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

Computer Methods and Programs in Biomedicine
J Geoffrey ChaseGeoffrey M Shaw

Abstract

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 systems currently available can have errors in excess of 20-30%. In contrast, typical bedside testing kits have errors of approximately 7-10%. Despite greater measurement frequency larger errors significantly impact the resulting glucose and patient specific parameter estimates, and thus the control actions determined creating an important safety and performance issue. This paper models the impact of the continuous glucose monitoring system (CGMS, Medtronic, Northridge, CA) on model-based parameter identification and glucose prediction. An integral-based fitting and filtering method is developed to reduce the effect of these errors. A noise model is developed based on CGMS data reported in the literature, and is slightly conservative with a mean Clar...Continue Reading

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Citations

Dec 30, 2011·Medical & Biological Engineering & Computing·Paul D DochertyTimothy David
Oct 13, 2007·Diabetes Technology & Therapeutics·Daniel A FinanDale E Seborg
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