Literature DB >> 20920434

Hypoglycemia prevention via pump attenuation and red-yellow-green "traffic" lights using continuous glucose monitoring and insulin pump data.

Colleen S Hughes1, Stephen D Patek, Marc D Breton, Boris P Kovatchev.   

Abstract

BACKGROUND: Hypoglycemia has been identified as a primary barrier to optimal management of diabetes. This observation, in conjunction with the introduction of continuous glucose monitoring (CGM) devices, has set the stage for achieving tight glycemic control with systems that adjust the insulin pump settings based on measured glucose concentrations. Because system safety and system reliability are key considerations, there is a need for algorithms that reduce the risk of hypoglycemia in closed-loop, open-loop, and advisory-mode systems. More specifically, the algorithm presented here is formulated as a component of the independent safety system module proposed in the modular control-to-range architecture.
METHODS: We developed two algorithms for attenuating insulin pump injections, which we refer to as Brakes and Power Brakes: Brakes is a pump attenuation function computed using CGM information only, while Power Brakes is an attenuation function in which a metabolic state observer with insulin input is used in addition to CGM information to inform the level of pump attenuation. These algorithms modulate the insulin pump delivery so that the insulin injection rate is dramatically reduced when the risk of hypoglycemia is high. Additionally, we combined these algorithms with an alert system that indicates a level of hypoglycemic risk to the user.
RESULTS: We demonstrated the effectiveness of Brakes and Power Brakes in reducing the incidence of hypoglycemia in two simulated scenarios: an elevated basal rate scenario and a scenario in which a bolus is delivered for a meal that is skipped. For these scenarios, the incidence of hypoglycemia using Power Brakes was reduced by 88 and 94%, respectively, where we defined hypoglycemia based on the American Diabetes Association guidelines for defining and reporting as 70 mg/dl. In the elevated basal rate scenario, no rebounds above 180 mg/dl (the desired upper limit of the control-to-range protocol) following hypoglycemia were shown to occur. We demonstrated the way the hypoglycemia alert system can trigger the intake of carbohydrates to reduce the incidence of hypoglycemia by 98%.
CONCLUSIONS: This article offers, for the first time, a method for smoothly reducing insulin delivery rate to prevent hypoglycemia in individuals with type 1 diabetes mellitus based on a mathematically formal assessment of hypoglycemic risk. In silico, we demonstrate the way this method can prevent hypoglycemia while avoiding hyperglycemia rebounds that exceed 180 mg/dl. In conjunction with the pump attenuation algorithms, this article also proposes a system for alerting an individual of their hypoglycemic risk that contains three "levels" of alerts in the form of a traffic light. This alert system can be used in an advisory mode setting to alert the user to take action when hypoglycemia is imminent ("red" light) or in a closed-loop setting where initiation of rescue action begins when the red light alert is triggered.
© 2010 Diabetes Technology Society.

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Year:  2010        PMID: 20920434      PMCID: PMC2956822          DOI: 10.1177/193229681000400513

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  26 in total

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2.  Alarm characterization for continuous glucose monitors used as adjuncts to self-monitoring of blood glucose.

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3.  A randomized multicenter trial comparing the GlucoWatch Biographer with standard glucose monitoring in children with type 1 diabetes.

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4.  Accuracy of the 5-day FreeStyle Navigator Continuous Glucose Monitoring System: comparison with frequent laboratory reference measurements.

Authors:  Richard L Weinstein; Sherwyn L Schwartz; Ronald L Brazg; Jolyon R Bugler; Thomas A Peyser; Geoffrey V McGarraugh
Journal:  Diabetes Care       Date:  2007-03-02       Impact factor: 19.112

Review 5.  Real-time continuous glucose monitoring.

Authors:  Bruce Buckingham; Kimberly Caswell; Darrell M Wilson
Journal:  Curr Opin Endocrinol Diabetes Obes       Date:  2007-08       Impact factor: 3.243

6.  Control to range for diabetes: functionality and modular architecture.

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7.  Hypoglycemia prediction and detection using optimal estimation.

Authors:  Cesar C Palerm; John P Willis; James Desemone; B Wayne Bequette
Journal:  Diabetes Technol Ther       Date:  2005-02       Impact factor: 6.118

Review 8.  Defining and reporting hypoglycemia in diabetes: a report from the American Diabetes Association Workgroup on Hypoglycemia.

Authors: 
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9.  Analysis, modeling, and simulation of the accuracy of continuous glucose sensors.

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10.  Influence of short-term submaximal exercise on parameters of glucose assimilation analyzed with the minimal model.

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Journal:  Metabolism       Date:  1995-07       Impact factor: 8.694

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  38 in total

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Journal:  J Diabetes Sci Technol       Date:  2013-09-01

4.  Acute hypoglycemia causes depressive-like behaviors in mice.

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Review 5.  Hypo- and Hyperglycemic Alarms: Devices and Algorithms.

Authors:  Daniel Howsmon; B Wayne Bequette
Journal:  J Diabetes Sci Technol       Date:  2015-04-30

6.  Designing an artificial pancreas architecture: the AP@home experience.

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7.  Successful At-Home Use of the Tandem Control-IQ Artificial Pancreas System in Young Children During a Randomized Controlled Trial.

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Journal:  Diabetes Technol Ther       Date:  2019-03-19       Impact factor: 6.118

8.  Identification of Main Factors Explaining Glucose Dynamics During and Immediately After Moderate Exercise in Patients With Type 1 Diabetes.

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9.  Multicenter closed-loop insulin delivery study points to challenges for keeping blood glucose in a safe range by a control algorithm in adults and adolescents with type 1 diabetes from various sites.

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Journal:  Diabetes Technol Ther       Date:  2014-07-08       Impact factor: 6.118

10.  Multicenter closed-loop/hybrid meal bolus insulin delivery with type 1 diabetes.

Authors:  H Peter Chase; Francis J Doyle; Howard Zisser; Eric Renard; Revital Nimri; Claudio Cobelli; Bruce A Buckingham; David M Maahs; Stacey Anderson; Lalo Magni; John Lum; Peter Calhoun; Craig Kollman; Roy W Beck
Journal:  Diabetes Technol Ther       Date:  2014-09-04       Impact factor: 6.118

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