Experience with the Continuous Glucose Monitoring System® in a Medical Intensive Care Unit

Goldberg, Philip A.; Siegel, Mark D.; Russell, Raymond R.; Sherwin, Robert S.; Halickman, Joshua I; Cooper, Dawn; Dziura, James; Inzucchi, Silvio E.

doi:10.1089/152091504774198034

Cited by 151 publications

(139 citation statements)

References 19 publications

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“…The MiniMed continuous glucose monitoring system (CGMS) is a minimally invasive subcutaneous glucose monitor currently approved for the investigation of glycemic profiles in diabetic outpatients (9). Recent data have shown that CGM can be successfully undertaken in the acute hospital setting (10,11). Our pilot data suggested that quantification of persistent PSH with the CGMS was a more robust indicator of infarct growth and clinical outcome than isolated glucose estimates in hyperglycemic acute stroke patients (10).…”

mentioning

confidence: 89%

Frequency and Temporal Profile of Poststroke Hyperglycemia Using Continuous Glucose Monitoring

Allport

Baird²,

Butcher³

et al. 2006

Diabetes Care

112

117

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OBJECTIVE -Poststroke hyperglycemia (PSH) is common and has adverse effects on outcome. In this observational study, we aimed to describe the frequency and temporal profile of PSH using a continuous glucose monitoring system (CGMS) in patients with and without diabetes.RESEARCH DESIGN AND METHODS -Fifty-nine patients with acute hemispheric ischemic stroke were prospectively studied with the CGMS, regardless of medication, admission plasma glucose value, and diabetes status. The CGMS records interstitial glucose every 5 min for 72 h.RESULTS -On admission, 36% of patients had preexisting diabetes. At the earliest analyzed time point of 8 h from stroke onset, 50% of nondiabetic subjects and 100% of diabetic patients were hyperglycemic (Ն7 mmol/l). This early-phase hyperglycemia was followed by a decrease in glucose 14 -16 h poststroke when only 11% of nondiabetic and 27% of diabetic patients were hyperglycemic. A late hyperglycemic phase 48 -88 h poststroke was observed in 27% of nondiabetic and 78% of diabetic patients. Thirty-four percent of nondiabetic and 86% of diabetic patients were hyperglycemic for at least a quarter of the monitoring period. Multivariate regression analysis demonstrated that diabetes, insular cortical ischemia, and increasing age independently predicted higher glucose values.CONCLUSIONS -Poststroke hyperglycemia is common and prolonged despite treatment based on current guidelines. There are early and late hyperglycemic phases in nondiabetic as well as diabetic patients. Treatment protocols with frequent glucose measurement and intensive glucose-lowering therapy for a minimum of 72 h poststroke need to be evaluated.

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mentioning

confidence: 89%

Frequency and Temporal Profile of Poststroke Hyperglycemia Using Continuous Glucose Monitoring

Allport

Baird²,

Butcher³

et al. 2006

Diabetes Care

112

117

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“…Equation (8) gives a mean absolute error of 14%, which is typical for CGMS sensors [7]. Blood glucose is still assumed to be measured hourly with a glucocard and 7% uniformly distributed noise in addition to the CGMS for comparison.…”

Section: Combining Cgms With Glucocard Measurementsmentioning

confidence: 99%

“…Finally, a new model-based control method for metabolic control is presented, that combines a non-invasive continuous glucose sensor (CGMS) [7] with current standard glucometer sensors [8]. This method is shown to provide a potentially significant improvement in glucose control in simulation that warrants further clinical investigation in the future.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Model-based Therapeutics on Glucose Control in an Intensive Care Unit

et al. 2009

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-This paper investigates the impact of fast parameter identification methods, which do not require any forward simulations, on model-based glucose control, using retrospective data in the Christchurch Hospital Intensive Care Unit. The integral-based identification method has been previously clinically validated and extensively applied in a number of biomedical applications; and is a crucial element in the presented model-based therapeutics approach. Common non-linear regression and gradient descent approaches are too computationally intense and not suitable for the glucose control applications presented. The main focus in this paper is on better characterizing and understanding the importance of the integral in the formulation and the effect it has on modelbased drug therapy control. As a comparison, a potentially more natural derivative formulation which has the same computation speed advantages is investigated, and is shown to go unstable with respect to modelling error which is always present clinically. The integral method remains robust.

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“…The Continuous Glucose Monitoring System (CGMS, Minimed, Northridge, CA) currently offers the greatest data density with little difference in error from other reported emerging sensor technologies, and has been approved for clinical use by the FDA in the US [11,12]. While this technology is new, it has been evaluated in critical care [11], and been used for automated glucose regulation in critical care [13].…”

Section: [Insert Figure 1 Here]mentioning

confidence: 99%

“…Due to the discomfort and labour intensity of such frequent measurement, emerging semiinvasive continuous glucose sensors offer several advantages. In particular, they offer much higher data density, ranging from every 5 minutes to every 20 minutes [10][11][12], without any significant clinical effort. This higher data density could significantly improve the ability of model-based control methods to better fit patient specific parameters and more quickly react to sudden changes in patient condition.…”

Section: Introductionmentioning

confidence: 99%

Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care

Chase

Hann

Jackson

et al. 2006

Computer Methods and Programs in Biomedicine

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Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%.Adaptive control methods are capable of highly accurate, targeted blood glucose regulation using limited numbers of manual measurements due to patient discomfort and labour intensity. Therefore, the option to obtain greater data density using emerging continuous glucose sensing devices is attractive. However, the few such

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Experience with the Continuous Glucose Monitoring System® in a Medical Intensive Care Unit

Cited by 151 publications

References 19 publications

Frequency and Temporal Profile of Poststroke Hyperglycemia Using Continuous Glucose Monitoring

Frequency and Temporal Profile of Poststroke Hyperglycemia Using Continuous Glucose Monitoring

The Impact of Model-based Therapeutics on Glucose Control in an Intensive Care Unit

Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care

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