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.

Download full-text


Available from: Bruce Buckingham, Jul 08, 2015
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Automated closed-loop control of blood glucose concentration is a daily challenge for type 1 diabetes mellitus, where insulin and glucagon are two critical hormones for glucose regulation. According to whether glucagon is included, all artificial pancreas (AP) systems can be divided into two types: unihormonal AP (infuse only insulin) and bihormonal AP (infuse both insulin and glucagon). Even though the bihormonal AP is widely considered a promising direction, related studies are very scarce due to this system's short research history. More importantly, there are few studies to compare these two kinds of AP systems fairly and systematically. In this paper, two switching rules, P-type and PD-type, were proposed to design the logic of orchestrates switching between insulin and glucagon subsystems, where the delivery rates of both insulin and glucagon were designed by using IMC-PID method. These proposed algorithms have been compared with an optimal unihormonal system on virtual type 1 diabetic subjects. The in silico results demonstrate that the proposed bihormonal AP systems have outstanding superiorities in reducing the risk of hypoglycemia, smoothing the glucose level, and robustness with respect to insulin/glucagon sensitivity variations, compared with the optimal unihormonal AP system.
    Computational and Mathematical Methods in Medicine 10/2013; 2013:712496. DOI:10.1155/2013/712496 · 1.02 Impact Factor
  • Source
    • "Therefore, methods to minimize or avoid hypoglycaemia are required. One solution to handle hypoglycaemia is a combination of glucose prediction and insulin infusion suspending [2] [3], for short, named prediction/suspending solution. However, the capability of the prediction/suspending solution is limited. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Glucose management is an important clinical task for diabetic patients, and intensive insulin therapy is widely considered a promising way for the glucose management. However, the intensive insulin therapy has one potential risk: hypoglycemia, but there is no antagonist to compensate hypoglycaemia in the intensive insulin therapy. Dual infusion of insulin and glucagon can overcome this shortcoming. In this paper, a switching control algorithm was proposed to design and optimize the insulin and glucagon infusion rates simultaneously, and this algorithm has been implemented in a virtual type 1 diabetic subject. The in silico results demonstrate that the proposed algorithm can reduce hypoglycaemia significantly.
    Intelligent Control and Automation (WCICA), 2012 10th World Congress on; 01/2012
  • Source
    • "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. "
    Diabetes - Damages and Treatments, 11/2011; , ISBN: 978-953-307-652-2