solution method for glucose control algorithms

“…This trial also had some of the tightest glucose control reported as compared to other studies e.g. [2,18,[23][24][25][26][27][28][29], indicating the potential of computational models to impact this field.…”