Article

Preventing hypoglycemia using predictive alarm algorithms and insulin pump suspension.

Department of Pediatric Endocrinology, Stanford University, Stanford, California 94305-5208, USA.
Diabetes Technology &amp Therapeutics (Impact Factor: 2.29). 02/2009; 11(2):93-7. DOI: 10.1089/dia.2008.0032
Source: PubMed

ABSTRACT Nocturnal hypoglycemia is a significant problem. From 50% to 75% of hypoglycemia seizures occur at night. Despite the development of real-time glucose sensors (real-time continuous glucose monitor [CGM]) with hypoglycemic alarms, many patients sleep through these alarms. The goal of this pilot study was to assess the feasibility using a real-time CGM to discontinue insulin pump therapy when hypoglycemia was predicted.
Twenty-two subjects with type 1 diabetes had two daytime admissions to a clinical research center. On the first admission their basal insulin was increased until their blood glucose level was <60 mg/dL. On the second admission hypoglycemic prediction algorithms were tested to determine if hypoglycemia was prevented by a 90-min pump shutoff and to determine if the pump shutoff resulted in rebound hyperglycemia.
Using a statistical prediction algorithm with an 80 mg/dL threshold and a 30-min projection horizon, hypoglycemia was prevented 60% of the time. Using a linear prediction algorithm with an 80 mg/dL threshold and a 45-min prediction horizon, hypoglycemia was prevented 80% of the time. There was no rebound hyperglycemia following pump suspension.
Further development of algorithms is needed to prevent all episodes of hypoglycemia from occurring.

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Available from: Bruce Buckingham, Jul 08, 2015
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    • "As introduced in the previous paragraph , adding prediction/suspending term can enhance the performance of the uni-hormonal AP system. There have been a number of reported studies for hypoglycemia prediction [7] [8]. To achieve the performance limitation of the unihormonal AP system with prediction/suspending term, the perfect prediction/suspending solution is included in the unihormonal benchmark AP system; hence, this system can be considered as an optimal uni-hormonal AP system. "
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    • "The inclusion of IOB effect in predicting future hypoglycemic episodes could be another technique to improve the feasibility of these algorithms (Buckingham et al. (2009)). Finally, improving the accuracy and reliability of CGM systems is an essential task, since both control algorithms and hypoglycemia alarms depend widely on CGM measurements. "
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